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Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions | We present quantum algorithms for sampling from non-logconcave probability
distributions in the form of $\pi(x) \propto \exp(-\beta f(x))$. Here, $f$ can
be written as a finite sum $f(x):= \frac{1}{N}\sum_{k=1}^N f_k(x)$. Our
approach is based on quantum simulated annealing on slowly varying Markov
chains derived from unadjusted Langevin algorithms, removing the necessity for
function evaluations which can be computationally expensive for large data sets
in mixture modeling and multi-stable systems. We also incorporate a stochastic
gradient oracle that implements the quantum walk operators inexactly by only
using mini-batch gradients. As a result, our stochastic gradient based
algorithm only accesses small subsets of data points in implementing the
quantum walk. One challenge of quantizing the resulting Markov chains is that
they do not satisfy the detailed balance condition in general. Consequently,
the mixing time of the algorithm cannot be expressed in terms of the spectral
gap of the transition density, making the quantum algorithms nontrivial to
analyze. To overcome these challenges, we first build a hypothetical Markov
chain that is reversible, and also converges to the target distribution. Then,
we quantified the distance between our algorithm's output and the target
distribution by using this hypothetical chain as a bridge to establish the
total complexity. Our quantum algorithms exhibit polynomial speedups in terms
of both dimension and precision dependencies when compared to the best-known
classical algorithms. | [
"Guneykan Ozgul",
"Xiantao Li",
"Mehrdad Mahdavi",
"Chunhao Wang"
] | 2023-10-17 17:55:32 | http://arxiv.org/abs/2310.11445v1 | http://arxiv.org/pdf/2310.11445v1 | 2310.11445v1 |
Understanding deep neural networks through the lens of their non-linearity | The remarkable success of deep neural networks (DNN) is often attributed to
their high expressive power and their ability to approximate functions of
arbitrary complexity. Indeed, DNNs are highly non-linear models, and activation
functions introduced into them are largely responsible for this. While many
works studied the expressive power of DNNs through the lens of their
approximation capabilities, quantifying the non-linearity of DNNs or of
individual activation functions remains an open problem. In this paper, we
propose the first theoretically sound solution to track non-linearity
propagation in deep neural networks with a specific focus on computer vision
applications. Our proposed affinity score allows us to gain insights into the
inner workings of a wide range of different architectures and learning
paradigms. We provide extensive experimental results that highlight the
practical utility of the proposed affinity score and its potential for
long-reaching applications. | [
"Quentin Bouniot",
"Ievgen Redko",
"Anton Mallasto",
"Charlotte Laclau",
"Karol Arndt",
"Oliver Struckmeier",
"Markus Heinonen",
"Ville Kyrki",
"Samuel Kaski"
] | 2023-10-17 17:50:22 | http://arxiv.org/abs/2310.11439v1 | http://arxiv.org/pdf/2310.11439v1 | 2310.11439v1 |
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems | Single neurons in neural networks are often interpretable in that they
represent individual, intuitively meaningful features. However, many neurons
exhibit $\textit{mixed selectivity}$, i.e., they represent multiple unrelated
features. A recent hypothesis proposes that features in deep networks may be
represented in $\textit{superposition}$, i.e., on non-orthogonal axes by
multiple neurons, since the number of possible interpretable features in
natural data is generally larger than the number of neurons in a given network.
Accordingly, we should be able to find meaningful directions in activation
space that are not aligned with individual neurons. Here, we propose (1) an
automated method for quantifying visual interpretability that is validated
against a large database of human psychophysics judgments of neuron
interpretability, and (2) an approach for finding meaningful directions in
network activation space. We leverage these methods to discover directions in
convolutional neural networks that are more intuitively meaningful than
individual neurons, as we confirm and investigate in a series of analyses.
Moreover, we apply the same method to three recent datasets of visual neural
responses in the brain and find that our conclusions largely transfer to real
neural data, suggesting that superposition might be deployed by the brain. This
also provides a link with disentanglement and raises fundamental questions
about robust, efficient and factorized representations in both artificial and
biological neural systems. | [
"David Klindt",
"Sophia Sanborn",
"Francisco Acosta",
"Frédéric Poitevin",
"Nina Miolane"
] | 2023-10-17 17:41:28 | http://arxiv.org/abs/2310.11431v2 | http://arxiv.org/pdf/2310.11431v2 | 2310.11431v2 |
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression | This work studies training instabilities of behavior cloning with deep neural
networks. We observe that minibatch SGD updates to the policy network during
training result in sharp oscillations in long-horizon rewards, despite
negligibly affecting the behavior cloning loss. We empirically disentangle the
statistical and computational causes of these oscillations, and find them to
stem from the chaotic propagation of minibatch SGD noise through unstable
closed-loop dynamics. While SGD noise is benign in the single-step action
prediction objective, it results in catastrophic error accumulation over long
horizons, an effect we term gradient variance amplification (GVA). We show that
many standard mitigation techniques do not alleviate GVA, but find an
exponential moving average (EMA) of iterates to be surprisingly effective at
doing so. We illustrate the generality of this phenomenon by showing the
existence of GVA and its amelioration by EMA in both continuous control and
autoregressive language generation. Finally, we provide theoretical vignettes
that highlight the benefits of EMA in alleviating GVA and shed light on the
extent to which classical convex models can help in understanding the benefits
of iterate averaging in deep learning. | [
"Adam Block",
"Dylan J. Foster",
"Akshay Krishnamurthy",
"Max Simchowitz",
"Cyril Zhang"
] | 2023-10-17 17:39:40 | http://arxiv.org/abs/2310.11428v1 | http://arxiv.org/pdf/2310.11428v1 | 2310.11428v1 |
Group-blind optimal transport to group parity and its constrained variants | Fairness holds a pivotal role in the realm of machine learning, particularly
when it comes to addressing groups categorised by sensitive attributes, e.g.,
gender, race. Prevailing algorithms in fair learning predominantly hinge on
accessibility or estimations of these sensitive attributes, at least in the
training process. We design a single group-blind projection map that aligns the
feature distributions of both groups in the source data, achieving
(demographic) group parity, without requiring values of the protected attribute
for individual samples in the computation of the map, as well as its use.
Instead, our approach utilises the feature distributions of the privileged and
unprivileged groups in a boarder population and the essential assumption that
the source data are unbiased representation of the population. We present
numerical results on synthetic data and real data. | [
"Quan Zhou",
"Jakub Marecek"
] | 2023-10-17 17:14:07 | http://arxiv.org/abs/2310.11407v1 | http://arxiv.org/pdf/2310.11407v1 | 2310.11407v1 |
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests | Fairness, especially group fairness, is an important consideration in the
context of machine learning systems. The most commonly adopted group
fairness-enhancing techniques are in-processing methods that rely on a mixture
of a fairness objective (e.g., demographic parity) and a task-specific
objective (e.g., cross-entropy) during the training process. However, when data
arrives in an online fashion -- one instance at a time -- optimizing such
fairness objectives poses several challenges. In particular, group fairness
objectives are defined using expectations of predictions across different
demographic groups. In the online setting, where the algorithm has access to a
single instance at a time, estimating the group fairness objective requires
additional storage and significantly more computation (e.g., forward/backward
passes) than the task-specific objective at every time step. In this paper, we
propose Aranyani, an ensemble of oblique decision trees, to make fair decisions
in online settings. The hierarchical tree structure of Aranyani enables
parameter isolation and allows us to efficiently compute the fairness gradients
using aggregate statistics of previous decisions, eliminating the need for
additional storage and forward/backward passes. We also present an efficient
framework to train Aranyani and theoretically analyze several of its
properties. We conduct empirical evaluations on 5 publicly available benchmarks
(including vision and language datasets) to show that Aranyani achieves a
better accuracy-fairness trade-off compared to baseline approaches. | [
"Somnath Basu Roy Chowdhury",
"Nicholas Monath",
"Ahmad Beirami",
"Rahul Kidambi",
"Avinava Dubey",
"Amr Ahmed",
"Snigdha Chaturvedi"
] | 2023-10-17 17:10:56 | http://arxiv.org/abs/2310.11401v1 | http://arxiv.org/pdf/2310.11401v1 | 2310.11401v1 |
Last One Standing: A Comparative Analysis of Security and Privacy of Soft Prompt Tuning, LoRA, and In-Context Learning | Large Language Models (LLMs) are powerful tools for natural language
processing, enabling novel applications and user experiences. However, to
achieve optimal performance, LLMs often require adaptation with private data,
which poses privacy and security challenges. Several techniques have been
proposed to adapt LLMs with private data, such as Low-Rank Adaptation (LoRA),
Soft Prompt Tuning (SPT), and In-Context Learning (ICL), but their comparative
privacy and security properties have not been systematically investigated. In
this work, we fill this gap by evaluating the robustness of LoRA, SPT, and ICL
against three types of well-established attacks: membership inference, which
exposes data leakage (privacy); backdoor, which injects malicious behavior
(security); and model stealing, which can violate intellectual property
(privacy and security). Our results show that there is no silver bullet for
privacy and security in LLM adaptation and each technique has different
strengths and weaknesses. | [
"Rui Wen",
"Tianhao Wang",
"Michael Backes",
"Yang Zhang",
"Ahmed Salem"
] | 2023-10-17 17:03:00 | http://arxiv.org/abs/2310.11397v1 | http://arxiv.org/pdf/2310.11397v1 | 2310.11397v1 |
VaR\ and CVaR Estimation in a Markov Cost Process: Lower and Upper Bounds | We tackle the problem of estimating the Value-at-Risk (VaR) and the
Conditional Value-at-Risk (CVaR) of the infinite-horizon discounted cost within
a Markov cost process. First, we derive a minimax lower bound of
$\Omega(1/\sqrt{n})$ that holds both in an expected and in a probabilistic
sense. Then, using a finite-horizon truncation scheme, we derive an upper bound
for the error in CVaR estimation, which matches our lower bound up to constant
factors. Finally, we discuss an extension of our estimation scheme that covers
more general risk measures satisfying a certain continuity criterion, e.g.,
spectral risk measures, utility-based shortfall risk. To the best of our
knowledge, our work is the first to provide lower and upper bounds on the
estimation error for any risk measure within Markovian settings. We remark that
our lower bounds also extend to the infinite-horizon discounted costs' mean.
Even in that case, our result $\Omega(1/\sqrt{n}) $ improves upon the existing
result $\Omega(1/n)$[13]. | [
"Sanjay Bhat",
"Prashanth L. A.",
"Gugan Thoppe"
] | 2023-10-17 16:35:39 | http://arxiv.org/abs/2310.11389v1 | http://arxiv.org/pdf/2310.11389v1 | 2310.11389v1 |
Faster Algorithms for Generalized Mean Densest Subgraph Problem | The densest subgraph of a large graph usually refers to some subgraph with
the highest average degree, which has been extended to the family of $p$-means
dense subgraph objectives by~\citet{veldt2021generalized}. The $p$-mean densest
subgraph problem seeks a subgraph with the highest average $p$-th-power degree,
whereas the standard densest subgraph problem seeks a subgraph with a simple
highest average degree. It was shown that the standard peeling algorithm can
perform arbitrarily poorly on generalized objective when $p>1$ but uncertain
when $0<p<1$. In this paper, we are the first to show that a standard peeling
algorithm can still yield $2^{1/p}$-approximation for the case $0<p < 1$.
(Veldt 2021) proposed a new generalized peeling algorithm (GENPEEL), which for
$p \geq 1$ has an approximation guarantee ratio $(p+1)^{1/p}$, and time
complexity $O(mn)$, where $m$ and $n$ denote the number of edges and nodes in
graph respectively. In terms of algorithmic contributions, we propose a new and
faster generalized peeling algorithm (called GENPEEL++ in this paper), which
for $p \in [1, +\infty)$ has an approximation guarantee ratio $(2(p+1))^{1/p}$,
and time complexity $O(m(\log n))$, where $m$ and $n$ denote the number of
edges and nodes in graph, respectively. This approximation ratio converges to 1
as $p \rightarrow \infty$. | [
"Chenglin Fan",
"Ping Li",
"Hanyu Peng"
] | 2023-10-17 16:21:28 | http://arxiv.org/abs/2310.11377v1 | http://arxiv.org/pdf/2310.11377v1 | 2310.11377v1 |
End-to-End real time tracking of children's reading with pointer network | In this work, we explore how a real time reading tracker can be built
efficiently for children's voices. While previously proposed reading trackers
focused on ASR-based cascaded approaches, we propose a fully end-to-end model
making it less prone to lags in voice tracking. We employ a pointer network
that directly learns to predict positions in the ground truth text conditioned
on the streaming speech. To train this pointer network, we generate ground
truth training signals by using forced alignment between the read speech and
the text being read on the training set. Exploring different forced alignment
models, we find a neural attention based model is at least as close in
alignment accuracy to the Montreal Forced Aligner, but surprisingly is a better
training signal for the pointer network. Our results are reported on one adult
speech data (TIMIT) and two children's speech datasets (CMU Kids and Reading
Races). Our best model can accurately track adult speech with 87.8% accuracy
and the much harder and disfluent children's speech with 77.1% accuracy on CMU
Kids data and a 65.3% accuracy on the Reading Races dataset. | [
"Vishal Sunder",
"Beulah Karrolla",
"Eric Fosler-Lussier"
] | 2023-10-17 16:12:18 | http://arxiv.org/abs/2310.11486v1 | http://arxiv.org/pdf/2310.11486v1 | 2310.11486v1 |
Lie Group Decompositions for Equivariant Neural Networks | Invariance and equivariance to geometrical transformations have proven to be
very useful inductive biases when training (convolutional) neural network
models, especially in the low-data regime. Much work has focused on the case
where the symmetry group employed is compact or abelian, or both. Recent work
has explored enlarging the class of transformations used to the case of Lie
groups, principally through the use of their Lie algebra, as well as the group
exponential and logarithm maps. The applicability of such methods to larger
transformation groups is limited by the fact that depending on the group of
interest $G$, the exponential map may not be surjective. Further limitations
are encountered when $G$ is neither compact nor abelian. Using the structure
and geometry of Lie groups and their homogeneous spaces, we present a framework
by which it is possible to work with such groups primarily focusing on the Lie
groups $G = \text{GL}^{+}(n, \mathbb{R})$ and $G = \text{SL}(n, \mathbb{R})$,
as well as their representation as affine transformations $\mathbb{R}^{n}
\rtimes G$. Invariant integration as well as a global parametrization is
realized by decomposing the `larger` groups into subgroups and submanifolds
which can be handled individually. Under this framework, we show how
convolution kernels can be parametrized to build models equivariant with
respect to affine transformations. We evaluate the robustness and
out-of-distribution generalisation capability of our model on the standard
affine-invariant benchmark classification task, where we outperform all
previous equivariant models as well as all Capsule Network proposals. | [
"Mircea Mironenco",
"Patrick Forré"
] | 2023-10-17 16:04:33 | http://arxiv.org/abs/2310.11366v1 | http://arxiv.org/pdf/2310.11366v1 | 2310.11366v1 |
Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning | Artificial neural networks (ANNs) exhibit a narrow scope of expertise on
stationary independent data. However, the data in the real world is continuous
and dynamic, and ANNs must adapt to novel scenarios while also retaining the
learned knowledge to become lifelong learners. The ability of humans to excel
at these tasks can be attributed to multiple factors ranging from cognitive
computational structures, cognitive biases, and the multi-memory systems in the
brain. We incorporate key concepts from each of these to design a novel
framework, Dual Cognitive Architecture (DUCA), which includes multiple
sub-systems, implicit and explicit knowledge representation dichotomy,
inductive bias, and a multi-memory system. The inductive bias learner within
DUCA is instrumental in encoding shape information, effectively countering the
tendency of ANNs to learn local textures. Simultaneously, the inclusion of a
semantic memory submodule facilitates the gradual consolidation of knowledge,
replicating the dynamics observed in fast and slow learning systems,
reminiscent of the principles underpinning the complementary learning system in
human cognition. DUCA shows improvement across different settings and datasets,
and it also exhibits reduced task recency bias, without the need for extra
information. To further test the versatility of lifelong learning methods on a
challenging distribution shift, we introduce a novel domain-incremental dataset
DN4IL. In addition to improving performance on existing benchmarks, DUCA also
demonstrates superior performance on this complex dataset. | [
"Shruthi Gowda",
"Bahram Zonooz",
"Elahe Arani"
] | 2023-10-17 15:24:02 | http://arxiv.org/abs/2310.11341v1 | http://arxiv.org/pdf/2310.11341v1 | 2310.11341v1 |
Contextualized Machine Learning | We examine Contextualized Machine Learning (ML), a paradigm for learning
heterogeneous and context-dependent effects. Contextualized ML estimates
heterogeneous functions by applying deep learning to the meta-relationship
between contextual information and context-specific parametric models. This is
a form of varying-coefficient modeling that unifies existing frameworks
including cluster analysis and cohort modeling by introducing two reusable
concepts: a context encoder which translates sample context into model
parameters, and sample-specific model which operates on sample predictors. We
review the process of developing contextualized models, nonparametric inference
from contextualized models, and identifiability conditions of contextualized
models. Finally, we present the open-source PyTorch package ContextualizedML. | [
"Benjamin Lengerich",
"Caleb N. Ellington",
"Andrea Rubbi",
"Manolis Kellis",
"Eric P. Xing"
] | 2023-10-17 15:23:00 | http://arxiv.org/abs/2310.11340v1 | http://arxiv.org/pdf/2310.11340v1 | 2310.11340v1 |
Non-ergodicity in reinforcement learning: robustness via ergodicity transformations | Envisioned application areas for reinforcement learning (RL) include
autonomous driving, precision agriculture, and finance, which all require RL
agents to make decisions in the real world. A significant challenge hindering
the adoption of RL methods in these domains is the non-robustness of
conventional algorithms. In this paper, we argue that a fundamental issue
contributing to this lack of robustness lies in the focus on the expected value
of the return as the sole "correct" optimization objective. The expected value
is the average over the statistical ensemble of infinitely many trajectories.
