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Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms | This work describes the selection approach and analysis of existing AutoML
frameworks regarding their capability of a) incorporating Quantum Machine
Learning (QML) algorithms into this automated solving approach of the AutoML
framing and b) solving a set of industrial use-cases with different ML problem
types by benchmarking their most important characteristics. For that, available
open-source tools are condensed into a market overview and suitable frameworks
are systematically selected on a multi-phase, multi-criteria approach. This is
done by considering software selection approaches, as well as in terms of the
technical perspective of AutoML. The requirements for the framework selection
are divided into hard and soft criteria regarding their software and ML
attributes. Additionally, a classification of AutoML frameworks is made into
high- and low-level types, inspired by the findings of. Finally, we select Ray
and AutoGluon as the suitable low- and high-level frameworks respectively, as
they fulfil all requirements sufficiently and received the best evaluation
feedback during the use-case study. Based on those findings, we build an
extended Automated Quantum Machine Learning (AutoQML) framework with
QC-specific pipeline steps and decision characteristics for hardware and
software constraints. | [
"Dennis Klau",
"Marc Zöller",
"Christian Tutschku"
] | 2023-10-06 13:21:16 | http://arxiv.org/abs/2310.04238v1 | http://arxiv.org/pdf/2310.04238v1 | 2310.04238v1 |
A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the same Skeleton | Causal DAGs (also known as Bayesian networks) are a popular tool for encoding
conditional dependencies between random variables. In a causal DAG, the random
variables are modeled as vertices in the DAG, and it is stipulated that every
random variable is independent of its ancestors conditioned on its parents. It
is possible, however, for two different causal DAGs on the same set of random
variables to encode exactly the same set of conditional dependencies. Such
causal DAGs are said to be Markov equivalent, and equivalence classes of Markov
equivalent DAGs are known as Markov Equivalent Classes (MECs). Beautiful
combinatorial characterizations of MECs have been developed in the past few
decades, and it is known, in particular that all DAGs in the same MEC must have
the same ''skeleton'' (underlying undirected graph) and v-structures (induced
subgraph of the form $a\rightarrow b \leftarrow c$).
These combinatorial characterizations also suggest several natural
algorithmic questions. One of these is: given an undirected graph $G$ as input,
how many distinct Markov equivalence classes have the skeleton $G$? Much work
has been devoted in the last few years to this and other closely related
problems. However, to the best of our knowledge, a polynomial time algorithm
for the problem remains unknown.
In this paper, we make progress towards this goal by giving a fixed parameter
tractable algorithm for the above problem, with the parameters being the
treewidth and the maximum degree of the input graph $G$. The main technical
ingredient in our work is a construction we refer to as shadow, which lets us
create a "local description'' of long-range constraints imposed by the
combinatorial characterizations of MECs. | [
"Vidya Sagar Sharma"
] | 2023-10-06 13:05:07 | http://arxiv.org/abs/2310.04218v1 | http://arxiv.org/pdf/2310.04218v1 | 2310.04218v1 |
Cost-Effective Retraining of Machine Learning Models | It is important to retrain a machine learning (ML) model in order to maintain
its performance as the data changes over time. However, this can be costly as
it usually requires processing the entire dataset again. This creates a
trade-off between retraining too frequently, which leads to unnecessary
computing costs, and not retraining often enough, which results in stale and
inaccurate ML models. To address this challenge, we propose ML systems that
make automated and cost-effective decisions about when to retrain an ML model.
We aim to optimize the trade-off by considering the costs associated with each
decision. Our research focuses on determining whether to retrain or keep an
existing ML model based on various factors, including the data, the model, and
the predictive queries answered by the model. Our main contribution is a
Cost-Aware Retraining Algorithm called Cara, which optimizes the trade-off over
streams of data and queries. To evaluate the performance of Cara, we analyzed
synthetic datasets and demonstrated that Cara can adapt to different data
drifts and retraining costs while performing similarly to an optimal
retrospective algorithm. We also conducted experiments with real-world datasets
and showed that Cara achieves better accuracy than drift detection baselines
while making fewer retraining decisions, ultimately resulting in lower total
costs. | [
"Ananth Mahadevan",
"Michael Mathioudakis"
] | 2023-10-06 13:02:29 | http://arxiv.org/abs/2310.04216v1 | http://arxiv.org/pdf/2310.04216v1 | 2310.04216v1 |
A Bi-objective Perspective on Controllable Language Models: Reward Dropout Improves Off-policy Control Performance | We study the theoretical aspects of CLMs (Controllable Language Models) from
a bi-objective optimization perspective. Specifically, we consider the CLMs as
an off-policy RL problem that requires simultaneously maximizing the reward and
likelihood objectives. Our main contribution consists of three parts. First, we
establish the theoretical foundations of CLM by presenting reward upper bound
and Pareto improvement/optimality conditions. Second, we analyze conditions
that improve and violate Pareto optimality itself, respectively. Finally, we
propose Reward Dropout, a simple yet powerful method to guarantee policy
improvement based on a Pareto improvement condition. Our theoretical outcomes
are supported by not only deductive proofs but also empirical results. The
performance of Reward Dropout was evaluated on five CLM benchmark datasets, and
it turns out that the Reward Dropout significantly improves the performance of
CLMs. | [
"Changhun Lee",
"Chiehyeon Lim"
] | 2023-10-06 12:33:32 | http://arxiv.org/abs/2310.04483v1 | http://arxiv.org/pdf/2310.04483v1 | 2310.04483v1 |
EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through Rate Prediction | Track one of CTI competition is on click-through rate (CTR) prediction. The
dataset contains millions of records and each field-wise feature in a record
consists of hashed integers for privacy. For this task, the keys of
network-based methods might be type-wise feature extraction and information
fusion across different fields. Multi-layer perceptrons (MLPs) are able to
extract field feature, but could not efficiently fuse features. Motivated by
the natural fusion characteristic of cross attention and the efficiency of
transformer-based structures, we propose simple plug-in mixers for
field/type-wise feature fusion, and thus construct an field&type-wise ensemble
model, namely EMOFM (Ensemble MLP mOdel with Feature-based Mixers). In the
experiments, the proposed model is evaluated on the dataset, the optimization
process is visualized and ablation studies are explored. It is shown that EMOFM
outperforms compared baselines. In the end, we discuss on future work. WARNING:
The comparison might not be fair enough since the proposed method is designed
for this data in particular while compared methods are not. For example, EMOFM
especially takes different types of interactions into consideration while
others do not. Anyway, we do hope that the ideas inside our method could help
other developers/learners/researchers/thinkers and so on. | [
"Yujian Betterest Li",
"Kai Wu"
] | 2023-10-06 12:32:23 | http://arxiv.org/abs/2310.04482v2 | http://arxiv.org/pdf/2310.04482v2 | 2310.04482v2 |
Conversational Financial Information Retrieval Model (ConFIRM) | With the exponential growth in large language models (LLMs), leveraging their
emergent properties for specialized domains like finance merits exploration.
However, regulated fields such as finance pose unique constraints, requiring
domain-optimized frameworks. We present ConFIRM, an LLM-based conversational
financial information retrieval model tailored for query intent classification
and knowledge base labeling.
ConFIRM comprises two modules:
1) a method to synthesize finance domain-specific question-answer pairs, and
2) evaluation of parameter efficient fine-tuning approaches for the query
classification task. We generate a dataset of over 4000 samples, assessing
accuracy on a separate test set.
ConFIRM achieved over 90% accuracy, essential for regulatory compliance.
ConFIRM provides a data-efficient solution to extract precise query intent for
financial dialog systems. | [
"Stephen Choi",
"William Gazeley",
"Siu Ho Wong",
"Tingting Li"
] | 2023-10-06 12:31:05 | http://arxiv.org/abs/2310.13001v1 | http://arxiv.org/pdf/2310.13001v1 | 2310.13001v1 |
Non-Redundant Graph Neural Networks with Improved Expressiveness | Message passing graph neural networks iteratively compute node embeddings by
aggregating messages from all neighbors. This procedure can be viewed as a
neural variant of the Weisfeiler-Leman method, which limits their expressive
power. Moreover, oversmoothing and oversquashing restrict the number of layers
these networks can effectively utilize. The repeated exchange and encoding of
identical information in message passing amplifies oversquashing. We propose a
novel aggregation scheme based on neighborhood trees, which allows for
controlling the redundancy by pruning branches of the unfolding trees
underlying standard message passing. We prove that reducing redundancy improves
expressivity and experimentally show that it alleviates oversquashing. We
investigate the interaction between redundancy in message passing and
redundancy in computation and propose a compact representation of neighborhood
trees, from which we compute node and graph embeddings via a neural tree
canonization technique. Our method is provably more expressive than the
Weisfeiler-Leman method, less susceptible to oversquashing than message passing
neural networks, and provides high classification accuracy on widely-used
benchmark datasets. | [
"Franka Bause",
"Samir Moustafa",
"Johannes Langguth",
"Wilfried N. Gansterer",
"Nils M. Kriege"
] | 2023-10-06 12:09:09 | http://arxiv.org/abs/2310.04190v1 | http://arxiv.org/pdf/2310.04190v1 | 2310.04190v1 |
Entropic Score metric: Decoupling Topology and Size in Training-free NAS | Neural Networks design is a complex and often daunting task, particularly for
resource-constrained scenarios typical of mobile-sized models. Neural
Architecture Search is a promising approach to automate this process, but
existing competitive methods require large training time and computational
resources to generate accurate models. To overcome these limits, this paper
contributes with: i) a novel training-free metric, named Entropic Score, to
estimate model expressivity through the aggregated element-wise entropy of its
activations; ii) a cyclic search algorithm to separately yet synergistically
search model size and topology. Entropic Score shows remarkable ability in
searching for the topology of the network, and a proper combination with
LogSynflow, to search for model size, yields superior capability to completely
design high-performance Hybrid Transformers for edge applications in less than
1 GPU hour, resulting in the fastest and most accurate NAS method for ImageNet
classification. | [
"Niccolò Cavagnero",
"Luca Robbiano",
"Francesca Pistilli",
"Barbara Caputo",
"Giuseppe Averta"
] | 2023-10-06 11:49:21 | http://arxiv.org/abs/2310.04179v1 | http://arxiv.org/pdf/2310.04179v1 | 2310.04179v1 |
Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions | Given the increasing amount and general complexity of time series data in
domains such as finance, weather forecasting, and healthcare, there is a
growing need for state-of-the-art performance models that can provide
interpretable insights into underlying patterns and relationships. Attribution
techniques enable the extraction of explanations from time series models to
gain insights but are hard to evaluate for their robustness and
trustworthiness. We propose the Attribution Stability Indicator (ASI), a
measure to incorporate robustness and trustworthiness as properties of
attribution techniques for time series into account. We extend a perturbation
analysis with correlations of the original time series to the perturbed
instance and the attributions to include wanted properties in the measure. We
demonstrate the wanted properties based on an analysis of the attributions in a
dimension-reduced space and the ASI scores distribution over three whole time
series classification datasets. | [
"Udo Schlegel",
"Daniel A. Keim"
] | 2023-10-06 11:48:26 | http://arxiv.org/abs/2310.04178v1 | http://arxiv.org/pdf/2310.04178v1 | 2310.04178v1 |
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection | Fraud detection aims to discover fraudsters deceiving other users by, for
example, leaving fake reviews or making abnormal transactions. Graph-based
fraud detection methods consider this task as a classification problem with two
classes: frauds or normal. We address this problem using Graph Neural Networks
(GNNs) by proposing a dynamic relation-attentive aggregation mechanism. Based
on the observation that many real-world graphs include different types of
relations, we propose to learn a node representation per relation and aggregate
the node representations using a learnable attention function that assigns a
different attention coefficient to each relation. Furthermore, we combine the
node representations from different layers to consider both the local and
global structures of a target node, which is beneficial to improving the
performance of fraud detection on graphs with heterophily. By employing dynamic
graph attention in all the aggregation processes, our method adaptively
computes the attention coefficients for each node. Experimental results show
that our method, DRAG, outperforms state-of-the-art fraud detection methods on
real-world benchmark datasets. | [
"Heehyeon Kim",
"Jinhyeok Choi",
"Joyce Jiyoung Whang"
] | 2023-10-06 11:41:38 | http://arxiv.org/abs/2310.04171v2 | http://arxiv.org/pdf/2310.04171v2 | 2310.04171v2 |
Amortized Network Intervention to Steer the Excitatory Point Processes | We tackle the challenge of large-scale network intervention for guiding
excitatory point processes, such as infectious disease spread or traffic
congestion control. Our model-based reinforcement learning utilizes neural ODEs
to capture how the networked excitatory point processes will evolve subject to
the time-varying changes in network topology. Our approach incorporates
Gradient-Descent based Model Predictive Control (GD-MPC), offering policy
flexibility to accommodate prior knowledge and constraints. To address the
intricacies of planning and overcome the high dimensionality inherent to such
decision-making problems, we design an Amortize Network Interventions (ANI)
framework, allowing for the pooling of optimal policies from history and other
contexts, while ensuring a permutation equivalent property. This property
enables efficient knowledge transfer and sharing across diverse contexts. Our
approach has broad applications, from curbing infectious disease spread to
reducing carbon emissions through traffic light optimization, and thus has the
potential to address critical societal and environmental challenges. | [
"Zitao Song",
"Wendi Ren",
"Shuang Li"
] | 2023-10-06 11:17:28 | http://arxiv.org/abs/2310.04159v1 | http://arxiv.org/pdf/2310.04159v1 | 2310.04159v1 |
From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying | Safeguarding the Intellectual Property (IP) of data has become critically
important as machine learning applications continue to proliferate, and their
success heavily relies on the quality of training data. While various
mechanisms exist to secure data during storage, transmission, and consumption,
fewer studies have been developed to detect whether they are already leaked for
model training without authorization. This issue is particularly challenging
due to the absence of information and control over the training process
conducted by potential attackers.
In this paper, we concentrate on the domain of tabular data and introduce a
novel methodology, Local Distribution Shifting Synthesis (\textsc{LDSS}), to
detect leaked data that are used to train classification models. The core
concept behind \textsc{LDSS} involves injecting a small volume of synthetic
data--characterized by local shifts in class distribution--into the owner's
dataset. This enables the effective identification of models trained on leaked
data through model querying alone, as the synthetic data injection results in a
pronounced disparity in the predictions of models trained on leaked and
modified datasets. \textsc{LDSS} is \emph{model-oblivious} and hence compatible
with a diverse range of classification models, such as Naive Bayes, Decision
Tree, and Random Forest. We have conducted extensive experiments on seven types
of classification models across five real-world datasets. The comprehensive
results affirm the reliability, robustness, fidelity, security, and efficiency
of \textsc{LDSS}. | [
"Biao Wu",
"Qiang Huang",
"Anthony K. H. Tung"
] | 2023-10-06 10:36:28 | http://arxiv.org/abs/2310.04145v1 | http://arxiv.org/pdf/2310.04145v1 | 2310.04145v1 |
Routing Arena: A Benchmark Suite for Neural Routing Solvers | Neural Combinatorial Optimization has been researched actively in the last
eight years. Even though many of the proposed Machine Learning based approaches
are compared on the same datasets, the evaluation protocol exhibits essential
flaws and the selection of baselines often neglects State-of-the-Art Operations
Research approaches. To improve on both of these shortcomings, we propose the
Routing Arena, a benchmark suite for Routing Problems that provides a seamless
integration of consistent evaluation and the provision of baselines and
benchmarks prevalent in the Machine Learning- and Operations Research field.