For non-ergodic returns, this average differs from the average over a single
but infinitely long trajectory. Consequently, optimizing the expected value can
lead to policies that yield exceptionally high returns with probability zero
but almost surely result in catastrophic outcomes. This problem can be
circumvented by transforming the time series of collected returns into one with
ergodic increments. This transformation enables learning robust policies by
optimizing the long-term return for individual agents rather than the average
across infinitely many trajectories. We propose an algorithm for learning
ergodicity transformations from data and demonstrate its effectiveness in an
instructive, non-ergodic environment and on standard RL benchmarks. | [
"Dominik Baumann",
"Erfaun Noorani",
"James Price",
"Ole Peters",
"Colm Connaughton",
"Thomas B. Schön"
] | 2023-10-17 15:13:33 | http://arxiv.org/abs/2310.11335v1 | http://arxiv.org/pdf/2310.11335v1 | 2310.11335v1 |
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting | As large language models (LLMs) are adopted as a fundamental component of
language technologies, it is crucial to accurately characterize their
performance. Because choices in prompt design can strongly influence model
behavior, this design process is critical in effectively using any modern
pre-trained generative language model. In this work, we focus on LLM
sensitivity to a quintessential class of meaning-preserving design choices:
prompt formatting. We find that several widely used open-source LLMs are
extremely sensitive to subtle changes in prompt formatting in few-shot
settings, with performance differences of up to 76 accuracy points when
evaluated using LLaMA-2-13B. Sensitivity remains even when increasing model
size, the number of few-shot examples, or performing instruction tuning. Our
analysis suggests that work evaluating LLMs with prompting-based methods would
benefit from reporting a range of performance across plausible prompt formats,
instead of the currently-standard practice of reporting performance on a single
format. We also show that format performance only weakly correlates between
models, which puts into question the methodological validity of comparing
models with an arbitrarily chosen, fixed prompt format. To facilitate
systematic analysis we propose FormatSpread, an algorithm that rapidly
evaluates a sampled set of plausible prompt formats for a given task, and
reports the interval of expected performance without accessing model weights.
Furthermore, we present a suite of analyses that characterize the nature of
this sensitivity, including exploring the influence of particular atomic
perturbations and the internal representation of particular formats. | [
"Melanie Sclar",
"Yejin Choi",
"Yulia Tsvetkov",
"Alane Suhr"
] | 2023-10-17 15:03:30 | http://arxiv.org/abs/2310.11324v1 | http://arxiv.org/pdf/2310.11324v1 | 2310.11324v1 |
Elucidating The Design Space of Classifier-Guided Diffusion Generation | Guidance in conditional diffusion generation is of great importance for
sample quality and controllability. However, existing guidance schemes are to
be desired. On one hand, mainstream methods such as classifier guidance and
classifier-free guidance both require extra training with labeled data, which
is time-consuming and unable to adapt to new conditions. On the other hand,
training-free methods such as universal guidance, though more flexible, have
yet to demonstrate comparable performance. In this work, through a
comprehensive investigation into the design space, we show that it is possible
to achieve significant performance improvements over existing guidance schemes
by leveraging off-the-shelf classifiers in a training-free fashion, enjoying
the best of both worlds. Employing calibration as a general guideline, we
propose several pre-conditioning techniques to better exploit pretrained
off-the-shelf classifiers for guiding diffusion generation. Extensive
experiments on ImageNet validate our proposed method, showing that
state-of-the-art diffusion models (DDPM, EDM, DiT) can be further improved (up
to 20%) using off-the-shelf classifiers with barely any extra computational
cost. With the proliferation of publicly available pretrained classifiers, our
proposed approach has great potential and can be readily scaled up to
text-to-image generation tasks. The code is available at
https://github.com/AlexMaOLS/EluCD/tree/main. | [
"Jiajun Ma",
"Tianyang Hu",
"Wenjia Wang",
"Jiacheng Sun"
] | 2023-10-17 14:34:58 | http://arxiv.org/abs/2310.11311v1 | http://arxiv.org/pdf/2310.11311v1 | 2310.11311v1 |
MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games | This paper presents MiniZero, a zero-knowledge learning framework that
supports four state-of-the-art algorithms, including AlphaZero, MuZero, Gumbel
AlphaZero, and Gumbel MuZero. While these algorithms have demonstrated
super-human performance in many games, it remains unclear which among them is
most suitable or efficient for specific tasks. Through MiniZero, we
systematically evaluate the performance of each algorithm in two board games,
9x9 Go and 8x8 Othello, as well as 57 Atari games. Our empirical findings are
summarized as follows. For two board games, using more simulations generally
results in higher performance. However, the choice of AlphaZero and MuZero may
differ based on game properties. For Atari games, both MuZero and Gumbel MuZero
are worth considering. Since each game has unique characteristics, different
algorithms and simulations yield varying results. In addition, we introduce an
approach, called progressive simulation, which progressively increases the
simulation budget during training to allocate computation more efficiently. Our
empirical results demonstrate that progressive simulation achieves
significantly superior performance in two board games. By making our framework
and trained models publicly available, this paper contributes a benchmark for
future research on zero-knowledge learning algorithms, assisting researchers in
algorithm selection and comparison against these zero-knowledge learning
baselines. | [
"Ti-Rong Wu",
"Hung Guei",
"Po-Wei Huang",
"Pei-Chiun Peng",
"Ting Han Wei",
"Chung-Chin Shih",
"Yun-Jui Tsai"
] | 2023-10-17 14:29:25 | http://arxiv.org/abs/2310.11305v1 | http://arxiv.org/pdf/2310.11305v1 | 2310.11305v1 |
An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent | The delta-bar-delta algorithm is recognized as a learning rate adaptation
technique that enhances the convergence speed of the training process in
optimization by dynamically scheduling the learning rate based on the
difference between the current and previous weight updates. While this
algorithm has demonstrated strong competitiveness in full data optimization
when compared to other state-of-the-art algorithms like Adam and SGD, it may
encounter convergence issues in mini-batch optimization scenarios due to the
presence of noisy gradients.
In this study, we thoroughly investigate the convergence behavior of the
delta-bar-delta algorithm in real-world neural network optimization. To address
any potential convergence challenges, we propose a novel approach called RDBD
(Regrettable Delta-Bar-Delta). Our approach allows for prompt correction of
biased learning rate adjustments and ensures the convergence of the
optimization process. Furthermore, we demonstrate that RDBD can be seamlessly
integrated with any optimization algorithm and significantly improve the
convergence speed.
By conducting extensive experiments and evaluations, we validate the
effectiveness and efficiency of our proposed RDBD approach. The results
showcase its capability to overcome convergence issues in mini-batch
optimization and its potential to enhance the convergence speed of various
optimization algorithms. This research contributes to the advancement of
optimization techniques in neural network training, providing practitioners
with a reliable automatic learning rate scheduler for achieving faster
convergence and improved optimization outcomes. | [
"Zhao Song",
"Chiwun Yang"
] | 2023-10-17 14:15:57 | http://arxiv.org/abs/2310.11291v1 | http://arxiv.org/pdf/2310.11291v1 | 2310.11291v1 |
Evaluating the Impact of Humanitarian Aid on Food Security | In the face of climate change-induced droughts, vulnerable regions encounter
severe threats to food security, demanding urgent humanitarian assistance. This
paper introduces a causal inference framework for the Horn of Africa, aiming to
assess the impact of cash-based interventions on food crises. Our contributions
encompass identifying causal relationships within the food security system,
harmonizing a comprehensive database, and estimating the causal effect of
humanitarian interventions on malnutrition. Our results revealed no significant
effects, likely due to limited sample size, suboptimal data quality, and an
imperfect causal graph resulting from our limited understanding of
multidisciplinary systems like food security. This underscores the need to
enhance data collection and refine causal models with domain experts for more
effective future interventions and policies, improving transparency and
accountability in humanitarian aid. | [
"Jordi Cerdà-Bautista",
"José María Tárraga",
"Vasileios Sitokonstantinou",
"Gustau Camps-Valls"
] | 2023-10-17 14:09:45 | http://arxiv.org/abs/2310.11287v1 | http://arxiv.org/pdf/2310.11287v1 | 2310.11287v1 |
Self-supervision meets kernel graph neural models: From architecture to augmentations | Graph representation learning has now become the de facto standard when
handling graph-structured data, with the framework of message-passing graph
neural networks (MPNN) being the most prevailing algorithmic tool. Despite its
popularity, the family of MPNNs suffers from several drawbacks such as
transparency and expressivity. Recently, the idea of designing neural models on
graphs using the theory of graph kernels has emerged as a more transparent as
well as sometimes more expressive alternative to MPNNs known as kernel graph
neural networks (KGNNs). Developments on KGNNs are currently a nascent field of
research, leaving several challenges from algorithmic design and adaptation to
other learning paradigms such as self-supervised learning. In this paper, we
improve the design and learning of KGNNs. Firstly, we extend the algorithmic
formulation of KGNNs by allowing a more flexible graph-level similarity
definition that encompasses former proposals like random walk graph kernel, as
well as providing a smoother optimization objective that alleviates the need of
introducing combinatorial learning procedures. Secondly, we enhance KGNNs
through the lens of self-supervision via developing a novel
structure-preserving graph data augmentation method called latent graph
augmentation (LGA). Finally, we perform extensive empirical evaluations to
demonstrate the efficacy of our proposed mechanisms. Experimental results over
benchmark datasets suggest that our proposed model achieves competitive
performance that is comparable to or sometimes outperforming state-of-the-art
graph representation learning frameworks with or without self-supervision on
graph classification tasks. Comparisons against other previously established
graph data augmentation methods verify that the proposed LGA augmentation
scheme captures better semantics of graph-level invariance. | [
"Jiawang Dan",
"Ruofan Wu",
"Yunpeng Liu",
"Baokun Wang",
"Changhua Meng",
"Tengfei Liu",
"Tianyi Zhang",
"Ningtao Wang",
"Xing Fu",
"Qi Li",
"Weiqiang Wang"
] | 2023-10-17 14:04:22 | http://arxiv.org/abs/2310.11281v1 | http://arxiv.org/pdf/2310.11281v1 | 2310.11281v1 |
Gromov-Wassertein-like Distances in the Gaussian Mixture Models Space | In this paper, we introduce two Gromov-Wasserstein-type distances on the set
of Gaussian mixture models. The first one takes the form of a
Gromov-Wasserstein distance between two discrete distributionson the space of
Gaussian measures. This distance can be used as an alternative to
Gromov-Wasserstein for applications which only require to evaluate how far the
distributions are from each other but does not allow to derive directly an
optimal transportation plan between clouds of points. To design a way to define
such a transportation plan, we introduce another distance between measures
living in incomparable spaces that turns out to be closely related to
Gromov-Wasserstein. When restricting the set of admissible transportation
couplings to be themselves Gaussian mixture models in this latter, this defines
another distance between Gaussian mixture models that can be used as another
alternative to Gromov-Wasserstein and which allows to derive an optimal
assignment between points. Finally, we design a transportation plan associated
with the first distance by analogy with the second, and we illustrate their
practical uses on medium-to-large scale problems such as shape matching and
hyperspectral image color transfer. | [
"Antoine Salmona",
"Julie Delon",
"Agnès Desolneux"
] | 2023-10-17 13:22:36 | http://arxiv.org/abs/2310.11256v1 | http://arxiv.org/pdf/2310.11256v1 | 2310.11256v1 |
CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion | Code completion models have made significant progress in recent years, yet
current popular evaluation datasets, such as HumanEval and MBPP, predominantly
focus on code completion tasks within a single file. This over-simplified
setting falls short of representing the real-world software development
scenario where repositories span multiple files with numerous cross-file
dependencies, and accessing and understanding cross-file context is often
required to complete the code correctly.
To fill in this gap, we propose CrossCodeEval, a diverse and multilingual
code completion benchmark that necessitates an in-depth cross-file contextual
understanding to complete the code accurately. CrossCodeEval is built on a
diverse set of real-world, open-sourced, permissively-licensed repositories in
four popular programming languages: Python, Java, TypeScript, and C#. To create
examples that strictly require cross-file context for accurate completion, we
propose a straightforward yet efficient static-analysis-based approach to
pinpoint the use of cross-file context within the current file.
Extensive experiments on state-of-the-art code language models like CodeGen
and StarCoder demonstrate that CrossCodeEval is extremely challenging when the
relevant cross-file context is absent, and we see clear improvements when
adding these context into the prompt. However, despite such improvements, the
pinnacle of performance remains notably unattained even with the
highest-performing model, indicating that CrossCodeEval is also capable of
assessing model's capability in leveraging extensive context to make better
code completion. Finally, we benchmarked various methods in retrieving
cross-file context, and show that CrossCodeEval can also be used to measure the
capability of code retrievers. | [
"Yangruibo Ding",
"Zijian Wang",
"Wasi Uddin Ahmad",
"Hantian Ding",
"Ming Tan",
"Nihal Jain",
"Murali Krishna Ramanathan",
"Ramesh Nallapati",
"Parminder Bhatia",
"Dan Roth",
"Bing Xiang"
] | 2023-10-17 13:18:01 | http://arxiv.org/abs/2310.11248v1 | http://arxiv.org/pdf/2310.11248v1 | 2310.11248v1 |
Entity Matching using Large Language Models | Entity Matching is the task of deciding whether two entity descriptions refer
to the same real-world entity. Entity Matching is a central step in most data
integration pipelines and an enabler for many e-commerce applications which
require to match products offers from different vendors. State-of-the-art
entity matching methods often rely on pre-trained language models (PLMs) such
as BERT or RoBERTa. Two major drawbacks of these models for entity matching are
that (i) the models require significant amounts of task-specific training data
and (ii) the fine-tuned models are not robust concerning out-of-distribution
entities. In this paper, we investigate using large language models (LLMs) for
entity matching as a less domain-specific training data reliant and more robust
alternative to PLM-based matchers. Our study covers hosted LLMs, such as GPT3.5
and GPT4, as well as open source LLMs based on Llama2 which can be run locally.
We evaluate these models in a zero-shot scenario as well as a scenario where
task-specific training data is available. We compare different prompt designs
as well as the prompt sensitivity of the models in the zero-shot scenario. We
investigate (i) the selection of in-context demonstrations, (ii) the generation
of matching rules, as well as (iii) fine-tuning GPT3.5 in the second scenario
using the same pool of training data across the different approaches. Our
experiments show that GPT4 without any task-specific training data outperforms
fine-tuned PLMs (RoBERTa and Ditto) on three out of five benchmark datasets
reaching F1 scores around 90%. The experiments with in-context learning and
rule generation show that all models beside of GPT4 benefit from these
techniques (on average 5.9% and 2.2% F1), while GPT4 does not need such
additional guidance in most cases... | [
"Ralph Peeters",
"Christian Bizer"
] | 2023-10-17 13:12:32 | http://arxiv.org/abs/2310.11244v1 | http://arxiv.org/pdf/2310.11244v1 | 2310.11244v1 |
Learning to Sample Better | These lecture notes provide an introduction to recent advances in generative
modeling methods based on the dynamical transportation of measures, by means of
which samples from a simple base measure are mapped to samples from a target
measure of interest. Special emphasis is put on the applications of these
methods to Monte-Carlo (MC) sampling techniques, such as importance sampling
and Markov Chain Monte-Carlo (MCMC) schemes. In this context, it is shown how
the maps can be learned variationally using data generated by MC sampling, and
how they can in turn be used to improve such sampling in a positive feedback
loop. | [
"Michael S. Albergo",
"Eric Vanden-Eijnden"
] | 2023-10-17 13:03:49 | http://arxiv.org/abs/2310.11232v1 | http://arxiv.org/pdf/2310.11232v1 | 2310.11232v1 |
Zipformer: A faster and better encoder for automatic speech recognition | The Conformer has become the most popular encoder model for automatic speech
recognition (ASR). It adds convolution modules to a transformer to learn both
local and global dependencies. In this work we describe a faster, more
memory-efficient, and better-performing transformer, called Zipformer. Modeling
changes include: 1) a U-Net-like encoder structure where middle stacks operate
at lower frame rates; 2) reorganized block structure with more modules, within
which we re-use attention weights for efficiency; 3) a modified form of
LayerNorm called BiasNorm allows us to retain some length information; 4) new
activation functions SwooshR and SwooshL work better than Swish. We also
propose a new optimizer, called ScaledAdam, which scales the update by each
tensor's current scale to keep the relative change about the same, and also
explictly learns the parameter scale. It achieves faster convergence and better
performance than Adam. Extensive experiments on LibriSpeech, Aishell-1, and
WenetSpeech datasets demonstrate the effectiveness of our proposed Zipformer
over other state-of-the-art ASR models. Our code is publicly available at
https://github.com/k2-fsa/icefall. | [
"Zengwei Yao",
"Liyong Guo",
"Xiaoyu Yang",
"Wei Kang",
"Fangjun Kuang",
"Yifan Yang",
"Zengrui Jin",
"Long Lin",
"Daniel Povey"
] | 2023-10-17 13:01:10 | http://arxiv.org/abs/2310.11230v1 | http://arxiv.org/pdf/2310.11230v1 | 2310.11230v1 |
Understanding Fairness Surrogate Functions in Algorithmic Fairness | It has been observed that machine learning algorithms exhibit biased
predictions against certain population groups. To mitigate such bias while
achieving comparable accuracy, a promising approach is to introduce surrogate
functions of the concerned fairness definition and solve a constrained
optimization problem. However, an intriguing issue in previous work is that
such fairness surrogate functions may yield unfair results. In this work, in
order to deeply understand this issue, taking a widely used fairness
definition, demographic parity as an example, we both theoretically and
empirically show that there is a surrogate-fairness gap between the fairness
definition and the fairness surrogate function. The "gap" directly determines
whether a surrogate function is an appropriate substitute for a fairness
definition. Also, the theoretical analysis and experimental results about the
"gap" motivate us that the unbounded surrogate functions will be affected by
the points far from the decision boundary, which is the large margin points
issue investigated in this paper. To address it, we propose the general sigmoid
surrogate with a rigorous and reliable fairness guarantee. Interestingly, the
theory also provides insights into two important issues that deal with the
large margin points as well as obtaining a more balanced dataset are beneficial
to fairness. Furthermore, we elaborate a novel and general algorithm called
Balanced Surrogate, which iteratively reduces the "gap" to improve fairness.
Finally, we provide empirical evidence showing that our methods achieve better
fairness performance in three real-world datasets. | [
"Wei Yao",
"Zhanke Zhou",
"Zhicong Li",
"Bo Han",
"Yong Liu"
] | 2023-10-17 12:40:53 | http://arxiv.org/abs/2310.11211v2 | http://arxiv.org/pdf/2310.11211v2 | 2310.11211v2 |
Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations | Large language models (LLMs) such as ChatGPT have demonstrated superior
performance on a variety of natural language processing (NLP) tasks including
sentiment analysis, mathematical reasoning and summarization. Furthermore,
since these models are instruction-tuned on human conversations to produce
"helpful" responses, they can and often will produce explanations along with
the response, which we call self-explanations. For example, when analyzing the
sentiment of a movie review, the model may output not only the positivity of
the sentiment, but also an explanation (e.g., by listing the sentiment-laden
words such as "fantastic" and "memorable" in the review). How good are these
automatically generated self-explanations? In this paper, we investigate this
question on the task of sentiment analysis and for feature attribution
explanation, one of the most commonly studied settings in the interpretability
literature (for pre-ChatGPT models). Specifically, we study different ways to
elicit the self-explanations, evaluate their faithfulness on a set of
evaluation metrics, and compare them to traditional explanation methods such as
occlusion or LIME saliency maps. Through an extensive set of experiments, we
find that ChatGPT's self-explanations perform on par with traditional ones, but
are quite different from them according to various agreement metrics, meanwhile
being much cheaper to produce (as they are generated along with the
prediction). In addition, we identified several interesting characteristics of
them, which prompt us to rethink many current model interpretability practices
in the era of ChatGPT(-like) LLMs. | [
"Shiyuan Huang",
"Siddarth Mamidanna",
"Shreedhar Jangam",
"Yilun Zhou",
"Leilani H. Gilpin"
] | 2023-10-17 12:34:32 | http://arxiv.org/abs/2310.11207v1 | http://arxiv.org/pdf/2310.11207v1 | 2310.11207v1 |
Whole-brain radiomics for clustered federated personalization in brain tumor segmentation | Federated learning and its application to medical image segmentation have
recently become a popular research topic. This training paradigm suffers from
statistical heterogeneity between participating institutions' local datasets,
incurring convergence slowdown as well as potential accuracy loss compared to
classical training. To mitigate this effect, federated personalization emerged
as the federated optimization of one model per institution. We propose a novel
personalization algorithm tailored to the feature shift induced by the usage of
different scanners and acquisition parameters by different institutions. This
method is the first to account for both inter and intra-institution feature
shift (multiple scanners used in a single institution). It is based on the
computation, within each centre, of a series of radiomic features capturing the
global texture of each 3D image volume, followed by a clustering analysis
pooling all feature vectors transferred from the local institutions to the
central server. Each computed clustered decentralized dataset (potentially
including data from different institutions) then serves to finetune a global
model obtained through classical federated learning. We validate our approach
on the Federated Brain Tumor Segmentation 2022 Challenge dataset (FeTS2022).
Our code is available at (https://github.com/MatthisManthe/radiomics_CFFL). | [
"Matthis Manthe",
"Stefan Duffner",
"Carole Lartizien"
] | 2023-10-17 12:33:43 | http://arxiv.org/abs/2310.11480v1 | http://arxiv.org/pdf/2310.11480v1 | 2310.11480v1 |
Federated Learning with Nonvacuous Generalisation Bounds | We introduce a novel strategy to train randomised predictors in federated
learning, where each node of the network aims at preserving its privacy by
releasing a local predictor but keeping secret its training dataset with
respect to the other nodes. We then build a global randomised predictor which
inherits the properties of the local private predictors in the sense of a
PAC-Bayesian generalisation bound. We consider the synchronous case where all
nodes share the same training objective (derived from a generalisation bound),
and the asynchronous case where each node may have its own personalised
training objective. We show through a series of numerical experiments that our
approach achieves a comparable predictive performance to that of the batch
approach where all datasets are shared across nodes. Moreover the predictors
are supported by numerically nonvacuous generalisation bounds while preserving
privacy for each node. We explicitly compute the increment on predictive
performance and generalisation bounds between batch and federated settings,
highlighting the price to pay to preserve privacy. | [
"Pierre Jobic",
"Maxime Haddouche",
"Benjamin Guedj"
] | 2023-10-17 12:29:29 | http://arxiv.org/abs/2310.11203v1 | http://arxiv.org/pdf/2310.11203v1 | 2310.11203v1 |
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms | The objective of this study is to investigate the application of various
channel attention mechanisms within the domain of brain-computer interface
(BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a
powerful evolution of spatial filters traditionally used for motor imagery
decoding. This study systematically compares such mechanisms by integrating
them into a lightweight architecture framework to evaluate their impact. We
carefully construct a straightforward and lightweight baseline architecture
designed to seamlessly integrate different channel attention mechanisms. This
approach is contrary to previous works which only investigate one attention
mechanism and usually build a very complex, sometimes nested architecture. Our
framework allows us to evaluate and compare the impact of different attention
mechanisms under the same circumstances. The easy integration of different
channel attention mechanisms as well as the low computational complexity
enables us to conduct a wide range of experiments on three datasets to
thoroughly assess the effectiveness of the baseline model and the attention
mechanisms. Our experiments demonstrate the strength and generalizability of
our architecture framework as well as how channel attention mechanisms can
improve the performance while maintaining the small memory footprint and low
computational complexity of our baseline architecture. Our architecture
emphasizes simplicity, offering easy integration of channel attention
mechanisms, while maintaining a high degree of generalizability across
datasets, making it a versatile and efficient solution for EEG motor imagery
decoding within brain-computer interfaces. | [
"Martin Wimpff",
"Leonardo Gizzi",
"Jan Zerfowski",
"Bin Yang"
] | 2023-10-17 12:25:31 | http://arxiv.org/abs/2310.11198v1 | http://arxiv.org/pdf/2310.11198v1 | 2310.11198v1 |
A Modified EXP3 and Its Adaptive Variant in Adversarial Bandits with Multi-User Delayed Feedback | For the adversarial multi-armed bandit problem with delayed feedback, we
consider that the delayed feedback results are from multiple users and are
unrestricted on internal distribution. As the player picks an arm, feedback
from multiple users may not be received instantly yet after an arbitrary delay
of time which is unknown to the player in advance. For different users in a
round, the delays in feedback have no latent correlation. Thus, we formulate an
adversarial multi-armed bandit problem with multi-user delayed feedback and
design a modified EXP3 algorithm named MUD-EXP3, which makes a decision at each
round by considering the importance-weighted estimator of the received feedback
from different users. On the premise of known terminal round index $T$, the
number of users $M$, the number of arms $N$, and upper bound of delay
$d_{max}$, we prove a regret of
$\mathcal{O}(\sqrt{TM^2\ln{N}(N\mathrm{e}+4d_{max})})$. Furthermore, for the
more common case of unknown $T$, an adaptive algorithm named AMUD-EXP3 is
proposed with a sublinear regret with respect to $T$. Finally, extensive
experiments are conducted to indicate the correctness and effectiveness of our
algorithms. | [
"Yandi Li",
"Jianxiong Guo"
] | 2023-10-17 12:08:15 | http://arxiv.org/abs/2310.11188v1 | http://arxiv.org/pdf/2310.11188v1 | 2310.11188v1 |
Efficiently Visualizing Large Graphs | Most existing graph visualization methods based on dimension reduction are
limited to relatively small graphs due to performance issues. In this work, we
propose a novel dimension reduction method for graph visualization, called
t-Distributed Stochastic Graph Neighbor Embedding (t-SGNE). t-SGNE is
specifically designed to visualize cluster structures in the graph. As a
variant of the standard t-SNE method, t-SGNE avoids the time-consuming
computations of pairwise similarity. Instead, it uses the neighbor structures
of the graph to reduce the time complexity from quadratic to linear, thus
supporting larger graphs. In addition, to suit t-SGNE, we combined Laplacian
Eigenmaps with the shortest path algorithm in graphs to form the graph
embedding algorithm ShortestPath Laplacian Eigenmaps Embedding (SPLEE).
Performing SPLEE to obtain a high-dimensional embedding of the large-scale
graph and then using t-SGNE to reduce its dimension for visualization, we are
able to visualize graphs with up to 300K nodes and 1M edges within 5 minutes
and achieve approximately 10% improvement in visualization quality. Codes and
data are available at
https://github.com/Charlie-XIAO/embedding-visualization-test. | [
"Xinyu Li",
"Yao Xiao",
"Yuchen Zhou"
] | 2023-10-17 12:07:14 | http://arxiv.org/abs/2310.11186v1 | http://arxiv.org/pdf/2310.11186v1 | 2310.11186v1 |
MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection | Multimodal time series (MTS) anomaly detection is crucial for maintaining the
safety and stability of working devices (e.g., water treatment system and
spacecraft), whose data are characterized by multivariate time series with
diverse modalities. Although recent deep learning methods show great potential
in anomaly detection, they do not explicitly capture spatial-temporal
relationships between univariate time series of different modalities, resulting
in more false negatives and false positives. In this paper, we propose a
multimodal spatial-temporal graph attention network (MST-GAT) to tackle this
problem. MST-GAT first employs a multimodal graph attention network (M-GAT) and
a temporal convolution network to capture the spatial-temporal correlation in
multimodal time series. Specifically, M-GAT uses a multi-head attention module
and two relational attention modules (i.e., intra- and inter-modal attention)
to model modal correlations explicitly. Furthermore, MST-GAT optimizes the
reconstruction and prediction modules simultaneously. Experimental results on
four multimodal benchmarks demonstrate that MST-GAT outperforms the
state-of-the-art baselines. Further analysis indicates that MST-GAT strengthens
the interpretability of detected anomalies by locating the most anomalous
univariate time series. | [
"Chaoyue Ding",
"Shiliang Sun",
"Jing Zhao"
] | 2023-10-17 11:37:40 | http://arxiv.org/abs/2310.11169v1 | http://arxiv.org/pdf/2310.11169v1 | 2310.11169v1 |
Serenade: A Model for Human-in-the-loop Automatic Chord Estimation | Computational harmony analysis is important for MIR tasks such as automatic
segmentation, corpus analysis and automatic chord label estimation. However,
recent research into the ambiguous nature of musical harmony, causing limited
inter-rater agreement, has made apparent that there is a glass ceiling for
common metrics such as accuracy. Commonly, these issues are addressed either in
the training data itself by creating majority-rule annotations or during the
training phase by learning soft targets. We propose a novel alternative
approach in which a human and an autoregressive model together co-create a
harmonic annotation for an audio track. After automatically generating harmony
predictions, a human sparsely annotates parts with low model confidence and the
model then adjusts its predictions following human guidance. We evaluate our
model on a dataset of popular music and we show that, with this
human-in-the-loop approach, harmonic analysis performance improves over a
model-only approach. The human contribution is amplified by the second,
constrained prediction of the model. | [
"Hendrik Vincent Koops",
"Gianluca Micchi",
"Ilaria Manco",
"Elio Quinton"
] | 2023-10-17 11:31:29 | http://arxiv.org/abs/2310.11165v1 | http://arxiv.org/pdf/2310.11165v1 | 2310.11165v1 |
Probing the Creativity of Large Language Models: Can models produce divergent semantic association? | Large language models possess remarkable capacity for processing language,
but it remains unclear whether these models can further generate creative
content. The present study aims to investigate the creative thinking of large
language models through a cognitive perspective. We utilize the divergent
association task (DAT), an objective measurement of creativity that asks models
to generate unrelated words and calculates the semantic distance between them.
We compare the results across different models and decoding strategies. Our
findings indicate that: (1) When using the greedy search strategy, GPT-4
outperforms 96% of humans, while GPT-3.5-turbo exceeds the average human level.
(2) Stochastic sampling and temperature scaling are effective to obtain higher
DAT scores for models except GPT-4, but face a trade-off between creativity and
stability. These results imply that advanced large language models have
divergent semantic associations, which is a fundamental process underlying
creativity. | [
"Honghua Chen",
"Nai Ding"
] | 2023-10-17 11:23:32 | http://arxiv.org/abs/2310.11158v1 | http://arxiv.org/pdf/2310.11158v1 | 2310.11158v1 |
A new high-resolution indoor radon map for Germany using a machine learning based probabilistic exposure model | Radon is a carcinogenic, radioactive gas that can accumulate indoors. Indoor
radon exposure at the national scale is usually estimated on the basis of
extensive measurement campaigns. However, characteristics of the sample often
differ from the characteristics of the population due to the large number of
relevant factors such as the availability of geogenic radon or floor level.