The proposed evaluation protocol considers the two most important evaluation
cases for different applications: First, the solution quality for an a priori
fixed time budget and secondly the anytime performance of the respective
methods. By setting the solution trajectory in perspective to a Best Known
Solution and a Base Solver's solutions trajectory, we furthermore propose the
Weighted Relative Average Performance (WRAP), a novel evaluation metric that
quantifies the often claimed runtime efficiency of Neural Routing Solvers. A
comprehensive first experimental evaluation demonstrates that the most recent
Operations Research solvers generate state-of-the-art results in terms of
solution quality and runtime efficiency when it comes to the vehicle routing
problem. Nevertheless, some findings highlight the advantages of neural
approaches and motivate a shift in how neural solvers should be conceptualized. | [
"Daniela Thyssens",
"Tim Dernedde",
"Jonas K. Falkner",
"Lars Schmidt-Thieme"
] | 2023-10-06 10:24:33 | http://arxiv.org/abs/2310.04140v1 | http://arxiv.org/pdf/2310.04140v1 | 2310.04140v1 |
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations | The goal of our research is to automatically retrieve the satisfaction and
the frustration in real-life call-center conversations. This study focuses an
industrial application in which the customer satisfaction is continuously
tracked down to improve customer services. To compensate the lack of large
annotated emotional databases, we explore the use of pre-trained speech
representations as a form of transfer learning towards AlloSat corpus.
Moreover, several studies have pointed out that emotion can be detected not
only in speech but also in facial trait, in biological response or in textual
information. In the context of telephone conversations, we can break down the
audio information into acoustic and linguistic by using the speech signal and
its transcription. Our experiments confirms the large gain in performance
obtained with the use of pre-trained features. Surprisingly, we found that the
linguistic content is clearly the major contributor for the prediction of
satisfaction and best generalizes to unseen data. Our experiments conclude to
the definitive advantage of using CamemBERT representations, however the
benefit of the fusion of acoustic and linguistic modalities is not as obvious.
With models learnt on individual annotations, we found that fusion approaches
are more robust to the subjectivity of the annotation task. This study also
tackles the problem of performances variability and intends to estimate this
variability from different views: weights initialization, confidence intervals
and annotation subjectivity. A deep analysis on the linguistic content
investigates interpretable factors able to explain the high contribution of the
linguistic modality for this task. | [
"Manon Macary",
"Marie Tahon",
"Yannick Estève",
"Daniel Luzzati"
] | 2023-10-06 10:22:51 | http://arxiv.org/abs/2310.04481v1 | http://arxiv.org/pdf/2310.04481v1 | 2310.04481v1 |
Reinforcement Learning with Fast and Forgetful Memory | Nearly all real world tasks are inherently partially observable,
necessitating the use of memory in Reinforcement Learning (RL). Most model-free
approaches summarize the trajectory into a latent Markov state using memory
models borrowed from Supervised Learning (SL), even though RL tends to exhibit
different training and efficiency characteristics. Addressing this discrepancy,
we introduce Fast and Forgetful Memory, an algorithm-agnostic memory model
designed specifically for RL. Our approach constrains the model search space
via strong structural priors inspired by computational psychology. It is a
drop-in replacement for recurrent neural networks (RNNs) in recurrent RL
algorithms, achieving greater reward than RNNs across various recurrent
benchmarks and algorithms without changing any hyperparameters. Moreover, Fast
and Forgetful Memory exhibits training speeds two orders of magnitude faster
than RNNs, attributed to its logarithmic time and linear space complexity. Our
implementation is available at https://github.com/proroklab/ffm. | [
"Steven Morad",
"Ryan Kortvelesy",
"Stephan Liwicki",
"Amanda Prorok"
] | 2023-10-06 09:56:26 | http://arxiv.org/abs/2310.04128v1 | http://arxiv.org/pdf/2310.04128v1 | 2310.04128v1 |
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems | With the growing privacy concerns in recommender systems, recommendation
unlearning, i.e., forgetting the impact of specific learned targets, is getting
increasing attention. Existing studies predominantly use training data, i.e.,
model inputs, as the unlearning target. However, we find that attackers can
extract private information, i.e., gender, race, and age, from a trained model
even if it has not been explicitly encountered during training. We name this
unseen information as attribute and treat it as the unlearning target. To
protect the sensitive attribute of users, Attribute Unlearning (AU) aims to
degrade attacking performance and make target attributes indistinguishable. In
this paper, we focus on a strict but practical setting of AU, namely
Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be
performed after the training of the recommendation model is completed. To
address the PoT-AU problem in recommender systems, we design a two-component
loss function that consists of i) distinguishability loss: making attribute
labels indistinguishable from attackers, and ii) regularization loss:
preventing drastic changes in the model that result in a negative impact on
recommendation performance. Specifically, we investigate two types of
distinguishability measurements, i.e., user-to-user and
distribution-to-distribution. We use the stochastic gradient descent algorithm
to optimize our proposed loss. Extensive experiments on three real-world
datasets demonstrate the effectiveness of our proposed methods. | [
"Yuyuan Li",
"Chaochao Chen",
"Xiaolin Zheng",
"Yizhao Zhang",
"Zhongxuan Han",
"Dan Meng",
"Jun Wang"
] | 2023-10-06 09:36:44 | http://arxiv.org/abs/2310.05847v1 | http://arxiv.org/pdf/2310.05847v1 | 2310.05847v1 |
Leveraging Data Geometry to Mitigate CSM in Steganalysis | In operational scenarios, steganographers use sets of covers from various
sensors and processing pipelines that differ significantly from those used by
researchers to train steganalysis models. This leads to an inevitable
performance gap when dealing with out-of-distribution covers, commonly referred
to as Cover Source Mismatch (CSM). In this study, we consider the scenario
where test images are processed using the same pipeline. However, knowledge
regarding both the labels and the balance between cover and stego is missing.