Furthermore, the sample size usually does not allow exposure estimation with
high spatial resolution. We propose a model-based approach that allows a more
realistic estimation of indoor radon distribution with a higher spatial
resolution than a purely data-based approach. We applied a two-stage modelling
approach: 1) a quantile regression forest using environmental and building data
as predictors was applied to estimate the probability distribution function of
indoor radon for each floor level of each residential building in Germany; (2)
a probabilistic Monte Carlo sampling technique enabled the combination and
population weighting of floor-level predictions. In this way, the uncertainty
of the individual predictions is effectively propagated into the estimate of
variability at the aggregated level. The results give an arithmetic mean of 63
Bq/m3, a geometric mean of 41 Bq/m3 and a 95 %ile of 180 Bq/m3. The exceedance
probability for 100 Bq/m3 and 300 Bq/m3 are 12.5 % (10.5 million people) and
2.2 % (1.9 million people), respectively. In large cities, individual indoor
radon exposure is generally lower than in rural areas, which is a due to the
different distribution of the population on floor levels. The advantages of our
approach are 1) an accurate exposure estimation even if the survey was not
fully representative with respect to the main controlling factors, and 2) an
estimate of the exposure distribution with a much higher spatial resolution
than basic descriptive statistics. | [
"Eric Petermann",
"Peter Bossew",
"Joachim Kemski",
"Valeria Gruber",
"Nils Suhr",
"Bernd Hoffmann"
] | 2023-10-17 10:51:05 | http://arxiv.org/abs/2310.11143v1 | http://arxiv.org/pdf/2310.11143v1 | 2310.11143v1 |
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference | Diffusion models have impressive image generation capability, but low-quality
generations still exist, and their identification remains challenging due to
the lack of a proper sample-wise metric. To address this, we propose BayesDiff,
a pixel-wise uncertainty estimator for generations from diffusion models based
on Bayesian inference. In particular, we derive a novel uncertainty iteration
principle to characterize the uncertainty dynamics in diffusion, and leverage
the last-layer Laplace approximation for efficient Bayesian inference. The
estimated pixel-wise uncertainty can not only be aggregated into a sample-wise
metric to filter out low-fidelity images but also aids in augmenting successful
generations and rectifying artifacts in failed generations in text-to-image
tasks. Extensive experiments demonstrate the efficacy of BayesDiff and its
promise for practical applications. | [
"Siqi Kou",
"Lei Gan",
"Dequan Wang",
"Chongxuan Li",
"Zhijie Deng"
] | 2023-10-17 10:45:28 | http://arxiv.org/abs/2310.11142v1 | http://arxiv.org/pdf/2310.11142v1 | 2310.11142v1 |
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control | The combination of deep reinforcement learning (DRL) with ensemble methods
has been proved to be highly effective in addressing complex sequential
decision-making problems. This success can be primarily attributed to the
utilization of multiple models, which enhances both the robustness of the
policy and the accuracy of value function estimation. However, there has been
limited analysis of the empirical success of current ensemble RL methods thus
far. Our new analysis reveals that the sample efficiency of previous ensemble
DRL algorithms may be limited by sub-policies that are not as diverse as they
could be. Motivated by these findings, our study introduces a new ensemble RL
algorithm, termed \textbf{T}rajectories-awar\textbf{E} \textbf{E}nsemble
exploratio\textbf{N} (TEEN). The primary goal of TEEN is to maximize the
expected return while promoting more diverse trajectories. Through extensive
experiments, we demonstrate that TEEN not only enhances the sample diversity of
the ensemble policy compared to using sub-policies alone but also improves the
performance over ensemble RL algorithms. On average, TEEN outperforms the
baseline ensemble DRL algorithms by 41\% in performance on the tested
representative environments. | [
"Chao Li",
"Chen Gong",
"Qiang He",
"Xinwen Hou"
] | 2023-10-17 10:40:05 | http://arxiv.org/abs/2310.11138v1 | http://arxiv.org/pdf/2310.11138v1 | 2310.11138v1 |
Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation | Conditional independence testing (CIT) is a common task in machine learning,
e.g., for variable selection, and a main component of constraint-based causal
discovery. While most current CIT approaches assume that all variables are
numerical or all variables are categorical, many real-world applications
involve mixed-type datasets that include numerical and categorical variables.
Non-parametric CIT can be conducted using conditional mutual information (CMI)
estimators combined with a local permutation scheme. Recently, two novel CMI
estimators for mixed-type datasets based on k-nearest-neighbors (k-NN) have
been proposed. As with any k-NN method, these estimators rely on the definition
of a distance metric. One approach computes distances by a one-hot encoding of
the categorical variables, essentially treating categorical variables as
discrete-numerical, while the other expresses CMI by entropy terms where the
categorical variables appear as conditions only. In this work, we study these
estimators and propose a variation of the former approach that does not treat
categorical variables as numeric. Our numerical experiments show that our
variant detects dependencies more robustly across different data distributions
and preprocessing types. | [
"Oana-Iuliana Popescu",
"Andreas Gerhardus",
"Jakob Runge"
] | 2023-10-17 10:29:23 | http://arxiv.org/abs/2310.11132v1 | http://arxiv.org/pdf/2310.11132v1 | 2310.11132v1 |
FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation | The Open Radio Access Network (O-RAN) is a burgeoning market with projected
growth in the upcoming years. RAN has the highest CAPEX impact on the network
and, most importantly, consumes 73% of its total energy. That makes it an ideal
target for optimisation through the integration of Machine Learning (ML).
However, the energy consumption of ML is frequently overlooked in such
ecosystems. Our work addresses this critical aspect by presenting FROST -
Flexible Reconfiguration method with Online System Tuning - a solution for
energy-aware ML pipelines that adhere to O-RAN's specifications and principles.
FROST is capable of profiling the energy consumption of an ML pipeline and
optimising the hardware accordingly, thereby limiting the power draw. Our
findings indicate that FROST can achieve energy savings of up to 26.4% without
compromising the model's accuracy or introducing significant time delays. | [
"Ioannis Mavromatis",
"Stefano De Feo",
"Pietro Carnelli",
"Robert J. Piechocki",
"Aftab Khan"
] | 2023-10-17 10:28:28 | http://arxiv.org/abs/2310.11131v1 | http://arxiv.org/pdf/2310.11131v1 | 2310.11131v1 |
Topological Expressivity of ReLU Neural Networks | We study the expressivity of ReLU neural networks in the setting of a binary
classification problem from a topological perspective. Recently, empirical
studies showed that neural networks operate by changing topology, transforming
a topologically complicated data set into a topologically simpler one as it
passes through the layers. This topological simplification has been measured by
Betti numbers, which are algebraic invariants of a topological space. We use
the same measure to establish lower and upper bounds on the topological
simplification a ReLU neural network can achieve with a given architecture. We
therefore contribute to a better understanding of the expressivity of ReLU
neural networks in the context of binary classification problems by shedding
light on their ability to capture the underlying topological structure of the
data. In particular the results show that deep ReLU neural networks are
exponentially more powerful than shallow ones in terms of topological
simplification. This provides a mathematically rigorous explanation why deeper
networks are better equipped to handle complex and topologically rich datasets. | [
"Ekin Ergen",
"Moritz Grillo"
] | 2023-10-17 10:28:00 | http://arxiv.org/abs/2310.11130v1 | http://arxiv.org/pdf/2310.11130v1 | 2310.11130v1 |
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction | Accurate uncertainty quantification in graph neural networks (GNNs) is
essential, especially in high-stakes domains where GNNs are frequently
employed. Conformal prediction (CP) offers a promising framework for
quantifying uncertainty by providing $\textit{valid}$ prediction sets for any
black-box model. CP ensures formal probabilistic guarantees that a prediction
set contains a true label with a desired probability. However, the size of
prediction sets, known as $\textit{inefficiency}$, is influenced by the
underlying model and data generating process. On the other hand, Bayesian
learning also provides a credible region based on the estimated posterior
distribution, but this region is $\textit{well-calibrated}$ only when the model
is correctly specified. Building on a recent work that introduced a scaling
parameter for constructing valid credible regions from posterior estimate, our
study explores the advantages of incorporating a temperature parameter into
Bayesian GNNs within CP framework. We empirically demonstrate the existence of
temperatures that result in more efficient prediction sets. Furthermore, we
conduct an analysis to identify the factors contributing to inefficiency and
offer valuable insights into the relationship between CP performance and model
calibration. | [
"Seohyeon Cha",
"Honggu Kang",
"Joonhyuk Kang"
] | 2023-10-17 10:24:25 | http://arxiv.org/abs/2310.11479v1 | http://arxiv.org/pdf/2310.11479v1 | 2310.11479v1 |
Sensitivity-Aware Amortized Bayesian Inference | Bayesian inference is a powerful framework for making probabilistic
inferences and decisions under uncertainty. Fundamental choices in modern
Bayesian workflows concern the specification of the likelihood function and
prior distributions, the posterior approximator, and the data. Each choice can
significantly influence model-based inference and subsequent decisions, thereby
necessitating sensitivity analysis. In this work, we propose a multifaceted
approach to integrate sensitivity analyses into amortized Bayesian inference
(ABI, i.e., simulation-based inference with neural networks). First, we utilize
weight sharing to encode the structural similarities between alternative
likelihood and prior specifications in the training process with minimal
computational overhead. Second, we leverage the rapid inference of neural
networks to assess sensitivity to various data perturbations or pre-processing
procedures. In contrast to most other Bayesian approaches, both steps
circumvent the costly bottleneck of refitting the model(s) for each choice of
likelihood, prior, or dataset. Finally, we propose to use neural network
ensembles to evaluate variation in results induced by unreliable approximation
on unseen data. We demonstrate the effectiveness of our method in applied
modeling problems, ranging from the estimation of disease outbreak dynamics and
global warming thresholds to the comparison of human decision-making models.
Our experiments showcase how our approach enables practitioners to effectively
unveil hidden relationships between modeling choices and inferential
conclusions. | [
"Lasse Elsemüller",
"Hans Olischläger",
"Marvin Schmitt",
"Paul-Christian Bürkner",
"Ullrich Köthe",
"Stefan T. Radev"
] | 2023-10-17 10:14:10 | http://arxiv.org/abs/2310.11122v2 | http://arxiv.org/pdf/2310.11122v2 | 2310.11122v2 |
Super resolution of histopathological frozen sections via deep learning preserving tissue structure | Histopathology plays a pivotal role in medical diagnostics. In contrast to
preparing permanent sections for histopathology, a time-consuming process,
preparing frozen sections is significantly faster and can be performed during
surgery, where the sample scanning time should be optimized. Super-resolution
techniques allow imaging the sample in lower magnification and sparing scanning
time. In this paper, we present a new approach to super resolution for
histopathological frozen sections, with focus on achieving better distortion
measures, rather than pursuing photorealistic images that may compromise
critical diagnostic information. Our deep-learning architecture focuses on
learning the error between interpolated images and real images, thereby it
generates high-resolution images while preserving critical image details,
reducing the risk of diagnostic misinterpretation. This is done by leveraging
the loss functions in the frequency domain, assigning higher weights to the
reconstruction of complex, high-frequency components. In comparison to existing
methods, we obtained significant improvements in terms of Structural Similarity
Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), as well as indicated
details that lost in the low-resolution frozen-section images, affecting the
pathologist's clinical decisions. Our approach has a great potential in
providing more-rapid frozen-section imaging, with less scanning, while
preserving the high resolution in the imaged sample. | [
"Elad Yoshai",
"Gil Goldinger",
"Miki Haifler",
"Natan T. Shaked"
] | 2023-10-17 09:52:54 | http://arxiv.org/abs/2310.11112v1 | http://arxiv.org/pdf/2310.11112v1 | 2310.11112v1 |
Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data | Linear Discriminant Analysis (LDA) is one of the oldest and most popular
linear methods for supervised classification problems. In this paper, we
demonstrate that it is possible to compute the exact projection vector from LDA
models based on unlabelled data, if some minimal prior information is
available. More precisely, we show that only one of the following three pieces
of information is actually sufficient to compute the LDA projection vector if
only unlabelled data are available: (1) the class average of one of the two
classes, (2) the difference between both class averages (up to a scaling), or
(3) the class covariance matrices (up to a scaling). These theoretical results
are validated in numerical experiments, demonstrating that this minimally
informed Linear Discriminant Analysis (MILDA) model closely matches the
performance of a supervised LDA model. Furthermore, we show that the MILDA
projection vector can be computed in a closed form with a computational cost
comparable to LDA and is able to quickly adapt to non-stationary data, making
it well-suited to use as an adaptive classifier. | [
"Nicolas Heintz",
"Tom Francart",
"Alexander Bertrand"
] | 2023-10-17 09:50:31 | http://arxiv.org/abs/2310.11110v1 | http://arxiv.org/pdf/2310.11110v1 | 2310.11110v1 |
Local Lipschitz Constant Computation of ReLU-FNNs: Upper Bound Computation with Exactness Verification | This paper is concerned with the computation of the local Lipschitz constant
of feedforward neural networks (FNNs) with activation functions being rectified
linear units (ReLUs). The local Lipschitz constant of an FNN for a target input
is a reasonable measure for its quantitative evaluation of the reliability. By
following a standard procedure using multipliers that capture the behavior of
ReLUs,we first reduce the upper bound computation problem of the local
Lipschitz constant into a semidefinite programming problem (SDP). Here we newly
introduce copositive multipliers to capture the ReLU behavior accurately. Then,
by considering the dual of the SDP for the upper bound computation, we second
derive a viable test to conclude the exactness of the computed upper bound.
However, these SDPs are intractable for practical FNNs with hundreds of ReLUs.
To address this issue, we further propose a method to construct a reduced order
model whose input-output property is identical to the original FNN over a
neighborhood of the target input. We finally illustrate the effectiveness of
the model reduction and exactness verification methods with numerical examples
of practical FNNs. | [
"Yoshio Ebihara",
"Xin Dai",
"Victor Magron",
"Dimitri Peaucelle",
"Sophie Tarbouriech"
] | 2023-10-17 09:37:16 | http://arxiv.org/abs/2310.11104v1 | http://arxiv.org/pdf/2310.11104v1 | 2310.11104v1 |
ASP: Automatic Selection of Proxy dataset for efficient AutoML | Deep neural networks have gained great success due to the increasing amounts
of data, and diverse effective neural network designs. However, it also brings
a heavy computing burden as the amount of training data is proportional to the
training time. In addition, a well-behaved model requires repeated trials of
different structure designs and hyper-parameters, which may take a large amount
of time even with state-of-the-art (SOTA) hyper-parameter optimization (HPO)
algorithms and neural architecture search (NAS) algorithms. In this paper, we
propose an Automatic Selection of Proxy dataset framework (ASP) aimed to
dynamically find the informative proxy subsets of training data at each epoch,
reducing the training data size as well as saving the AutoML processing time.
We verify the effectiveness and generalization of ASP on CIFAR10, CIFAR100,
ImageNet16-120, and ImageNet-1k, across various public model benchmarks. The
experiment results show that ASP can obtain better results than other data
selection methods at all selection ratios. ASP can also enable much more
efficient AutoML processing with a speedup of 2x-20x while obtaining better
architectures and better hyper-parameters compared to utilizing the entire
dataset. | [
"Peng Yao",
"Chao Liao",
"Jiyuan Jia",
"Jianchao Tan",
"Bin Chen",
"Chengru Song",
"Di Zhang"
] | 2023-10-17 09:36:22 | http://arxiv.org/abs/2310.11478v1 | http://arxiv.org/pdf/2310.11478v1 | 2310.11478v1 |
HGCVAE: Integrating Generative and Contrastive Learning for Heterogeneous Graph Learning | Generative self-supervised learning (SSL) has exhibited significant potential
and garnered increasing interest in graph learning. In this study, we aim to
explore the problem of generative SSL in the context of heterogeneous graph
learning (HGL). The previous SSL approaches for heterogeneous graphs have
primarily relied on contrastive learning, necessitating the design of complex
views to capture heterogeneity. However, existing generative SSL methods have
not fully leveraged the capabilities of generative models to address the
challenges of HGL. In this paper, we present HGCVAE, a novel contrastive
variational graph auto-encoder that liberates HGL from the burden of intricate
heterogeneity capturing. Instead of focusing on complicated heterogeneity,
HGCVAE harnesses the full potential of generative SSL. HGCVAE innovatively
consolidates contrastive learning with generative SSL, introducing several key
innovations. Firstly, we employ a progressive mechanism to generate
high-quality hard negative samples for contrastive learning, utilizing the
power of variational inference. Additionally, we present a dynamic mask
strategy to ensure effective and stable learning. Moreover, we propose an
enhanced scaled cosine error as the criterion for better attribute
reconstruction. As an initial step in combining generative and contrastive SSL,
HGCVAE achieves remarkable results compared to various state-of-the-art
baselines, confirming its superiority. | [
"Yulan Hu",
"Zhirui Yang",
"Sheng Ouyang",
"Junchen Wan",
"Fuzheng Zhang",
"Zhongyuan Wang",
"Yong Liu"
] | 2023-10-17 09:34:34 | http://arxiv.org/abs/2310.11102v3 | http://arxiv.org/pdf/2310.11102v3 | 2310.11102v3 |
Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads | Running multiple deep neural networks (DNNs) in parallel has become an
emerging workload in both edge devices, such as mobile phones where multiple
tasks serve a single user for daily activities, and data centers, where various
requests are raised from millions of users, as seen with large language models.
To reduce the costly computational and memory requirements of these workloads,
various efficient sparsification approaches have been introduced, resulting in
widespread sparsity across different types of DNN models. In this context,
there is an emerging need for scheduling sparse multi-DNN workloads, a problem
that is largely unexplored in previous literature. This paper systematically
analyses the use-cases of multiple sparse DNNs and investigates the
opportunities for optimizations. Based on these findings, we propose Dysta, a
novel bi-level dynamic and static scheduler that utilizes both static sparsity
patterns and dynamic sparsity information for the sparse multi-DNN scheduling.
Both static and dynamic components of Dysta are jointly designed at the
software and hardware levels, respectively, to improve and refine the
scheduling approach. To facilitate future progress in the study of this class
of workloads, we construct a public benchmark that contains sparse multi-DNN
workloads across different deployment scenarios, spanning from mobile phones
and AR/VR wearables to data centers. A comprehensive evaluation on the sparse
multi-DNN benchmark demonstrates that our proposed approach outperforms the
state-of-the-art methods with up to 10% decrease in latency constraint
violation rate and nearly 4X reduction in average normalized turnaround time.