Our objective is to identify a training dataset that allows for maximum
generalization to our target. By exploring a grid of processing pipelines
fostering CSM, we discovered a geometrical metric based on the chordal distance
between subspaces spanned by DCTr features, that exhibits high correlation with
operational regret while being not affected by the cover-stego balance. Our
contribution lies in the development of a strategy that enables the selection
or derivation of customized training datasets, enhancing the overall
generalization performance for a given target. Experimental validation
highlights that our geometry-based optimization strategy outperforms
traditional atomistic methods given reasonable assumptions. Additional
resources are available at
github.com/RonyAbecidan/LeveragingGeometrytoMitigateCSM. | [
"Rony Abecidan",
"Vincent Itier",
"Jérémie Boulanger",
"Patrick Bas",
"Tomáš Pevný"
] | 2023-10-06 09:08:25 | http://arxiv.org/abs/2310.04479v1 | http://arxiv.org/pdf/2310.04479v1 | 2310.04479v1 |
Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends | Learning binary classifiers from positive and unlabeled data (PUL) is vital
in many real-world applications, especially when verifying negative examples is
difficult. Despite the impressive empirical performance of recent PUL methods,
challenges like accumulated errors and increased estimation bias persist due to
the absence of negative labels. In this paper, we unveil an intriguing yet
long-overlooked observation in PUL: \textit{resampling the positive data in
each training iteration to ensure a balanced distribution between positive and
unlabeled examples results in strong early-stage performance. Furthermore,
predictive trends for positive and negative classes display distinctly
different patterns.} Specifically, the scores (output probability) of unlabeled
negative examples consistently decrease, while those of unlabeled positive
examples show largely chaotic trends. Instead of focusing on classification
within individual time frames, we innovatively adopt a holistic approach,
interpreting the scores of each example as a temporal point process (TPP). This
reformulates the core problem of PUL as recognizing trends in these scores. We
then propose a novel TPP-inspired measure for trend detection and prove its
asymptotic unbiasedness in predicting changes. Notably, our method accomplishes
PUL without requiring additional parameter tuning or prior assumptions,
offering an alternative perspective for tackling this problem. Extensive
experiments verify the superiority of our method, particularly in a highly
imbalanced real-world setting, where it achieves improvements of up to $11.3\%$
in key metrics. The code is available at
\href{https://github.com/wxr99/HolisticPU}{https://github.com/wxr99/HolisticPU}. | [
"Xinrui Wang",
"Wenhai Wan",
"Chuanxin Geng",
"Shaoyuan LI",
"Songcan Chen"
] | 2023-10-06 08:06:15 | http://arxiv.org/abs/2310.04078v1 | http://arxiv.org/pdf/2310.04078v1 | 2310.04078v1 |
Automatic Aspect Extraction from Scientific Texts | Being able to extract from scientific papers their main points, key insights,
and other important information, referred to here as aspects, might facilitate
the process of conducting a scientific literature review. Therefore, the aim of
our research is to create a tool for automatic aspect extraction from
Russian-language scientific texts of any domain. In this paper, we present a
cross-domain dataset of scientific texts in Russian, annotated with such
aspects as Task, Contribution, Method, and Conclusion, as well as a baseline
algorithm for aspect extraction, based on the multilingual BERT model
fine-tuned on our data. We show that there are some differences in aspect
representation in different domains, but even though our model was trained on a
limited number of scientific domains, it is still able to generalize to new
domains, as was proved by cross-domain experiments. The code and the dataset
are available at
\url{https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts}. | [
"Anna Marshalova",
"Elena Bruches",
"Tatiana Batura"
] | 2023-10-06 07:59:54 | http://arxiv.org/abs/2310.04074v1 | http://arxiv.org/pdf/2310.04074v1 | 2310.04074v1 |
How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation | In the classical transformer attention scheme, we are given three $n \times
d$ size matrices $Q, K, V$ (the query, key, and value tokens), and the goal is
to compute a new $n \times d$ size matrix $D^{-1} \exp(QK^\top) V$ where $D =
\mathrm{diag}( \exp(QK^\top) {\bf 1}_n )$. In this work, we study a
generalization of attention which captures triple-wise correlations. This
generalization is able to solve problems about detecting triple-wise
connections that were shown to be impossible for transformers. The potential
downside of this generalization is that it appears as though computations are
even more difficult, since the straightforward algorithm requires cubic time in
$n$. However, we show that in the bounded-entry setting (which arises in
practice, and which is well-studied in both theory and practice), there is
actually a near-linear time algorithm. More precisely, we show that bounded
entries are both necessary and sufficient for quickly performing generalized
computations:
$\bullet$ On the positive side, if all entries of the input matrices are
bounded above by $o(\sqrt[3]{\log n})$ then we show how to approximate the
``tensor-type'' attention matrix in $n^{1+o(1)}$ time.
$\bullet$ On the negative side, we show that if the entries of the input
matrices may be as large as $\Omega(\sqrt[3]{\log n})$, then there is no
algorithm that runs faster than $n^{3-o(1)}$ (assuming the Strong Exponential
Time Hypothesis from fine-grained complexity theory).
We also show that our construction, algorithms, and lower bounds naturally
generalize to higher-order tensors and correlations. Interestingly, the higher
the order of the tensors, the lower the bound on the entries needs to be for an
efficient algorithm. Our results thus yield a natural tradeoff between the
boundedness of the entries, and order of the tensor one may use for more
expressive, efficient attention computation. | [
"Josh Alman",
"Zhao Song"
] | 2023-10-06 07:42:39 | http://arxiv.org/abs/2310.04064v1 | http://arxiv.org/pdf/2310.04064v1 | 2310.04064v1 |
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection | In the ever-evolving realm of network security, the swift and accurate
identification of diverse attack classes within network traffic is of paramount
importance. This paper introduces "ByteStack-ID," a pioneering approach
tailored for packet-level intrusion detection. At its core, ByteStack-ID
leverages grayscale images generated from the frequency distributions of
payload data, a groundbreaking technique that greatly enhances the model's
ability to discern intricate data patterns. Notably, our approach is
exclusively grounded in packet-level information, a departure from conventional
Network Intrusion Detection Systems (NIDS) that predominantly rely on
flow-based data. While building upon the fundamental concept of stacking
methodology, ByteStack-ID diverges from traditional stacking approaches. It
seamlessly integrates additional meta learner layers into the concatenated base
learners, creating a highly optimized, unified model. Empirical results
unequivocally confirm the outstanding effectiveness of the ByteStack-ID
framework, consistently outperforming baseline models and state-of-the-art
approaches across pivotal performance metrics, including precision, recall, and
F1-score. Impressively, our proposed approach achieves an exceptional 81\%
macro F1-score in multiclass classification tasks. In a landscape marked by the
continuous evolution of network threats, ByteStack-ID emerges as a robust and
versatile security solution, relying solely on packet-level information
extracted from network traffic data. | [
"Irfan Khan",
"Yasir Ali Farrukh",
"Syed Wali"
] | 2023-10-06 07:30:02 | http://arxiv.org/abs/2310.09298v1 | http://arxiv.org/pdf/2310.09298v1 | 2310.09298v1 |
DEFT: A new distance-based feature set for keystroke dynamics | Keystroke dynamics is a behavioural biometric utilised for user
identification and authentication. We propose a new set of features based on
the distance between keys on the keyboard, a concept that has not been
considered before in keystroke dynamics. We combine flight times, a popular
metric, with the distance between keys on the keyboard and call them as
Distance Enhanced Flight Time features (DEFT). This novel approach provides
comprehensive insights into a person's typing behaviour, surpassing typing
velocity alone. We build a DEFT model by combining DEFT features with other
previously used keystroke dynamic features. The DEFT model is designed to be
device-agnostic, allowing us to evaluate its effectiveness across three
commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms
the existing state-of-the-art methods when we evaluate its effectiveness across
two datasets. We obtain accuracy rates exceeding 99% and equal error rates
below 10% on all three devices. | [
"Nuwan Kaluarachchi",
"Sevvandi Kandanaarachchi",
"Kristen Moore",
"Arathi Arakala"
] | 2023-10-06 07:26:40 | http://arxiv.org/abs/2310.04059v1 | http://arxiv.org/pdf/2310.04059v1 | 2310.04059v1 |
Higher-Order DeepTrails: Unified Approach to *Trails | Analyzing, understanding, and describing human behavior is advantageous in
different settings, such as web browsing or traffic navigation. Understanding
human behavior naturally helps to improve and optimize the underlying
infrastructure or user interfaces. Typically, human navigation is represented
by sequences of transitions between states. Previous work suggests to use
hypotheses, representing different intuitions about the navigation to analyze
these transitions. To mathematically grasp this setting, first-order Markov
chains are used to capture the behavior, consequently allowing to apply
different kinds of graph comparisons, but comes with the inherent drawback of
losing information about higher-order dependencies within the sequences. To
this end, we propose to analyze entire sequences using autoregressive language
models, as they are traditionally used to model higher-order dependencies in
sequences. We show that our approach can be easily adapted to model different
settings introduced in previous work, namely HypTrails, MixedTrails and even
SubTrails, while at the same time bringing unique advantages: 1. Modeling
higher-order dependencies between state transitions, while 2. being able to
identify short comings in proposed hypotheses, and 3. naturally introducing a
unified approach to model all settings. To show the expressiveness of our
approach, we evaluate our approach on different synthetic datasets and conclude
with an exemplary analysis of a real-world dataset, examining the behavior of
users who interact with voice assistants. | [
"Tobias Koopmann",
"Jan Pfister",
"André Markus",
"Astrid Carolus",
"Carolin Wienrich",
"Andreas Hotho"
] | 2023-10-06 06:54:11 | http://arxiv.org/abs/2310.04477v1 | http://arxiv.org/pdf/2310.04477v1 | 2310.04477v1 |
AUTOPARLLM: GNN-Guided Automatic Code Parallelization using Large Language Models | Parallelizing sequentially written programs is a challenging task. Even
experienced developers need to spend considerable time finding parallelism
opportunities and then actually writing parallel versions of sequentially
written programs. To address this issue, we present AUTOPARLLM, a framework for
automatically discovering parallelism and generating the parallel version of
the sequentially written program. Our framework consists of two major
components: i) a heterogeneous Graph Neural Network (GNN) based parallelism
discovery and parallel pattern detection module, and ii) an LLM-based code
generator to generate the parallel counterpart of the sequential programs. We
use the GNN to learn the flow-aware characteristics of the programs to identify
parallel regions in sequential programs and then construct an enhanced prompt
using the GNN's results for the LLM-based generator to finally produce the
parallel counterparts of the sequential programs. We evaluate AUTOPARLLM on 11
applications of 2 well-known benchmark suites: NAS Parallel Benchmark and
Rodinia Benchmark. Our results show that AUTOPARLLM is indeed effective in
improving the state-of-the-art LLM-based models for the task of parallel code
generation in terms of multiple code generation metrics. AUTOPARLLM also
improves the average runtime of the parallel code generated by the
state-of-the-art LLMs by as high as 3.4% and 2.9% for the NAS Parallel
Benchmark and Rodinia Benchmark respectively. Additionally, to overcome the
issue that well-known metrics for translation evaluation have not been
optimized to evaluate the quality of the generated parallel code, we propose
OMPScore for evaluating the quality of the generated code. We show that
OMPScore exhibits a better correlation with human judgment than existing
metrics, measured by up to 75% improvement of Spearman correlation. | [
"Quazi Ishtiaque Mahmud",
"Ali TehraniJamsaz",
"Hung D Phan",
"Nesreen K. Ahmed",
"Ali Jannesari"
] | 2023-10-06 06:51:16 | http://arxiv.org/abs/2310.04047v2 | http://arxiv.org/pdf/2310.04047v2 | 2310.04047v2 |
Observation-Guided Diffusion Probabilistic Models | We propose a novel diffusion model called observation-guided diffusion
probabilistic model (OGDM), which effectively addresses the trade-off between
quality control and fast sampling. Our approach reestablishes the training
objective by integrating the guidance of the observation process with the
Markov chain in a principled way. This is achieved by introducing an additional
loss term derived from the observation based on the conditional discriminator
on noise level, which employs Bernoulli distribution indicating whether its
input lies on the (noisy) real manifold or not. This strategy allows us to
optimize the more accurate negative log-likelihood induced in the inference
stage especially when the number of function evaluations is limited. The
proposed training method is also advantageous even when incorporated only into
the fine-tuning process, and it is compatible with various fast inference
strategies since our method yields better denoising networks using the exactly
same inference procedure without incurring extra computational cost. We
demonstrate the effectiveness of the proposed training algorithm using diverse
inference methods on strong diffusion model baselines. | [
"Junoh Kang",
"Jinyoung Choi",
"Sungik Choi",
"Bohyung Han"
] | 2023-10-06 06:29:06 | http://arxiv.org/abs/2310.04041v1 | http://arxiv.org/pdf/2310.04041v1 | 2310.04041v1 |
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering | Incomplete multi-view clustering (IMVC) has received increasing attention
since it is often that some views of samples are incomplete in reality. Most
existing methods learn similarity subgraphs from original incomplete multi-view
data and seek complete graphs by exploring the incomplete subgraphs of each
view for spectral clustering. However, the graphs constructed on the original
high-dimensional data may be suboptimal due to feature redundancy and noise.
Besides, previous methods generally ignored the graph noise caused by the
inter-class and intra-class structure variation during the transformation of
incomplete graphs and complete graphs. To address these problems, we propose a
novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD)
for IMVC. Specifically, to alleviate the influence of redundant features and
noise in high-dimensional data, JPLTD introduces an orthogonal projection
matrix to project the high-dimensional features into a lower-dimensional space
for compact feature learning.Meanwhile, based on the lower-dimensional space,
the similarity graphs corresponding to instances of different views are
learned, and JPLTD stacks these graphs into a third-order low-rank tensor to
explore the high-order correlations across different views. We further consider
the graph noise of projected data caused by missing samples and use a
tensor-decomposition based graph filter for robust clustering.JPLTD decomposes
the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic
tensor models the true data similarities. An effective optimization algorithm
is adopted to solve the JPLTD model. Comprehensive experiments on several
benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art
methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD. | [
"Wei Lv",
"Chao Zhang",
"Huaxiong Li",
"Xiuyi Jia",
"Chunlin Chen"
] | 2023-10-06 06:19:16 | http://arxiv.org/abs/2310.04038v1 | http://arxiv.org/pdf/2310.04038v1 | 2310.04038v1 |
Genetic prediction of quantitative traits: a machine learner's guide focused on height | Machine learning and deep learning have been celebrating many successes in
the application to biological problems, especially in the domain of protein
folding. Another equally complex and important question has received relatively
little attention by the machine learning community, namely the one of
prediction of complex traits from genetics. Tackling this problem requires
in-depth knowledge of the related genetics literature and awareness of various
subtleties associated with genetic data. In this guide, we provide an overview
for the machine learning community on current state of the art models and
associated subtleties which need to be taken into consideration when developing
new models for phenotype prediction. We use height as an example of a
continuous-valued phenotype and provide an introduction to benchmark datasets,
confounders, feature selection, and common metrics. | [
"Lucie Bourguignon",
"Caroline Weis",
"Catherine R. Jutzeler",
"Michael Adamer"
] | 2023-10-06 05:43:50 | http://arxiv.org/abs/2310.04028v1 | http://arxiv.org/pdf/2310.04028v1 | 2310.04028v1 |
Demystifying Embedding Spaces using Large Language Models | Embeddings have become a pivotal means to represent complex, multi-faceted
information about entities, concepts, and relationships in a condensed and
useful format. Nevertheless, they often preclude direct interpretation. While
downstream tasks make use of these compressed representations, meaningful
interpretation usually requires visualization using dimensionality reduction or
specialized machine learning interpretability methods. This paper addresses the
challenge of making such embeddings more interpretable and broadly useful, by
employing Large Language Models (LLMs) to directly interact with embeddings --
transforming abstract vectors into understandable narratives. By injecting
embeddings into LLMs, we enable querying and exploration of complex embedding
data. We demonstrate our approach on a variety of diverse tasks, including:
enhancing concept activation vectors (CAVs), communicating novel embedded
entities, and decoding user preferences in recommender systems. Our work
couples the immense information potential of embeddings with the interpretative
power of LLMs. | [
"Guy Tennenholtz",
"Yinlam Chow",
"Chih-Wei Hsu",
"Jihwan Jeong",
"Lior Shani",
"Azamat Tulepbergenov",
"Deepak Ramachandran",
"Martin Mladenov",
"Craig Boutilier"
] | 2023-10-06 05:27:28 | http://arxiv.org/abs/2310.04475v1 | http://arxiv.org/pdf/2310.04475v1 | 2310.04475v1 |
PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps | Developing and discovering new drugs is a complex and resource-intensive
endeavor that often involves substantial costs, time investment, and safety
concerns. A key aspect of drug discovery involves identifying novel drug-target
(DT) interactions. Existing computational methods for predicting DT
interactions have primarily focused on binary classification tasks, aiming to
determine whether a DT pair interacts or not. However, protein-ligand
interactions exhibit a continuum of binding strengths, known as binding
affinity, presenting a persistent challenge for accurate prediction. In this
study, we investigate various techniques employed in Drug Target Interaction
(DTI) prediction and propose novel enhancements to enhance their performance.