Our artifacts and code are publicly available at:
https://github.com/SamsungLabs/Sparse-Multi-DNN-Scheduling. | [
"Hongxiang Fan",
"Stylianos I. Venieris",
"Alexandros Kouris",
"Nicholas D. Lane"
] | 2023-10-17 09:25:17 | http://arxiv.org/abs/2310.11096v1 | http://arxiv.org/pdf/2310.11096v1 | 2310.11096v1 |
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs | The infrequent occurrence of overfit in deep neural networks is perplexing.
On the one hand, theory predicts that as models get larger they should
eventually become too specialized for a specific training set, with ensuing
decrease in generalization. In contrast, empirical results in image
classification indicate that increasing the training time of deep models or
using bigger models almost never hurts generalization. Is it because the way we
measure overfit is too limited? Here, we introduce a novel score for
quantifying overfit, which monitors the forgetting rate of deep models on
validation data. Presumably, this score indicates that even while
generalization improves overall, there are certain regions of the data space
where it deteriorates. When thus measured, we show that overfit can occur with
and without a decrease in validation accuracy, and may be more common than
previously appreciated. This observation may help to clarify the aforementioned
confusing picture. We use our observations to construct a new ensemble method,
based solely on the training history of a single network, which provides
significant improvement in performance without any additional cost in training
time. An extensive empirical evaluation with modern deep models shows our
method's utility on multiple datasets, neural networks architectures and
training schemes, both when training from scratch and when using pre-trained
networks in transfer learning. Notably, our method outperforms comparable
methods while being easier to implement and use, and further improves the
performance of competitive networks on Imagenet by 1\%. | [
"Uri Stern",
"Daphna Weinshall"
] | 2023-10-17 09:22:22 | http://arxiv.org/abs/2310.11094v1 | http://arxiv.org/pdf/2310.11094v1 | 2310.11094v1 |
SODA: Robust Training of Test-Time Data Adaptors | Adapting models deployed to test distributions can mitigate the performance
degradation caused by distribution shifts. However, privacy concerns may render
model parameters inaccessible. One promising approach involves utilizing
zeroth-order optimization (ZOO) to train a data adaptor to adapt the test data
to fit the deployed models. Nevertheless, the data adaptor trained with ZOO
typically brings restricted improvements due to the potential corruption of
data features caused by the data adaptor. To address this issue, we revisit ZOO
in the context of test-time data adaptation. We find that the issue directly
stems from the unreliable estimation of the gradients used to optimize the data
adaptor, which is inherently due to the unreliable nature of the pseudo-labels
assigned to the test data. Based on this observation, we propose
pseudo-label-robust data adaptation (SODA) to improve the performance of data
adaptation. Specifically, SODA leverages high-confidence predicted labels as
reliable labels to optimize the data adaptor with ZOO for label prediction. For
data with low-confidence predictions, SODA encourages the adaptor to preserve
data information to mitigate data corruption. Empirical results indicate that
SODA can significantly enhance the performance of deployed models in the
presence of distribution shifts without requiring access to model parameters. | [
"Zige Wang",
"Yonggang Zhang",
"Zhen Fang",
"Long Lan",
"Wenjing Yang",
"Bo Han"
] | 2023-10-17 09:22:20 | http://arxiv.org/abs/2310.11093v1 | http://arxiv.org/pdf/2310.11093v1 | 2310.11093v1 |
MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation | It is a long-standing challenge in modern recommender systems to effectively
make recommendations for new users, namely the cold-start problem. Cross-Domain
Recommendation (CDR) has been proposed to address this challenge, but current
ways to represent users' interests across systems are still severely limited.
We introduce Personal Knowledge Graph (PKG) as a domain-invariant interest
representation, and propose a novel CDR paradigm named MeKB-Rec. We first link
users and entities in a knowledge base to construct a PKG of users' interests,
named MeKB. Then we learn a semantic representation of MeKB for the
cross-domain recommendation. To efficiently utilize limited training data in
CDR, MeKB-Rec employs Pretrained Language Models to inject world knowledge into
understanding users' interests. Beyond most existing systems, our approach
builds a semantic mapping across domains which breaks the requirement for
in-domain user behaviors, enabling zero-shot recommendations for new users in a
low-resource domain. We experiment MeKB-Rec on well-established public CDR
datasets, and demonstrate that the new formulation % is more powerful than
previous approaches, achieves a new state-of-the-art that significantly
improves HR@10 and NDCG@10 metrics over best previous approaches by 24\%--91\%,
with a 105\% improvement for HR@10 of zero-shot users with no behavior in the
target domain. We deploy MeKB-Rec in WeiXin recommendation scenarios and
achieve significant gains in core online metrics. MeKB-Rec is now serving
hundreds of millions of users in real-world products. | [
"Xin Su",
"Yao Zhou",
"Zifei Shan",
"Qian Chen"
] | 2023-10-17 09:13:24 | http://arxiv.org/abs/2310.11088v1 | http://arxiv.org/pdf/2310.11088v1 | 2310.11088v1 |
Feature Pyramid biLSTM: Using Smartphone Sensors for Transportation Mode Detection | The widespread utilization of smartphones has provided extensive availability
to Inertial Measurement Units, providing a wide range of sensory data that can
be advantageous for the detection of transportation modes. The objective of
this study is to propose a novel end-to-end approach to effectively explore a
reduced amount of sensory data collected from a smartphone to achieve accurate
mode detection in common daily traveling activities. Our approach, called
Feature Pyramid biLSTM (FPbiLSTM), is characterized by its ability to reduce
the number of sensors required and processing demands, resulting in a more
efficient modeling process without sacrificing the quality of the outcomes than
the other current models. FPbiLSTM extends an existing CNN biLSTM model with
the Feature Pyramid Network, leveraging the advantages of both shallow layer
richness and deeper layer feature resilience for capturing temporal moving
patterns in various transportation modes. It exhibits an excellent performance
by employing the data collected from only three out of seven sensors, i.e.
accelerometers, gyroscopes, and magnetometers, in the 2018 Sussex-Huawei
Locomotion (SHL) challenge dataset, attaining a noteworthy accuracy of 95.1%
and an F1-score of 94.7% in detecting eight different transportation modes. | [
"Qinrui Tang",
"Hao Cheng"
] | 2023-10-17 09:13:10 | http://arxiv.org/abs/2310.11087v1 | http://arxiv.org/pdf/2310.11087v1 | 2310.11087v1 |
Data Drift Monitoring for Log Anomaly Detection Pipelines | Logs enable the monitoring of infrastructure status and the performance of
associated applications. Logs are also invaluable for diagnosing the root
causes of any problems that may arise. Log Anomaly Detection (LAD) pipelines
automate the detection of anomalies in logs, providing assistance to site
reliability engineers (SREs) in system diagnosis. Log patterns change over
time, necessitating updates to the LAD model defining the `normal' log activity
profile. In this paper, we introduce a Bayes Factor-based drift detection
method that identifies when intervention, retraining, and updating of the LAD
model are required with human involvement. We illustrate our method using
sequences of log activity, both from unaltered data, and simulated activity
with controlled levels of anomaly contamination, based on real collected log
data. | [
"Dipak Wani",
"Samuel Ackerman",
"Eitan Farchi",
"Xiaotong Liu",
"Hau-wen Chang",
"Sarasi Lalithsena"
] | 2023-10-17 09:10:40 | http://arxiv.org/abs/2310.14893v1 | http://arxiv.org/pdf/2310.14893v1 | 2310.14893v1 |
In-Context Few-Shot Relation Extraction via Pre-Trained Language Models | Relation extraction aims at inferring structured human knowledge from textual
documents. State-of-the-art methods based on language models commonly have two
limitations: (1) they require named entities to be either given as input or
infer them, which introduces additional noise, and (2) they require human
annotations of documents. As a remedy, we present a novel framework for
in-context few-shot relation extraction via pre-trained language models. To the
best of our knowledge, we are the first to reformulate the relation extraction
task as a tailored in-context few-shot learning paradigm. Thereby, we achieve
crucial benefits in that we eliminate the need for both named entity
recognition and human annotation of documents. Unlike existing methods based on
fine-tuning, our framework is flexible in that it can be easily updated for a
new set of relations without re-training. We evaluate our framework using
DocRED, the largest publicly available dataset for document-level relation
extraction, and demonstrate that our framework achieves state-of-the-art
performance. Finally, our framework allows us to identify missing annotations,
and we thus show that our framework actually performs much better than the
original labels from the development set of DocRED. | [
"Yilmazcan Ozyurt",
"Stefan Feuerriegel",
"Ce Zhang"
] | 2023-10-17 09:10:27 | http://arxiv.org/abs/2310.11085v1 | http://arxiv.org/pdf/2310.11085v1 | 2310.11085v1 |
CSG: Curriculum Representation Learning for Signed Graph | Signed graphs are valuable for modeling complex relationships with positive
and negative connections, and Signed Graph Neural Networks (SGNNs) have become
crucial tools for their analysis. However, prior to our work, no specific
training plan existed for SGNNs, and the conventional random sampling approach
did not address varying learning difficulties within the graph's structure. We
proposed a curriculum-based training approach, where samples progress from easy
to complex, inspired by human learning. To measure learning difficulty, we
introduced a lightweight mechanism and created the Curriculum representation
learning framework for Signed Graphs (CSG). This framework optimizes the order
in which samples are presented to the SGNN model. Empirical validation across
six real-world datasets showed impressive results, enhancing SGNN model
accuracy by up to 23.7% in link sign prediction (AUC) and significantly
improving stability with an up to 8.4 reduction in the standard deviation of
AUC scores. | [
"Zeyu Zhang",
"Jiamou Liu",
"Kaiqi Zhao",
"Yifei Wang",
"Pengqian Han",
"Xianda Zheng",
"Qiqi Wang",
"Zijian Zhang"
] | 2023-10-17 09:08:33 | http://arxiv.org/abs/2310.11083v1 | http://arxiv.org/pdf/2310.11083v1 | 2310.11083v1 |
Multi-omics Sampling-based Graph Transformer for Synthetic Lethality Prediction | Synthetic lethality (SL) prediction is used to identify if the co-mutation of
two genes results in cell death. The prevalent strategy is to abstract SL
prediction as an edge classification task on gene nodes within SL data and
achieve it through graph neural networks (GNNs). However, GNNs suffer from
limitations in their message passing mechanisms, including over-smoothing and
over-squashing issues. Moreover, harnessing the information of non-SL gene
relationships within large-scale multi-omics data to facilitate SL prediction
poses a non-trivial challenge. To tackle these issues, we propose a new
multi-omics sampling-based graph transformer for SL prediction (MSGT-SL).
Concretely, we introduce a shallow multi-view GNN to acquire local structural
patterns from both SL and multi-omics data. Further, we input gene features
that encode multi-view information into the standard self-attention to capture
long-range dependencies. Notably, starting with batch genes from SL data, we
adopt parallel random walk sampling across multiple omics gene graphs
encompassing them. Such sampling effectively and modestly incorporates genes
from omics in a structure-aware manner before using self-attention. We showcase
the effectiveness of MSGT-SL on real-world SL tasks, demonstrating the
empirical benefits gained from the graph transformer and multi-omics data. | [
"Xusheng Zhao",
"Hao Liu",
"Qiong Dai",
"Hao Peng",
"Xu Bai",
"Huailiang Peng"
] | 2023-10-17 09:06:41 | http://arxiv.org/abs/2310.11082v1 | http://arxiv.org/pdf/2310.11082v1 | 2310.11082v1 |
United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit | Deep neural networks have become the method of choice for solving many image
classification tasks, largely because they can fit very complex functions
defined over raw images. The downside of such powerful learners is the danger
of overfitting the training set, leading to poor generalization, which is
usually avoided by regularization and "early stopping" of the training. In this
paper, we propose a new deep network ensemble classifier that is very effective
against overfit. We begin with the theoretical analysis of a regression model,
whose predictions - that the variance among classifiers increases when overfit
occurs - is demonstrated empirically in deep networks in common use. Guided by
these results, we construct a new ensemble-based prediction method designed to
combat overfit, where the prediction is determined by the most consensual
prediction throughout the training. On multiple image and text classification
datasets, we show that when regular ensembles suffer from overfit, our method
eliminates the harmful reduction in generalization due to overfit, and often
even surpasses the performance obtained by early stopping. Our method is easy
to implement, and can be integrated with any training scheme and architecture,
without additional prior knowledge beyond the training set. Accordingly, it is
a practical and useful tool to overcome overfit. | [
"Uri Stern",
"Daniel Shwartz",
"Daphna Weinshall"
] | 2023-10-17 08:51:44 | http://arxiv.org/abs/2310.11077v1 | http://arxiv.org/pdf/2310.11077v1 | 2310.11077v1 |
Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification | Stochastic gradient descent (SGD) or stochastic approximation has been widely
used in model training and stochastic optimization. While there is a huge
literature on analyzing its convergence, inference on the obtained solutions
from SGD has only been recently studied, yet is important due to the growing
need for uncertainty quantification. We investigate two computationally cheap
resampling-based methods to construct confidence intervals for SGD solutions.
One uses multiple, but few, SGDs in parallel via resampling with replacement
from the data, and another operates this in an online fashion. Our methods can
be regarded as enhancements of established bootstrap schemes to substantially
reduce the computation effort in terms of resampling requirements, while at the
same time bypassing the intricate mixing conditions in existing batching
methods. We achieve these via a recent so-called cheap bootstrap idea and
Berry-Esseen-type bound for SGD. | [
"Henry Lam",
"Zitong Wang"
] | 2023-10-17 08:18:10 | http://arxiv.org/abs/2310.11065v1 | http://arxiv.org/pdf/2310.11065v1 | 2310.11065v1 |
Locally Differentially Private Graph Embedding | Graph embedding has been demonstrated to be a powerful tool for learning
latent representations for nodes in a graph. However, despite its superior
performance in various graph-based machine learning tasks, learning over graphs
can raise significant privacy concerns when graph data involves sensitive
information. To address this, in this paper, we investigate the problem of
developing graph embedding algorithms that satisfy local differential privacy
(LDP). We propose LDP-GE, a novel privacy-preserving graph embedding framework,
to protect the privacy of node data. Specifically, we propose an LDP mechanism
to obfuscate node data and adopt personalized PageRank as the proximity measure
to learn node representations. Then, we theoretically analyze the privacy
guarantees and utility of the LDP-GE framework. Extensive experiments conducted
over several real-world graph datasets demonstrate that LDP-GE achieves
favorable privacy-utility trade-offs and significantly outperforms existing
approaches in both node classification and link prediction tasks. | [
"Zening Li",
"Rong-Hua Li",
"Meihao Liao",
"Fusheng Jin",
"Guoren Wang"
] | 2023-10-17 08:06:08 | http://arxiv.org/abs/2310.11060v1 | http://arxiv.org/pdf/2310.11060v1 | 2310.11060v1 |
Causal Feature Selection via Transfer Entropy | Machine learning algorithms are designed to capture complex relationships
between features. In this context, the high dimensionality of data often
results in poor model performance, with the risk of overfitting. Feature
selection, the process of selecting a subset of relevant and non-redundant
features, is, therefore, an essential step to mitigate these issues. However,
classical feature selection approaches do not inspect the causal relationship
between selected features and target, which can lead to misleading results in
real-world applications. Causal discovery, instead, aims to identify causal
relationships between features with observational data. In this paper, we
propose a novel methodology at the intersection between feature selection and
causal discovery, focusing on time series. We introduce a new causal feature
selection approach that relies on the forward and backward feature selection
procedures and leverages transfer entropy to estimate the causal flow of
information from the features to the target in time series. Our approach
enables the selection of features not only in terms of mere model performance
but also captures the causal information flow. In this context, we provide
theoretical guarantees on the regression and classification errors for both the
exact and the finite-sample cases. Finally, we present numerical validations on
synthetic and real-world regression problems, showing results competitive
w.r.t. the considered baselines. | [
"Paolo Bonetti",
"Alberto Maria Metelli",
"Marcello Restelli"
] | 2023-10-17 08:04:45 | http://arxiv.org/abs/2310.11059v1 | http://arxiv.org/pdf/2310.11059v1 | 2310.11059v1 |
Robust-MBFD: A Robust Deep Learning System for Motor Bearing Faults Detection Using Multiple Deep Learning Training Strategies and A Novel Double Loss Function | This paper presents a comprehensive analysis of motor bearing fault detection
(MBFD), which involves the task of identifying faults in a motor bearing based
on its vibration. To this end, we first propose and evaluate various machine
learning based systems for the MBFD task. Furthermore, we propose three deep
learning based systems for the MBFD task, each of which explores one of the
following training strategies: supervised learning, semi-supervised learning,
and unsupervised learning. The proposed machine learning based systems and deep
learning based systems are evaluated, compared, and then they are used to
identify the best model for the MBFD task. We conducted extensive experiments
on various benchmark datasets of motor bearing faults, including those from the
American Society for Mechanical Failure Prevention Technology (MFPT), Case
Western Reserve University Bearing Center (CWRU), and the Condition Monitoring
of Bearing Damage in Electromechanical Drive Systems from Paderborn University
(PU). The experimental results on different datasets highlight two main
contributions of this study. First, we prove that deep learning based systems
are more effective than machine learning based systems for the MBFD task.