Our approaches include the integration of Protein Language Models (PLMs) and
the incorporation of Contact Map information as an inductive bias within
current models. Through extensive experimentation, we demonstrate that our
proposed approaches outperform the baseline models considered in this study,
presenting a compelling case for further development in this direction. We
anticipate that the insights gained from this work will significantly narrow
the search space for potential drugs targeting specific proteins, thereby
accelerating drug discovery. Code and data for PGraphDTA are available at
https://anonymous.4open.science/r/PGraphDTA. | [
"Rakesh Bal",
"Yijia Xiao",
"Wei Wang"
] | 2023-10-06 05:00:25 | http://arxiv.org/abs/2310.04017v1 | http://arxiv.org/pdf/2310.04017v1 | 2310.04017v1 |
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization | While personalized recommendations systems have become increasingly popular,
ensuring user data protection remains a top concern in the development of these
learning systems. A common approach to enhancing privacy involves training
models using anonymous data rather than individual data. In this paper, we
explore a natural technique called \emph{look-alike clustering}, which involves
replacing sensitive features of individuals with the cluster's average values.
We provide a precise analysis of how training models using anonymous cluster
centers affects their generalization capabilities. We focus on an asymptotic
regime where the size of the training set grows in proportion to the features
dimension. Our analysis is based on the Convex Gaussian Minimax Theorem (CGMT)
and allows us to theoretically understand the role of different model
components on the generalization error. In addition, we demonstrate that in
certain high-dimensional regimes, training over anonymous cluster centers acts
as a regularization and improves generalization error of the trained models.
Finally, we corroborate our asymptotic theory with finite-sample numerical
experiments where we observe a perfect match when the sample size is only of
order of a few hundreds. | [
"Adel Javanmard",
"Vahab Mirrokni"
] | 2023-10-06 04:52:46 | http://arxiv.org/abs/2310.04015v2 | http://arxiv.org/pdf/2310.04015v2 | 2310.04015v2 |
Accelerating optimization over the space of probability measures | Acceleration of gradient-based optimization methods is an issue of
significant practical and theoretical interest, particularly in machine
learning applications. Most research has focused on optimization over Euclidean
spaces, but given the need to optimize over spaces of probability measures in
many machine learning problems, it is of interest to investigate accelerated
gradient methods in this context too. To this end, we introduce a
Hamiltonian-flow approach that is analogous to moment-based approaches in
Euclidean space. We demonstrate that algorithms based on this approach can
achieve convergence rates of arbitrarily high order. Numerical examples
illustrate our claim. | [
"Shi Chen",
"Qin Li",
"Oliver Tse",
"Stephen J. Wright"
] | 2023-10-06 04:32:15 | http://arxiv.org/abs/2310.04006v2 | http://arxiv.org/pdf/2310.04006v2 | 2310.04006v2 |
The Role of Federated Learning in a Wireless World with Foundation Models | Foundation models (FMs) are general-purpose artificial intelligence (AI)
models that have recently enabled multiple brand-new generative AI
applications. The rapid advances in FMs serve as an important contextual
backdrop for the vision of next-generation wireless networks, where federated
learning (FL) is a key enabler of distributed network intelligence. Currently,
the exploration of the interplay between FMs and FL is still in its nascent
stage. Naturally, FMs are capable of boosting the performance of FL, and FL
could also leverage decentralized data and computing resources to assist in the
training of FMs. However, the exceptionally high requirements that FMs have for
computing resources, storage, and communication overhead would pose critical
challenges to FL-enabled wireless networks. In this article, we explore the
extent to which FMs are suitable for FL over wireless networks, including a
broad overview of research challenges and opportunities. In particular, we
discuss multiple new paradigms for realizing future intelligent networks that
integrate FMs and FL. We also consolidate several broad research directions
associated with these paradigms. | [
"Zihan Chen",
"Howard H. Yang",
"Y. C. Tay",
"Kai Fong Ernest Chong",
"Tony Q. S. Quek"
] | 2023-10-06 04:13:10 | http://arxiv.org/abs/2310.04003v1 | http://arxiv.org/pdf/2310.04003v1 | 2310.04003v1 |
Runtime Monitoring DNN-Based Perception | Deep neural networks (DNNs) are instrumental in realizing complex perception
systems. As many of these applications are safety-critical by design,
engineering rigor is required to ensure that the functional insufficiency of
the DNN-based perception is not the source of harm. In addition to conventional
static verification and testing techniques employed during the design phase,
there is a need for runtime verification techniques that can detect critical
events, diagnose issues, and even enforce requirements. This tutorial aims to
provide readers with a glimpse of techniques proposed in the literature. We
start with classical methods proposed in the machine learning community, then
highlight a few techniques proposed by the formal methods community. While we
surely can observe similarities in the design of monitors, how the decision
boundaries are created vary between the two communities. We conclude by
highlighting the need to rigorously design monitors, where data availability
outside the operational domain plays an important role. | [
"Chih-Hong Cheng",
"Michael Luttenberger",
"Rongjie Yan"
] | 2023-10-06 03:57:56 | http://arxiv.org/abs/2310.03999v1 | http://arxiv.org/pdf/2310.03999v1 | 2310.03999v1 |
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation | Multimodal learning seeks to utilize data from multiple sources to improve
the overall performance of downstream tasks. It is desirable for redundancies
in the data to make multimodal systems robust to missing or corrupted
observations in some correlated modalities. However, we observe that the
performance of several existing multimodal networks significantly deteriorates
if one or multiple modalities are absent at test time. To enable robustness to
missing modalities, we propose simple and parameter-efficient adaptation
procedures for pretrained multimodal networks. In particular, we exploit
low-rank adaptation and modulation of intermediate features to compensate for
the missing modalities. We demonstrate that such adaptation can partially
bridge performance drop due to missing modalities and outperform independent,
dedicated networks trained for the available modality combinations in some
cases. The proposed adaptation requires extremely small number of parameters
(e.g., fewer than 0.7% of the total parameters in most experiments). We conduct
a series of experiments to highlight the robustness of our proposed method
using diverse datasets for RGB-thermal and RGB-Depth semantic segmentation,
multimodal material segmentation, and multimodal sentiment analysis tasks. Our
proposed method demonstrates versatility across various tasks and datasets, and
outperforms existing methods for robust multimodal learning with missing
modalities. | [
"Md Kaykobad Reza",
"Ashley Prater-Bennette",
"M. Salman Asif"
] | 2023-10-06 03:04:21 | http://arxiv.org/abs/2310.03986v2 | http://arxiv.org/pdf/2310.03986v2 | 2310.03986v2 |
Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder | Dementia diagnosis requires a series of different testing methods, which is
complex and time-consuming. Early detection of dementia is crucial as it can
prevent further deterioration of the condition. This paper utilizes a speech
recognition model to construct a dementia assessment system tailored for
Mandarin speakers during the picture description task. By training an
attention-based speech recognition model on voice data closely resembling
real-world scenarios, we have significantly enhanced the model's recognition
capabilities. Subsequently, we extracted the encoder from the speech
recognition model and added a linear layer for dementia assessment. We
collected Mandarin speech data from 99 subjects and acquired their clinical
assessments from a local hospital. We achieved an accuracy of 92.04% in
Alzheimer's disease detection and a mean absolute error of 9% in clinical
dementia rating score prediction. | [
"Zih-Jyun Lin",
"Yi-Ju Chen",
"Po-Chih Kuo",
"Likai Huang",
"Chaur-Jong Hu",
"Cheng-Yu Chen"
] | 2023-10-06 03:04:11 | http://arxiv.org/abs/2310.03985v1 | http://arxiv.org/pdf/2310.03985v1 | 2310.03985v1 |
AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement | Growing attention has been paid to Reinforcement Learning (RL) algorithms
when optimizing long-term user engagement in sequential recommendation tasks.
One challenge in large-scale online recommendation systems is the constant and
complicated changes in users' behavior patterns, such as interaction rates and
retention tendencies. When formulated as a Markov Decision Process (MDP), the
dynamics and reward functions of the recommendation system are continuously
affected by these changes. Existing RL algorithms for recommendation systems
will suffer from distribution shift and struggle to adapt in such an MDP. In
this paper, we introduce a novel paradigm called Adaptive Sequential
Recommendation (AdaRec) to address this issue. AdaRec proposes a new
distance-based representation loss to extract latent information from users'
interaction trajectories. Such information reflects how RL policy fits to
current user behavior patterns, and helps the policy to identify subtle changes
in the recommendation system. To make rapid adaptation to these changes, AdaRec
encourages exploration with the idea of optimism under uncertainty. The
exploration is further guarded by zero-order action optimization to ensure
stable recommendation quality in complicated environments. We conduct extensive
empirical analyses in both simulator-based and live sequential recommendation
tasks, where AdaRec exhibits superior long-term performance compared to all
baseline algorithms. | [
"Zhenghai Xue",
"Qingpeng Cai",
"Tianyou Zuo",
"Bin Yang",
"Lantao Hu",
"Peng Jiang",
"Kun Gai",
"Bo An"
] | 2023-10-06 02:45:21 | http://arxiv.org/abs/2310.03984v1 | http://arxiv.org/pdf/2310.03984v1 | 2310.03984v1 |
Hierarchical Multi-Marginal Optimal Transport for Network Alignment | Finding node correspondence across networks, namely multi-network alignment,
is an essential prerequisite for joint learning on multiple networks. Despite
great success in aligning networks in pairs, the literature on multi-network
alignment is sparse due to the exponentially growing solution space and lack of
high-order discrepancy measures. To fill this gap, we propose a hierarchical
multi-marginal optimal transport framework named HOT for multi-network
alignment. To handle the large solution space, multiple networks are decomposed
into smaller aligned clusters via the fused Gromov-Wasserstein (FGW)
barycenter. To depict high-order relationships across multiple networks, the
FGW distance is generalized to the multi-marginal setting, based on which
networks can be aligned jointly. A fast proximal point method is further
developed with guaranteed convergence to a local optimum. Extensive experiments
and analysis show that our proposed HOT achieves significant improvements over
the state-of-the-art in both effectiveness and scalability. | [
"Zhichen Zeng",
"Boxin Du",
"Si Zhang",
"Yinglong Xia",
"Zhining Liu",
"Hanghang Tong"
] | 2023-10-06 02:35:35 | http://arxiv.org/abs/2310.04470v1 | http://arxiv.org/pdf/2310.04470v1 | 2310.04470v1 |
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation | While pre-training on object detection tasks, such as Common Objects in
Contexts (COCO) [1], could significantly boost the performance of cell
segmentation, it still consumes on massive fine-annotated cell images [2] with
bounding boxes, masks, and cell types for every cell in every image, to
fine-tune the pre-trained model. To lower the cost of annotation, this work
considers the problem of pre-training DNN models for few-shot cell
segmentation, where massive unlabeled cell images are available but only a
small proportion is annotated. Hereby, we propose Cross-domain Unsupervised
Pre-training, namely CUPre, transferring the capability of object detection and
instance segmentation for common visual objects (learned from COCO) to the
visual domain of cells using unlabeled images. Given a standard COCO
pre-trained network with backbone, neck, and head modules, CUPre adopts an
alternate multi-task pre-training (AMT2) procedure with two sub-tasks -- in
every iteration of pre-training, AMT2 first trains the backbone with cell
images from multiple cell datasets via unsupervised momentum contrastive
learning (MoCo) [3], and then trains the whole model with vanilla COCO datasets
via instance segmentation. After pre-training, CUPre fine-tunes the whole model
on the cell segmentation task using a few annotated images. We carry out
extensive experiments to evaluate CUPre using LIVECell [2] and BBBC038 [4]
datasets in few-shot instance segmentation settings. The experiment shows that
CUPre can outperform existing pre-training methods, achieving the highest
average precision (AP) for few-shot cell segmentation and detection. | [
"Weibin Liao",
"Xuhong Li",
"Qingzhong Wang",
"Yanwu Xu",
"Zhaozheng Yin",
"Haoyi Xiong"
] | 2023-10-06 02:35:31 | http://arxiv.org/abs/2310.03981v1 | http://arxiv.org/pdf/2310.03981v1 | 2310.03981v1 |
Perfect Alignment May be Poisonous to Graph Contrastive Learning | Graph Contrastive Learning (GCL) aims to learn node representations by
aligning positive pairs and separating negative ones. However, limited research
has been conducted on the inner law behind specific augmentations used in
graph-based learning. What kind of augmentation will help downstream
performance, how does contrastive learning actually influence downstream tasks,
and why the magnitude of augmentation matters? This paper seeks to address
these questions by establishing a connection between augmentation and
downstream performance, as well as by investigating the generalization of
contrastive learning. Our findings reveal that GCL contributes to downstream
tasks mainly by separating different classes rather than gathering nodes of the
same class. So perfect alignment and augmentation overlap which draw all
intra-class samples the same can not explain the success of contrastive
learning. Then in order to comprehend how augmentation aids the contrastive
learning process, we conduct further investigations into its generalization,
finding that perfect alignment that draw positive pair the same could help
contrastive loss but is poisonous to generalization, on the contrary, imperfect
alignment enhances the model's generalization ability. We analyse the result by
information theory and graph spectrum theory respectively, and propose two
simple but effective methods to verify the theories. The two methods could be
easily applied to various GCL algorithms and extensive experiments are
conducted to prove its effectiveness. | [
"Jingyu Liu",
"Huayi Tang",
"Yong Liu"
] | 2023-10-06 02:22:49 | http://arxiv.org/abs/2310.03977v1 | http://arxiv.org/pdf/2310.03977v1 | 2310.03977v1 |
Ultimate limit on learning non-Markovian behavior: Fisher information rate and excess information | We address the fundamental limits of learning unknown parameters of any
stochastic process from time-series data, and discover exact closed-form
expressions for how optimal inference scales with observation length. Given a
parametrized class of candidate models, the Fisher information of observed
sequence probabilities lower-bounds the variance in model estimation from
finite data. As sequence-length increases, the minimal variance scales as the
square inverse of the length -- with constant coefficient given by the
information rate. We discover a simple closed-form expression for this
information rate, even in the case of infinite Markov order. We furthermore
obtain the exact analytic lower bound on model variance from the