Second, we achieve a robust and general deep learning based system with a novel
loss function for the MBFD task on several benchmark datasets, demonstrating
its potential for real-life MBFD applications. | [
"Khoa Tran",
"Lam Pham",
"Hai-Canh Vu"
] | 2023-10-17 07:50:52 | http://arxiv.org/abs/2310.11477v1 | http://arxiv.org/pdf/2310.11477v1 | 2310.11477v1 |
Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation | This paper describes our submission to the SemEval-2023 for Task 6 on
LegalEval: Understanding Legal Texts. Our submission concentrated on three
subtasks: Legal Named Entity Recognition (L-NER) for Task-B, Legal Judgment
Prediction (LJP) for Task-C1, and Court Judgment Prediction with Explanation
(CJPE) for Task-C2. We conducted various experiments on these subtasks and
presented the results in detail, including data statistics and methodology. It
is worth noting that legal tasks, such as those tackled in this research, have
been gaining importance due to the increasing need to automate legal analysis
and support. Our team obtained competitive rankings of 15$^{th}$, 11$^{th}$,
and 1$^{st}$ in Task-B, Task-C1, and Task-C2, respectively, as reported on the
leaderboard. | [
"Shubham Kumar Nigam",
"Aniket Deroy",
"Noel Shallum",
"Ayush Kumar Mishra",
"Anup Roy",
"Shubham Kumar Mishra",
"Arnab Bhattacharya",
"Saptarshi Ghosh",
"Kripabandhu Ghosh"
] | 2023-10-17 07:35:11 | http://arxiv.org/abs/2310.11049v1 | http://arxiv.org/pdf/2310.11049v1 | 2310.11049v1 |
Understanding Contrastive Learning via Distributionally Robust Optimization | This study reveals the inherent tolerance of contrastive learning (CL)
towards sampling bias, wherein negative samples may encompass similar semantics
(\eg labels). However, existing theories fall short in providing explanations
for this phenomenon. We bridge this research gap by analyzing CL through the
lens of distributionally robust optimization (DRO), yielding several key
insights: (1) CL essentially conducts DRO over the negative sampling
distribution, thus enabling robust performance across a variety of potential
distributions and demonstrating robustness to sampling bias; (2) The design of
the temperature $\tau$ is not merely heuristic but acts as a Lagrange
Coefficient, regulating the size of the potential distribution set; (3) A
theoretical connection is established between DRO and mutual information, thus
presenting fresh evidence for ``InfoNCE as an estimate of MI'' and a new
estimation approach for $\phi$-divergence-based generalized mutual information.
We also identify CL's potential shortcomings, including over-conservatism and
sensitivity to outliers, and introduce a novel Adjusted InfoNCE loss (ADNCE) to
mitigate these issues. It refines potential distribution, improving performance
and accelerating convergence. Extensive experiments on various domains (image,
sentence, and graphs) validate the effectiveness of the proposal. The code is
available at \url{https://github.com/junkangwu/ADNCE}. | [
"Junkang Wu",
"Jiawei Chen",
"Jiancan Wu",
"Wentao Shi",
"Xiang Wang",
"Xiangnan He"
] | 2023-10-17 07:32:59 | http://arxiv.org/abs/2310.11048v1 | http://arxiv.org/pdf/2310.11048v1 | 2310.11048v1 |
Fast Graph Condensation with Structure-based Neural Tangent Kernel | The rapid development of Internet technology has given rise to a vast amount
of graph-structured data. Graph Neural Networks (GNNs), as an effective method
for various graph mining tasks, incurs substantial computational resource costs
when dealing with large-scale graph data. A data-centric manner solution is
proposed to condense the large graph dataset into a smaller one without
sacrificing the predictive performance of GNNs. However, existing efforts
condense graph-structured data through a computational intensive bi-level
optimization architecture also suffer from massive computation costs. In this
paper, we propose reforming the graph condensation problem as a Kernel Ridge
Regression (KRR) task instead of iteratively training GNNs in the inner loop of
bi-level optimization. More specifically, We propose a novel dataset
condensation framework (GC-SNTK) for graph-structured data, where a
Structure-based Neural Tangent Kernel (SNTK) is developed to capture the
topology of graph and serves as the kernel function in KRR paradigm.
Comprehensive experiments demonstrate the effectiveness of our proposed model
in accelerating graph condensation while maintaining high prediction
performance. | [
"Lin Wang",
"Wenqi Fan",
"Jiatong Li",
"Yao Ma",
"Qing Li"
] | 2023-10-17 07:25:59 | http://arxiv.org/abs/2310.11046v1 | http://arxiv.org/pdf/2310.11046v1 | 2310.11046v1 |
Matrix Compression via Randomized Low Rank and Low Precision Factorization | Matrices are exceptionally useful in various fields of study as they provide
a convenient framework to organize and manipulate data in a structured manner.
However, modern matrices can involve billions of elements, making their storage
and processing quite demanding in terms of computational resources and memory
usage. Although prohibitively large, such matrices are often approximately low
rank. We propose an algorithm that exploits this structure to obtain a low rank
decomposition of any matrix $\mathbf{A}$ as $\mathbf{A} \approx
\mathbf{L}\mathbf{R}$, where $\mathbf{L}$ and $\mathbf{R}$ are the low rank
factors. The total number of elements in $\mathbf{L}$ and $\mathbf{R}$ can be
significantly less than that in $\mathbf{A}$. Furthermore, the entries of
$\mathbf{L}$ and $\mathbf{R}$ are quantized to low precision formats $--$
compressing $\mathbf{A}$ by giving us a low rank and low precision
factorization. Our algorithm first computes an approximate basis of the range
space of $\mathbf{A}$ by randomly sketching its columns, followed by a
quantization of the vectors constituting this basis. It then computes
approximate projections of the columns of $\mathbf{A}$ onto this quantized
basis. We derive upper bounds on the approximation error of our algorithm, and
analyze the impact of target rank and quantization bit-budget. The tradeoff
between compression ratio and approximation accuracy allows for flexibility in
choosing these parameters based on specific application requirements. We
empirically demonstrate the efficacy of our algorithm in image compression,
nearest neighbor classification of image and text embeddings, and compressing
the layers of LlaMa-$7$b. Our results illustrate that we can achieve
compression ratios as aggressive as one bit per matrix coordinate, all while
surpassing or maintaining the performance of traditional compression
techniques. | [
"Rajarshi Saha",
"Varun Srivastava",
"Mert Pilanci"
] | 2023-10-17 06:56:57 | http://arxiv.org/abs/2310.11028v1 | http://arxiv.org/pdf/2310.11028v1 | 2310.11028v1 |
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning | The emerging graph Transformers have achieved impressive performance for
graph representation learning over graph neural networks (GNNs). In this work,
we regard the self-attention mechanism, the core module of graph Transformers,
as a two-step aggregation operation on a fully connected graph. Due to the
property of generating positive attention values, the self-attention mechanism
is equal to conducting a smooth operation on all nodes, preserving the
low-frequency information. However, only capturing the low-frequency
information is inefficient in learning complex relations of nodes on diverse
graphs, such as heterophily graphs where the high-frequency information is
crucial. To this end, we propose a Signed Attention-based Graph Transformer
(SignGT) to adaptively capture various frequency information from the graphs.
Specifically, SignGT develops a new signed self-attention mechanism (SignSA)
that produces signed attention values according to the semantic relevance of
node pairs. Hence, the diverse frequency information between different node
pairs could be carefully preserved. Besides, SignGT proposes a structure-aware
feed-forward network (SFFN) that introduces the neighborhood bias to preserve
the local topology information. In this way, SignGT could learn informative
node representations from both long-range dependencies and local topology
information. Extensive empirical results on both node-level and graph-level
tasks indicate the superiority of SignGT against state-of-the-art graph
Transformers as well as advanced GNNs. | [
"Jinsong Chen",
"Gaichao Li",
"John E. Hopcroft",
"Kun He"
] | 2023-10-17 06:42:11 | http://arxiv.org/abs/2310.11025v1 | http://arxiv.org/pdf/2310.11025v1 | 2310.11025v1 |
Compatible Transformer for Irregularly Sampled Multivariate Time Series | To analyze multivariate time series, most previous methods assume regular
subsampling of time series, where the interval between adjacent measurements
and the number of samples remain unchanged. Practically, data collection
systems could produce irregularly sampled time series due to sensor failures
and interventions. However, existing methods designed for regularly sampled
multivariate time series cannot directly handle irregularity owing to
misalignment along both temporal and variate dimensions. To fill this gap, we
propose Compatible Transformer (CoFormer), a transformer-based encoder to
achieve comprehensive temporal-interaction feature learning for each individual
sample in irregular multivariate time series. In CoFormer, we view each sample
as a unique variate-time point and leverage intra-variate/inter-variate
attentions to learn sample-wise temporal/interaction features based on
intra-variate/inter-variate neighbors. With CoFormer as the core, we can
analyze irregularly sampled multivariate time series for many downstream tasks,
including classification and prediction. We conduct extensive experiments on 3
real-world datasets and validate that the proposed CoFormer significantly and
consistently outperforms existing methods. | [
"Yuxi Wei",
"Juntong Peng",
"Tong He",
"Chenxin Xu",
"Jian Zhang",
"Shirui Pan",
"Siheng Chen"
] | 2023-10-17 06:29:09 | http://arxiv.org/abs/2310.11022v1 | http://arxiv.org/pdf/2310.11022v1 | 2310.11022v1 |
Pure Exploration in Asynchronous Federated Bandits | We study the federated pure exploration problem of multi-armed bandits and
linear bandits, where $M$ agents cooperatively identify the best arm via
communicating with the central server. To enhance the robustness against
latency and unavailability of agents that are common in practice, we propose
the first federated asynchronous multi-armed bandit and linear bandit
algorithms for pure exploration with fixed confidence. Our theoretical analysis
shows the proposed algorithms achieve near-optimal sample complexities and
efficient communication costs in a fully asynchronous environment. Moreover,
experimental results based on synthetic and real-world data empirically
elucidate the effectiveness and communication cost-efficiency of the proposed
algorithms. | [
"Zichen Wang",
"Chuanhao Li",
"Chenyu Song",
"Lianghui Wang",
"Quanquan Gu",
"Huazheng Wang"
] | 2023-10-17 06:04:00 | http://arxiv.org/abs/2310.11015v1 | http://arxiv.org/pdf/2310.11015v1 | 2310.11015v1 |
Hyperspectral In-Memory Computing with Optical Frequency Combs and Programmable Optical Memories | The rapid advancements in machine learning across numerous industries have
amplified the demand for extensive matrix-vector multiplication operations,
thereby challenging the capacities of traditional von Neumann computing
architectures. To address this, researchers are currently exploring
alternatives such as in-memory computing systems to develop faster and more
energy-efficient hardware. In particular, there is renewed interest in
computing systems based on optics, which could potentially handle matrix-vector
multiplication in a more energy-efficient way. Despite promising initial
results, developing a highly parallel, programmable, and scalable optical
computing system capable of rivaling electronic computing hardware still
remains elusive. In this context, we propose a hyperspectral in-memory
computing architecture that integrates space multiplexing with frequency
multiplexing of optical frequency combs and uses spatial light modulators as a
programmable optical memory, thereby boosting the computational throughput and
the energy efficiency. We have experimentally demonstrated multiply-accumulate
operations with higher than 4-bit precision in both matrix-vector and
matrix-matrix multiplications, which suggests the system's potential for a wide
variety of deep learning and optimization tasks. This system exhibits
extraordinary modularity, scalability, and programmability, effectively
transcending the traditional limitations of optics-based computing
architectures. Our approach demonstrates the potential to scale beyond peta
operations per second, marking a significant step towards achieving
high-throughput energy-efficient optical computing. | [
"Mostafa Honari Latifpour",
"Byoung Jun Park",
"Yoshihisa Yamamoto",
"Myoung-Gyun Suh"
] | 2023-10-17 06:03:45 | http://arxiv.org/abs/2310.11014v1 | http://arxiv.org/pdf/2310.11014v1 | 2310.11014v1 |
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling | Deep generative models have shown tremendous success in data density
estimation and data generation from finite samples. While these models have
shown impressive performance by learning correlations among features in the
data, some fundamental shortcomings are their lack of explainability, the
tendency to induce spurious correlations, and poor out-of-distribution
extrapolation. In an effort to remedy such challenges, one can incorporate the
theory of causality in deep generative modeling. Structural causal models
(SCMs) describe data-generating processes and model complex causal
relationships and mechanisms among variables in a system. Thus, SCMs can
naturally be combined with deep generative models. Causal models offer several
beneficial properties to deep generative models, such as distribution shift
robustness, fairness, and interoperability. We provide a technical survey on
causal generative modeling categorized into causal representation learning and
controllable counterfactual generation methods. We focus on fundamental theory,
formulations, drawbacks, datasets, metrics, and applications of causal
generative models in fairness, privacy, out-of-distribution generalization, and
precision medicine. We also discuss open problems and fruitful research
directions for future work in the field. | [
"Aneesh Komanduri",
"Xintao Wu",
"Yongkai Wu",
"Feng Chen"
] | 2023-10-17 05:45:32 | http://arxiv.org/abs/2310.11011v1 | http://arxiv.org/pdf/2310.11011v1 | 2310.11011v1 |
Adaptive Pairwise Encodings for Link Prediction | Link prediction is a common task on graph-structured data that has seen
applications in a variety of domains. Classically, hand-crafted heuristics were
used for this task. Heuristic measures are chosen such that they correlate well
with the underlying factors related to link formation. In recent years, a new
class of methods has emerged that combines the advantages of message-passing
neural networks (MPNN) and heuristics methods. These methods perform
predictions by using the output of an MPNN in conjunction with a "pairwise
encoding" that captures the relationship between nodes in the candidate link.
They have been shown to achieve strong performance on numerous datasets.
However, current pairwise encodings often contain a strong inductive bias,
using the same underlying factors to classify all links. This limits the
ability of existing methods to learn how to properly classify a variety of
different links that may form from different factors. To address this
limitation, we propose a new method, LPFormer, which attempts to adaptively
learn the pairwise encodings for each link. LPFormer models the link factors
via an attention module that learns the pairwise encoding that exists between
nodes by modeling multiple factors integral to link prediction. Extensive
experiments demonstrate that LPFormer can achieve SOTA performance on numerous
datasets while maintaining efficiency. | [
"Harry Shomer",
"Yao Ma",
"Haitao Mao",
"Juanhui Li",
"Bo Wu",
"Jiliang Tang"
] | 2023-10-17 05:36:46 | http://arxiv.org/abs/2310.11009v2 | http://arxiv.org/pdf/2310.11009v2 | 2310.11009v2 |
Correction Focused Language Model Training for Speech Recognition | Language models (LMs) have been commonly adopted to boost the performance of
automatic speech recognition (ASR) particularly in domain adaptation tasks.
Conventional way of LM training treats all the words in corpora equally,
resulting in suboptimal improvements in ASR performance. In this work, we
introduce a novel correction focused LM training approach which aims to
prioritize ASR fallible words. The word-level ASR fallibility score,
representing the likelihood of ASR mis-recognition, is defined and shaped as a
prior word distribution to guide the LM training. To enable correction focused
training with text-only corpora, large language models (LLMs) are employed as
fallibility score predictors and text generators through multi-task
fine-tuning. Experimental results for domain adaptation tasks demonstrate the
effectiveness of our proposed method. Compared with conventional LMs,
correction focused training achieves up to relatively 5.5% word error rate
(WER) reduction in sufficient text scenarios. In insufficient text scenarios,
LM training with LLM-generated text achieves up to relatively 13% WER
reduction, while correction focused training further obtains up to relatively
6% WER reduction. | [
"Yingyi Ma",
"Zhe Liu",
"Ozlem Kalinli"
] | 2023-10-17 05:10:39 | http://arxiv.org/abs/2310.11003v1 | http://arxiv.org/pdf/2310.11003v1 | 2310.11003v1 |
Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques | Accurate and timely hyperlocal weather predictions are essential for various
applications, ranging from agriculture to disaster management. In this paper,
we propose a novel approach that combines hyperlocal weather prediction and
anomaly detection using IoT sensor networks and advanced machine learning
techniques. Our approach leverages data from multiple spatially-distributed yet
relatively close locations and IoT sensors to create high-resolution weather
models capable of predicting short-term, localized weather conditions such as
temperature, pressure, and humidity. By monitoring changes in weather
parameters across these locations, our system is able to enhance the spatial
resolution of predictions and effectively detect anomalies in real-time.
Additionally, our system employs unsupervised learning algorithms to identify
unusual weather patterns, providing timely alerts. Our findings indicate that
this system has the potential to enhance decision-making. | [
"Anita B. Agarwal",
"Rohit Rajesh",
"Nitin Arul"
] | 2023-10-17 05:04:53 | http://arxiv.org/abs/2310.11001v1 | http://arxiv.org/pdf/2310.11001v1 | 2310.11001v1 |
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation | Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse
array of applications. However, the sheer size of large-scale graphs presents a
significant challenge to real-time inference with GNNs. Although existing
Scalable GNNs leverage linear propagation to preprocess the features and
accelerate the training and inference procedure, these methods still suffer
from scalability issues when making inferences on unseen nodes, as the feature
preprocessing requires the graph to be known and fixed. To further accelerate
Scalable GNNs inference in this inductive setting, we propose an online
propagation framework and two novel node-adaptive propagation methods that can
customize the optimal propagation depth for each node based on its topological
information and thereby avoid redundant feature propagation. The trade-off
between accuracy and latency can be flexibly managed through simple
hyper-parameters to accommodate various latency constraints. Moreover, to
compensate for the inference accuracy loss caused by the potential early
termination of propagation, we further propose Inception Distillation to
exploit the multi-scale receptive field information within graphs. The rigorous
and comprehensive experimental study on public datasets with varying scales and
characteristics demonstrates that the proposed inference acceleration framework
outperforms existing state-of-the-art graph inference acceleration methods in
terms of accuracy and efficiency. Particularly, the superiority of our approach
is notable on datasets with larger scales, yielding a 75x inference speedup on
the largest Ogbn-products dataset. | [
"Xinyi Gao",
"Wentao Zhang",
"Junliang Yu",
"Yingxia Shao",
"Quoc Viet Hung Nguyen",
"Bin Cui",
"Hongzhi Yin"
] | 2023-10-17 05:03:00 | http://arxiv.org/abs/2310.10998v1 | http://arxiv.org/pdf/2310.10998v1 | 2310.10998v1 |
Program Translation via Code Distillation | Software version migration and program translation are an important and
costly part of the lifecycle of large codebases. Traditional machine
translation relies on parallel corpora for supervised translation, which is not
feasible for program translation due to a dearth of aligned data. Recent
unsupervised neural machine translation techniques have overcome data
limitations by included techniques such as back translation and low level
compiler intermediate representations (IR). These methods face significant
challenges due to the noise in code snippet alignment and the diversity of IRs
respectively. In this paper we propose a novel model called Code Distillation
(CoDist) whereby we capture the semantic and structural equivalence of code in
a language agnostic intermediate representation. Distilled code serves as a
translation pivot for any programming language, leading by construction to
parallel corpora which scale to all available source code by simply applying
the distillation compiler. We demonstrate that our approach achieves
state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks
translation benchmarks, with an average absolute increase of 12.7% on the
TransCoder GeeksforGeeks translation benchmark compare to TransCoder-ST. | [
"Yufan Huang",
"Mengnan Qi",
"Yongqiang Yao",
"Maoquan Wang",
"Bin Gu",
"Colin Clement",
"Neel Sundaresan"
] | 2023-10-17 04:59:15 | http://arxiv.org/abs/2310.11476v1 | http://arxiv.org/pdf/2310.11476v1 | 2310.11476v1 |
Why Do Students Drop Out? University Dropout Prediction and Associated Factor Analysis Using Machine Learning Techniques | Graduation and dropout rates have always been a serious consideration for
educational institutions and students. High dropout rates negatively impact
both the lives of individual students and institutions. To address this
problem, this study examined university dropout prediction using academic,
demographic, socioeconomic, and macroeconomic data types. Additionally, we
performed associated factor analysis to analyze which type of data would be
most influential on the performance of machine learning models in predicting
graduation and dropout status. These features were used to train four binary
classifiers to determine if students would graduate or drop out. The overall
performance of the classifiers in predicting dropout status had an average
ROC-AUC score of 0.935. The data type most influential to the model performance
was found to be academic data, with the average ROC-AUC score dropping from
0.935 to 0.811 when excluding all academic-related features from the data set.
Preliminary results indicate that a correlation does exist between data types
and dropout status. | [
"Sean Kim",
"Eliot Yoo",
"Samuel Kim"
] | 2023-10-17 04:20:00 | http://arxiv.org/abs/2310.10987v1 | http://arxiv.org/pdf/2310.10987v1 | 2310.10987v1 |
Exact nonlinear state estimation | The majority of data assimilation (DA) methods in the geosciences are based
on Gaussian assumptions. While these assumptions facilitate efficient
algorithms, they cause analysis biases and subsequent forecast degradations.
Non-parametric, particle-based DA algorithms have superior accuracy, but their
application to high-dimensional models still poses operational challenges.
Drawing inspiration from recent advances in the field of generative artificial
intelligence (AI), this article introduces a new nonlinear estimation theory
which attempts to bridge the existing gap in DA methodology. Specifically, a
Conjugate Transform Filter (CTF) is derived and shown to generalize the
celebrated Kalman filter to arbitrarily non-Gaussian distributions. The new
filter has several desirable properties, such as its ability to preserve
statistical relationships in the prior state and convergence to highly accurate
observations. An ensemble approximation of the new theory (ECTF) is also
presented and validated using idealized statistical experiments that feature
bounded quantities with non-Gaussian distributions, a prevalent challenge in
Earth system models. Results from these experiments indicate that the greatest
benefits from ECTF occur when observation errors are small relative to the
forecast uncertainty and when state variables exhibit strong nonlinear
dependencies. Ultimately, the new filtering theory offers exciting avenues for
improving conventional DA algorithms through their principled integration with
AI techniques. | [
"Hristo G. Chipilski"
] | 2023-10-17 03:44:29 | http://arxiv.org/abs/2310.10976v1 | http://arxiv.org/pdf/2310.10976v1 | 2310.10976v1 |
Context-Aware Meta-Learning | Large Language Models like ChatGPT demonstrate a remarkable capacity to learn
new concepts during inference without any fine-tuning. However, visual models
trained to detect new objects during inference have been unable to replicate
this ability, and instead either perform poorly or require meta-training and/or
fine-tuning on similar objects. In this work, we propose a meta-learning
algorithm that emulates Large Language Models by learning new visual concepts
during inference without fine-tuning. Our approach leverages a frozen
pre-trained feature extractor, and analogous to in-context learning, recasts
meta-learning as sequence modeling over datapoints with known labels and a test
datapoint with an unknown label. On 8 out of 11 meta-learning benchmarks, our
approach -- without meta-training or fine-tuning -- exceeds or matches the
state-of-the-art algorithm, P>M>F, which is meta-trained on these benchmarks. | [
"Christopher Fifty",
"Dennis Duan",
"Ronald G. Junkins",
"Ehsan Amid",
"Jure Leskovec",
"Christopher Ré",
"Sebastian Thrun"
] | 2023-10-17 03:35:27 | http://arxiv.org/abs/2310.10971v1 | http://arxiv.org/pdf/2310.10971v1 | 2310.10971v1 |
SD-PINN: Deep Learning based Spatially Dependent PDEs Recovery | The physics-informed neural network (PINN) is capable of recovering partial
differential equation (PDE) coefficients that remain constant throughout the
spatial domain directly from physical measurements. In this work, we propose a
spatially dependent physics-informed neural network (SD-PINN), which enables
the recovery of coefficients in spatially-dependent PDEs using a single neural
network, eliminating the requirement for domain-specific physical expertise.
The proposed method exhibits robustness to noise owing to the incorporation of
physical constraints. It can also incorporate the low-rank assumption of the
spatial variation for the PDE coefficients to recover the coefficients at
locations without available measurements. | [
"Ruixian Liu",
"Peter Gerstoft"
] | 2023-10-17 03:31:47 | http://arxiv.org/abs/2310.10970v1 | http://arxiv.org/pdf/2310.10970v1 | 2310.10970v1 |
The neural network models with delays for solving absolute value equations | An inverse-free neural network model with mixed delays is proposed for
solving the absolute value equation (AVE) $Ax -|x| - b =0$, which includes an
inverse-free neural network model with discrete delay as a special case. By
using the Lyapunov-Krasovskii theory and the linear matrix inequality (LMI)
method, the developed neural network models are proved to be exponentially
convergent to the solution of the AVE. Compared with the existing neural
network models for solving the AVE, the proposed models feature the ability of
solving a class of AVE with $\|A^{-1}\|>1$. Numerical simulations are given to
show the effectiveness of the two delayed neural network models. | [
"Dongmei Yu",
"Gehao Zhang",
"Cairong Chen",
"Deren Han"
] | 2023-10-17 03:26:35 | http://arxiv.org/abs/2310.10965v1 | http://arxiv.org/pdf/2310.10965v1 | 2310.10965v1 |
Enhancing Deep Neural Network Training Efficiency and Performance through Linear Prediction | Deep neural networks (DNN) have achieved remarkable success in various
fields, including computer vision and natural language processing. However,
training an effective DNN model still poses challenges. This paper aims to
propose a method to optimize the training effectiveness of DNN, with the goal
of improving model performance. Firstly, based on the observation that the DNN
parameters change in certain laws during training process, the potential of
parameter prediction for improving model training efficiency and performance is
discovered. Secondly, considering the magnitude of DNN model parameters,
hardware limitations and characteristics of Stochastic Gradient Descent (SGD)
for noise tolerance, a Parameter Linear Prediction (PLP) method is exploit to
perform DNN parameter prediction. Finally, validations are carried out on some
representative backbones. Experiment results show that compare to the normal
training ways, under the same training conditions and epochs, by employing
proposed PLP method, the optimal model is able to obtain average about 1%
accuracy improvement and 0.01 top-1/top-5 error reduction for Vgg16, Resnet18
and GoogLeNet based on CIFAR-100 dataset, which shown the effectiveness of the
proposed method on different DNN structures, and validated its capacity in
enhancing DNN training efficiency and performance. | [
"Hejie Ying",
"Mengmeng Song",
"Yaohong Tang",
"Shungen Xiao",
"Zimin Xiao"
] | 2023-10-17 03:11:30 | http://arxiv.org/abs/2310.10958v1 | http://arxiv.org/pdf/2310.10958v1 | 2310.10958v1 |
A State-Vector Framework for Dataset Effects | The impressive success of recent deep neural network (DNN)-based systems is
significantly influenced by the high-quality datasets used in training.
However, the effects of the datasets, especially how they interact with each
other, remain underexplored. We propose a state-vector framework to enable
rigorous studies in this direction. This framework uses idealized probing test
results as the bases of a vector space. This framework allows us to quantify
the effects of both standalone and interacting datasets. We show that the
significant effects of some commonly-used language understanding datasets are
characteristic and are concentrated on a few linguistic dimensions.
Additionally, we observe some ``spill-over'' effects: the datasets could impact
the models along dimensions that may seem unrelated to the intended tasks. Our
state-vector framework paves the way for a systematic understanding of the
dataset effects, a crucial component in responsible and robust model
development. | [
"Esmat Sahak",
"Zining Zhu",
"Frank Rudzicz"
] | 2023-10-17 03:05:06 | http://arxiv.org/abs/2310.10955v1 | http://arxiv.org/pdf/2310.10955v1 | 2310.10955v1 |
A Local Graph Limits Perspective on Sampling-Based GNNs | We propose a theoretical framework for training Graph Neural Networks (GNNs)
on large input graphs via training on small, fixed-size sampled subgraphs. This
framework is applicable to a wide range of models, including popular
sampling-based GNNs, such as GraphSAGE and FastGCN. Leveraging the theory of
graph local limits, we prove that, under mild assumptions, parameters learned
from training sampling-based GNNs on small samples of a large input graph are
within an $\epsilon$-neighborhood of the outcome of training the same
architecture on the whole graph. We derive bounds on the number of samples, the
size of the graph, and the training steps required as a function of $\epsilon$.
Our results give a novel theoretical understanding for using sampling in
training GNNs. They also suggest that by training GNNs on small samples of the
input graph, practitioners can identify and select the best models,
hyperparameters, and sampling algorithms more efficiently. We empirically
illustrate our results on a node classification task on large citation graphs,
observing that sampling-based GNNs trained on local subgraphs 12$\times$
smaller than the original graph achieve comparable performance to those trained
on the input graph. | [
"Yeganeh Alimohammadi",
"Luana Ruiz",
"Amin Saberi"
] | 2023-10-17 02:58:49 | http://arxiv.org/abs/2310.10953v1 | http://arxiv.org/pdf/2310.10953v1 | 2310.10953v1 |
Restricted Tweedie Stochastic Block Models | The stochastic block model (SBM) is a widely used framework for community
detection in networks, where the network structure is typically represented by
an adjacency matrix. However, conventional SBMs are not directly applicable to
an adjacency matrix that consists of non-negative zero-inflated continuous edge
weights. To model the international trading network, where edge weights
represent trading values between countries, we propose an innovative SBM based
on a restricted Tweedie distribution. Additionally, we incorporate nodal
information, such as the geographical distance between countries, and account
for its dynamic effect on edge weights. Notably, we show that given a
sufficiently large number of nodes, estimating this covariate effect becomes
independent of community labels of each node when computing the maximum
likelihood estimator of parameters in our model. This result enables the
development of an efficient two-step algorithm that separates the estimation of
covariate effects from other parameters. We demonstrate the effectiveness of
our proposed method through extensive simulation studies and an application to
real-world international trading data. | [
"Jie Jian",
"Mu Zhu",
"Peijun Sang"
] | 2023-10-17 02:58:03 | http://arxiv.org/abs/2310.10952v1 | http://arxiv.org/pdf/2310.10952v1 | 2310.10952v1 |
Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control | Over the years, reinforcement learning has emerged as a popular approach to
develop signal control and vehicle platooning strategies either independently
or in a hierarchical way. However, jointly controlling both in real-time to
alleviate traffic congestion presents new challenges, such as the inherent
physical and behavioral heterogeneity between signal control and platooning, as
well as coordination between them. This paper proposes an innovative solution
to tackle these challenges based on heterogeneous graph multi-agent
reinforcement learning and traffic theories. Our approach involves: 1)
designing platoon and signal control as distinct reinforcement learning agents
with their own set of observations, actions, and reward functions to optimize
traffic flow; 2) designing coordination by incorporating graph neural networks
within multi-agent reinforcement learning to facilitate seamless information
exchange among agents on a regional scale. We evaluate our approach through
SUMO simulation, which shows a convergent result in terms of various
transportation metrics and better performance over sole signal or platooning
control. | [
"Xianyue Peng",
"Hang Gao",
"Hao Wang",
"H. Michael Zhang"
] | 2023-10-17 02:46:04 | http://arxiv.org/abs/2310.10948v1 | http://arxiv.org/pdf/2310.10948v1 | 2310.10948v1 |
Multi-point Feedback of Bandit Convex Optimization with Hard Constraints | This paper studies bandit convex optimization with constraints, where the
learner aims to generate a sequence of decisions under partial information of
loss functions such that the cumulative loss is reduced as well as the
cumulative constraint violation is simultaneously reduced. We adopt the
cumulative \textit{hard} constraint violation as the metric of constraint
violation, which is defined by $\sum_{t=1}^{T} \max\{g_t(\boldsymbol{x}_t),
0\}$. Owing to the maximum operator, a strictly feasible solution cannot cancel
out the effects of violated constraints compared to the conventional metric
known as \textit{long-term} constraints violation. We present a penalty-based
proximal gradient descent method that attains a sub-linear growth of both
regret and cumulative hard constraint violation, in which the gradient is
estimated with a two-point function evaluation. Precisely, our algorithm
attains $O(d^2T^{\max\{c,1-c\}})$ regret bounds and $O(d^2T^{1-\frac{c}{2}})$
cumulative hard constraint violation bounds for convex loss functions and
time-varying constraints, where $d$ is the dimensionality of the feasible
region and $c\in[\frac{1}{2}, 1)$ is a user-determined parameter. We also
extend the result for the case where the loss functions are strongly convex and
show that both regret and constraint violation bounds can be further reduced. | [
"Yasunari Hikima"
] | 2023-10-17 02:43:22 | http://arxiv.org/abs/2310.10946v1 | http://arxiv.org/pdf/2310.10946v1 | 2310.10946v1 |
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning | A central question in robotics is how to design a control system for an agile
mobile robot. This paper studies this question systematically, focusing on a
challenging setting: autonomous drone racing. We show that a neural network
controller trained with reinforcement learning (RL) outperformed optimal
control (OC) methods in this setting. We then investigated which fundamental
factors have contributed to the success of RL or have limited OC. Our study
indicates that the fundamental advantage of RL over OC is not that it optimizes
its objective better but that it optimizes a better objective. OC decomposes
the problem into planning and control with an explicit intermediate
representation, such as a trajectory, that serves as an interface. This
decomposition limits the range of behaviors that can be expressed by the
controller, leading to inferior control performance when facing unmodeled
effects. In contrast, RL can directly optimize a task-level objective and can
leverage domain randomization to cope with model uncertainty, allowing the
discovery of more robust control responses. Our findings allowed us to push an
agile drone to its maximum performance, achieving a peak acceleration greater
than 12 times the gravitational acceleration and a peak velocity of 108
kilometers per hour. Our policy achieved superhuman control within minutes of
training on a standard workstation. This work presents a milestone in agile
robotics and sheds light on the role of RL and OC in robot control. | [
"Yunlong Song",
"Angel Romero",
"Matthias Mueller",
"Vladlen Koltun",
"Davide Scaramuzza"
] | 2023-10-17 02:40:27 | http://arxiv.org/abs/2310.10943v2 | http://arxiv.org/pdf/2310.10943v2 | 2310.10943v2 |
MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts | Depression is a mental health disorder that has a profound impact on people's
lives. Recent research suggests that signs of depression can be detected in the
way individuals communicate, both through spoken words and written texts. In
particular, social media posts are a rich and convenient text source that we
may examine for depressive symptoms. The Beck Depression Inventory (BDI)
Questionnaire, which is frequently used to gauge the severity of depression, is
one instrument that can aid in this study. We can narrow our study to only
those symptoms since each BDI question is linked to a particular depressive
symptom. It's important to remember that not everyone with depression exhibits
all symptoms at once, but rather a combination of them. Therefore, it is
extremely useful to be able to determine if a sentence or a piece of
user-generated content is pertinent to a certain condition. With this in mind,
the eRisk 2023 Task 1 was designed to do exactly that: assess the relevance of
different sentences to the symptoms of depression as outlined in the BDI
questionnaire. This report is all about how our team, Mason-NLP, participated
in this subtask, which involved identifying sentences related to different
depression symptoms. We used a deep learning approach that incorporated
MentalBERT, RoBERTa, and LSTM. Despite our efforts, the evaluation results were
lower than expected, underscoring the challenges inherent in ranking sentences
from an extensive dataset about depression, which necessitates both appropriate
methodological choices and significant computational resources. We anticipate
that future iterations of this shared task will yield improved results as our
understanding and techniques evolve. | [
"Fardin Ahsan Sakib",
"Ahnaf Atef Choudhury",
"Ozlem Uzuner"
] | 2023-10-17 02:34:34 | http://arxiv.org/abs/2310.10941v1 | http://arxiv.org/pdf/2310.10941v1 | 2310.10941v1 |
Fast and Simple Spectral Clustering in Theory and Practice | Spectral clustering is a popular and effective algorithm designed to find $k$
clusters in a graph $G$. In the classical spectral clustering algorithm, the
vertices of $G$ are embedded into $\mathbb{R}^k$ using $k$ eigenvectors of the
graph Laplacian matrix. However, computing this embedding is computationally
expensive and dominates the running time of the algorithm. In this paper, we
present a simple spectral clustering algorithm based on a vertex embedding with
$O(\log(k))$ vectors computed by the power method. The vertex embedding is
computed in nearly-linear time with respect to the size of the graph, and the
algorithm provably recovers the ground truth clusters under natural assumptions
on the input graph. We evaluate the new algorithm on several synthetic and
real-world datasets, finding that it is significantly faster than alternative
clustering algorithms, while producing results with approximately the same
clustering accuracy. | [
"Peter Macgregor"
] | 2023-10-17 02:31:57 | http://arxiv.org/abs/2310.10939v1 | http://arxiv.org/pdf/2310.10939v1 | 2310.10939v1 |
Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti | As voice assistants cement their place in our technologically advanced
society, there remains a need to cater to the diverse linguistic landscape,
including colloquial forms of low-resource languages. Our study introduces the
first-ever comprehensive dataset for intent detection and slot filling in
formal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samples
across 10 unique intents. Our analysis reveals the robustness of large language
models for tackling downstream tasks with inadequate data. The GPT-3.5 model
achieves an impressive F1 score of 0.94 in intent detection and 0.51 in slot
filling for colloquial Bangla. | [
"Fardin Ahsan Sakib",
"A H M Rezaul Karim",
"Saadat Hasan Khan",
"Md Mushfiqur Rahman"
] | 2023-10-17 02:12:12 | http://arxiv.org/abs/2310.10935v1 | http://arxiv.org/pdf/2310.10935v1 | 2310.10935v1 |
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care | Telehealth is a valuable tool for primary health care (PHC), where depression
is a common condition. PHC is the first point of contact for most people with
depression, but about 25% of diagnoses made by PHC physicians are inaccurate.
Many other barriers also hinder depression detection and treatment in PHC.
Artificial intelligence (AI) may help reduce depression misdiagnosis in PHC and
improve overall diagnosis and treatment outcomes. Telehealth consultations
often have video issues, such as poor connectivity or dropped calls. Audio-only
telehealth is often more practical for lower-income patients who may lack
stable internet connections. Thus, our study focused on using audio data to
predict depression risk. The objectives were to: 1) Collect audio data from 24
people (12 with depression and 12 without mental health or major health
condition diagnoses); 2) Build a machine learning model to predict depression
risk. TPOT, an autoML tool, was used to select the best machine learning
algorithm, which was the K-nearest neighbors classifier. The selected model had
high performance in classifying depression risk (Precision: 0.98, Recall: 0.93,
F1-Score: 0.96). These findings may lead to a range of tools to help screen for
and treat depression. By developing tools to detect depression risk, patients
can be routed to AI-driven chatbots for initial screenings. Partnerships with a
range of stakeholders are crucial to implementing these solutions. Moreover,
ethical considerations, especially around data privacy and potential biases in
AI models, need to be at the forefront of any AI-driven intervention in mental
health care. | [
"Adam Valen Levinson",
"Abhay Goyal",
"Roger Ho Chun Man",
"Roy Ka-Wei Lee",
"Koustuv Saha",
"Nimay Parekh",
"Frederick L. Altice",
"Lam Yin Cheung",
"Munmun De Choudhury",
"Navin Kumar"
] | 2023-10-17 01:55:49 | http://arxiv.org/abs/2310.10928v1 | http://arxiv.org/pdf/2310.10928v1 | 2310.10928v1 |
Compositional preference models for aligning LMs | As language models (LMs) become more capable, it is increasingly important to
align them with human preferences. However, the dominant paradigm for training
Preference Models (PMs) for that purpose suffers from fundamental limitations,
such as lack of transparency and scalability, along with susceptibility to
overfitting the preference dataset. We propose Compositional Preference Models
(CPMs), a novel PM framework that decomposes one global preference assessment
into several interpretable features, obtains scalar scores for these features
from a prompted LM, and aggregates these scores using a logistic regression
classifier. CPMs allow to control which properties of the preference data are
used to train the preference model and to build it based on features that are
believed to underlie the human preference judgment. Our experiments show that
CPMs not only improve generalization and are more robust to overoptimization
than standard PMs, but also that best-of-n samples obtained using CPMs tend to
be preferred over samples obtained using conventional PMs. Overall, our
approach demonstrates the benefits of endowing PMs with priors about which
features determine human preferences while relying on LM capabilities to
extract those features in a scalable and robust way. | [
"Dongyoung Go",
"Tomasz Korbak",
"Germán Kruszewski",
"Jos Rozen",
"Marc Dymetman"
] | 2023-10-17 01:31:59 | http://arxiv.org/abs/2310.13011v1 | http://arxiv.org/pdf/2310.13011v1 | 2310.13011v1 |
Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines | This work endeavors to juxtapose the efficacy of machine learning algorithms
within classical and quantum computational paradigms. Particularly, by
emphasizing on Support Vector Machines (SVM), we scrutinize the classification
prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational
on quantum hardware over the Iris dataset. The methodology embraced
encapsulates an extensive array of experiments orchestrated through the Qiskit
library, alongside hyperparameter optimization. The findings unveil that in
particular scenarios, QSVMs extend a level of accuracy that can vie with
classical SVMs, albeit the execution times are presently protracted. Moreover,
we underscore that augmenting quantum computational capacity and the magnitude
of parallelism can markedly ameliorate the performance of quantum machine
learning algorithms. This inquiry furnishes invaluable insights regarding the
extant scenario and future potentiality of machine learning applications in the
quantum epoch. Colab: https://t.ly/QKuz0 | [
"Davut Emre Tasar",
"Kutan Koruyan",
"Ceren Ocal Tasar"
] | 2023-10-17 01:06:59 | http://arxiv.org/abs/2310.10910v1 | http://arxiv.org/pdf/2310.10910v1 | 2310.10910v1 |
Heterogenous Memory Augmented Neural Networks | It has been shown that semi-parametric methods, which combine standard neural
networks with non-parametric components such as external memory modules and
data retrieval, are particularly helpful in data scarcity and
out-of-distribution (OOD) scenarios. However, existing semi-parametric methods
mostly depend on independent raw data points - this strategy is difficult to
scale up due to both high computational costs and the incapacity of current
attention mechanisms with a large number of tokens. In this paper, we introduce
a novel heterogeneous memory augmentation approach for neural networks which,
by introducing learnable memory tokens with attention mechanism, can
effectively boost performance without huge computational overhead. Our
general-purpose method can be seamlessly combined with various backbones (MLP,
CNN, GNN, and Transformer) in a plug-and-play manner. We extensively evaluate
our approach on various image and graph-based tasks under both in-distribution
(ID) and OOD conditions and show its competitive performance against
task-specific state-of-the-art methods. Code is available at
\url{https://github.com/qiuzh20/HMA}. | [
"Zihan Qiu",
"Zhen Liu",
"Shuicheng Yan",
"Shanghang Zhang",
"Jie Fu"
] | 2023-10-17 01:05:28 | http://arxiv.org/abs/2310.10909v1 | http://arxiv.org/pdf/2310.10909v1 | 2310.10909v1 |
Emergent Mixture-of-Experts: Can Dense Pre-trained Transformers Benefit from Emergent Modular Structures? | Incorporating modular designs into neural networks demonstrates superior
out-of-generalization, learning efficiency, etc. Existing modular neural
networks are generally $\textit{explicit}$ because their modular architectures
are pre-defined, and individual modules are expected to implement distinct
functions. Conversely, recent works reveal that there exist $\textit{implicit}$
modular structures in standard pre-trained transformers, namely
$\textit{Emergent Modularity}$. They indicate that such modular structures
exhibit during the early pre-training phase and are totally spontaneous.
However, most transformers are still treated as monolithic models with their
modular natures underutilized. Therefore, given the excellent properties of
explicit modular architecture, we explore $\textit{whether and how dense
pre-trained transformers can benefit from emergent modular structures.}$ To
study this question, we construct \textbf{E}mergent
$\textbf{M}$ixture-$\textbf{o}$f-$\textbf{E}$xperts (EMoE). Without introducing
additional parameters, EMoE can be seen as the modular counterpart of the
original model and can be effortlessly incorporated into downstream tuning.
Extensive experiments (we tune 1785 models) on various downstream tasks (vision
and language) and models (22M to1.5B) demonstrate that EMoE effectively boosts
in-domain and out-of-domain generalization abilities. Further analysis and
ablation study suggest that EMoE mitigates negative knowledge transfer and is
robust to various configurations. Code is available at
\url{https://github.com/qiuzh20/EMoE} | [
"Zihan Qiu",
"Zeyu Huang",
"Jie Fu"
] | 2023-10-17 01:02:32 | http://arxiv.org/abs/2310.10908v1 | http://arxiv.org/pdf/2310.10908v1 | 2310.10908v1 |
Surrogate Active Subspaces for Jump-Discontinuous Functions | Surrogate modeling and active subspaces have emerged as powerful paradigms in
computational science and engineering. Porting such techniques to computational
models in the social sciences brings into sharp relief their limitations in
dealing with discontinuous simulators, such as Agent-Based Models, which have
discrete outputs. Nevertheless, prior applied work has shown that surrogate
estimates of active subspaces for such estimators can yield interesting
results. But given that active subspaces are defined by way of gradients, it is
not clear what quantity is being estimated when this methodology is applied to
a discontinuous simulator. We begin this article by showing some pathologies
that can arise when conducting such an analysis. This motivates an extension of
active subspaces to discontinuous functions, clarifying what is actually being
estimated in such analyses. We also conduct numerical experiments on synthetic
test functions to compare Gaussian process estimates of active subspaces on
continuous and discontinuous functions. Finally, we deploy our methodology on
Flee, an agent-based model of refugee movement, yielding novel insights into
which parameters of the simulation are most important across 8 displacement
crises in Africa and the Middle East. | [
"Nathan Wycoff"
] | 2023-10-17 00:44:51 | http://arxiv.org/abs/2310.10907v2 | http://arxiv.org/pdf/2310.10907v2 | 2310.10907v2 |
Instilling Inductive Biases with Subnetworks | Despite the recent success of artificial neural networks on a variety of
tasks, we have little knowledge or control over the exact solutions these
models implement. Instilling inductive biases -- preferences for some solutions
over others -- into these models is one promising path toward understanding and
controlling their behavior. Much work has been done to study the inherent
inductive biases of models and instill different inductive biases through
hand-designed architectures or carefully curated training regimens. In this
work, we explore a more mechanistic approach: Subtask Induction. Our method
discovers a functional subnetwork that implements a particular subtask within a
trained model and uses it to instill inductive biases towards solutions
utilizing that subtask. Subtask Induction is flexible and efficient, and we
demonstrate its effectiveness with two experiments. First, we show that Subtask
Induction significantly reduces the amount of training data required for a
model to adopt a specific, generalizable solution to a modular arithmetic task.
Second, we demonstrate that Subtask Induction successfully induces a human-like
shape bias while increasing data efficiency for convolutional and
transformer-based image classification models. | [
"Enyan Zhang",
"Michael A. Lepori",
"Ellie Pavlick"
] | 2023-10-17 00:12:19 | http://arxiv.org/abs/2310.10899v1 | http://arxiv.org/pdf/2310.10899v1 | 2310.10899v1 |
Analyzing Modularity Maximization in Approximation, Heuristic, and Graph Neural Network Algorithms for Community Detection | Community detection, a fundamental problem in computational sciences, finds
applications in various domains. Heuristics are often employed to detect
communities through maximizing an objective function, modularity, over
partitions of network nodes. Our research delves into the performance of
different modularity maximization algorithms in achieving optimal partitions.
We use 104 networks, comprising real-world instances from diverse contexts and
synthetic graphs with modular structures. We analyze ten inexact
modularity-based algorithms against an exact baseline which is an exact integer
programming method that globally optimizes modularity. The ten algorithms
analyzed include eight heuristics, two variations of a graph neural network
algorithm, and several variations of the Bayan approximation algorithm. Our
analysis uncovers substantial dissimilarities between the partitions obtained
by most commonly used modularity-based methods and any optimal partition of the
networks, as indicated by both adjusted and reduced mutual information metrics.
Importantly, our results show that near-optimal partitions are often
disproportionately dissimilar to any optimal partition. Taken together, our
analysis points to a crucial limitation of the commonly used unguaranteed
modularity-based methods for discovering communities: they rarely produce an
optimal partition or a partition resembling an optimal partition even on
networks with modular structures. If modularity is to be used for detecting
communities, approximate optimization algorithms are recommendable for a more
methodologically sound usage of modularity within its applicability limits. | [
"Samin Aref",
"Mahdi Mostajabdaveh"
] | 2023-10-17 00:12:18 | http://arxiv.org/abs/2310.10898v1 | http://arxiv.org/pdf/2310.10898v1 | 2310.10898v1 |
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity Prediction | T cell receptors (TCRs) are critical components of adaptive immune systems,
responsible for responding to threats by recognizing epitope sequences
presented on host cell surface. Computational prediction of binding affinity
between TCRs and epitope sequences using machine/deep learning has attracted
intense attention recently. However, its success is hindered by the lack of
large collections of annotated TCR-epitope pairs. Annotating their binding
affinity requires expensive and time-consuming wet-lab evaluation. To reduce
annotation cost, we present ActiveTCR, a framework that incorporates active
learning and TCR-epitope binding affinity prediction models. Starting with a
small set of labeled training pairs, ActiveTCR iteratively searches for
unlabeled TCR-epitope pairs that are ''worth'' for annotation. It aims to
maximize performance gains while minimizing the cost of annotation. We compared
four query strategies with a random sampling baseline and demonstrated that
ActiveTCR reduces annotation costs by approximately 40%. Furthermore, we showed
that providing ground truth labels of TCR-epitope pairs to query strategies can
help identify and reduce more than 40% redundancy among already annotated pairs
without compromising model performance, enabling users to train equally
powerful prediction models with less training data. Our work is the first
systematic investigation of data optimization for TCR-epitope binding affinity
prediction. | [
"Pengfei Zhang",
"Seojin Bang",
"Heewook Lee"
] | 2023-10-16 23:53:07 | http://arxiv.org/abs/2310.10893v1 | http://arxiv.org/pdf/2310.10893v1 | 2310.10893v1 |
Subsets and Splits