observation-induced metadynamic among belief states. We discover ephemeral,
exponential, and more general modes of convergence to the asymptotic
information rate. Surprisingly, this myopic information rate converges to the
asymptotic Fisher information rate with exactly the same relaxation timescales
that appear in the myopic entropy rate as it converges to the Shannon entropy
rate for the process. We illustrate these results with a sequence of examples
that highlight qualitatively distinct features of stochastic processes that
shape optimal learning. | [
"Paul M. Riechers"
] | 2023-10-06 01:53:42 | http://arxiv.org/abs/2310.03968v1 | http://arxiv.org/pdf/2310.03968v1 | 2310.03968v1 |
A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis | To understand the biological characteristics of neurological disorders with
functional connectivity (FC), recent studies have widely utilized deep
learning-based models to identify the disease and conducted post-hoc analyses
via explainable models to discover disease-related biomarkers. Most existing
frameworks consist of three stages, namely, feature selection, feature
extraction for classification, and analysis, where each stage is implemented
separately. However, if the results at each stage lack reliability, it can
cause misdiagnosis and incorrect analysis in afterward stages. In this study,
we propose a novel unified framework that systemically integrates diagnoses
(i.e., feature selection and feature extraction) and explanations. Notably, we
devised an adaptive attention network as a feature selection approach to
identify individual-specific disease-related connections. We also propose a
functional network relational encoder that summarizes the global topological
properties of FC by learning the inter-network relations without pre-defined
edges between functional networks. Last but not least, our framework provides a
novel explanatory power for neuroscientific interpretation, also termed
counter-condition analysis. We simulated the FC that reverses the diagnostic
information (i.e., counter-condition FC): converting a normal brain to be
abnormal and vice versa. We validated the effectiveness of our framework by
using two large resting-state functional magnetic resonance imaging (fMRI)
datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and
demonstrated that our framework outperforms other competing methods for disease
identification. Furthermore, we analyzed the disease-related neurological
patterns based on counter-condition analysis. | [
"Eunsong Kang",
"Da-woon Heo",
"Jiwon Lee",
"Heung-Il Suk"
] | 2023-10-06 01:33:47 | http://arxiv.org/abs/2310.03964v1 | http://arxiv.org/pdf/2310.03964v1 | 2310.03964v1 |
Understanding prompt engineering may not require rethinking generalization | Zero-shot learning in prompted vision-language models, the practice of
crafting prompts to build classifiers without an explicit training process, has
achieved impressive performance in many settings. This success presents a
seemingly surprising observation: these methods suffer relatively little from
overfitting, i.e., when a prompt is manually engineered to achieve low error on
a given training set (thus rendering the method no longer actually zero-shot),
the approach still performs well on held-out test data. In this paper, we show
that we can explain such performance well via recourse to classical PAC-Bayes
bounds. Specifically, we show that the discrete nature of prompts, combined
with a PAC-Bayes prior given by a language model, results in generalization
bounds that are remarkably tight by the standards of the literature: for
instance, the generalization bound of an ImageNet classifier is often within a
few percentage points of the true test error. We demonstrate empirically that
this holds for existing handcrafted prompts and prompts generated through
simple greedy search. Furthermore, the resulting bound is well-suited for model
selection: the models with the best bound typically also have the best test
performance. This work thus provides a possible justification for the
widespread practice of prompt engineering, even if it seems that such methods
could potentially overfit the training data. | [
"Victor Akinwande",
"Yiding Jiang",
"Dylan Sam",
"J. Zico Kolter"
] | 2023-10-06 00:52:48 | http://arxiv.org/abs/2310.03957v1 | http://arxiv.org/pdf/2310.03957v1 | 2310.03957v1 |
Improved prediction of ligand-protein binding affinities by meta-modeling | The accurate screening of candidate drug ligands against target proteins
through computational approaches is of prime interest to drug development
efforts, as filtering potential candidates would save time and expenses for
finding drugs. Such virtual screening depends in part on methods to predict the
binding affinity between ligands and proteins. Given many computational models
for binding affinity prediction with varying results across targets, we herein
develop a meta-modeling framework by integrating published empirical
structure-based docking and sequence-based deep learning models. In building
this framework, we evaluate many combinations of individual models, training
databases, and linear and nonlinear meta-modeling approaches. We show that many
of our meta-models significantly improve affinity predictions over individual
base models. Our best meta-models achieve comparable performance to
state-of-the-art exclusively structure-based deep learning tools. Overall, we
demonstrate that diverse modeling approaches can be ensembled together to gain
substantial improvement in binding affinity prediction while allowing control
over input features such as physicochemical properties or molecular
descriptors. | [
"Ho-Joon Lee",
"Prashant S. Emani",
"Mark B. Gerstein"
] | 2023-10-05 23:46:45 | http://arxiv.org/abs/2310.03946v1 | http://arxiv.org/pdf/2310.03946v1 | 2310.03946v1 |
On Wasserstein distances for affine transformations of random vectors | We expound on some known lower bounds of the quadratic Wasserstein distance
between random vectors in $\mathbb{R}^n$ with an emphasis on affine
transformations that have been used in manifold learning of data in Wasserstein
space. In particular, we give concrete lower bounds for rotated copies of
random vectors in $\mathbb{R}^2$ with uncorrelated components by computing the
Bures metric between the covariance matrices. We also derive upper bounds for
compositions of affine maps which yield a fruitful variety of diffeomorphisms
applied to an initial data measure. We apply these bounds to various
distributions including those lying on a 1-dimensional manifold in
$\mathbb{R}^2$ and illustrate the quality of the bounds. Finally, we give a
framework for mimicking handwritten digit or alphabet datasets that can be
applied in a manifold learning framework. | [
"Keaton Hamm",
"Andrzej Korzeniowski"
] | 2023-10-05 23:30:41 | http://arxiv.org/abs/2310.03945v1 | http://arxiv.org/pdf/2310.03945v1 | 2310.03945v1 |
LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics | The COVID-19 pandemic has accentuated socioeconomic disparities across
various racial and ethnic groups in the United States. While previous studies
have utilized traditional survey methods like the Household Pulse Survey (HPS)
to elucidate these disparities, this paper explores the role of social media
platforms in both highlighting and addressing these challenges. Drawing from
real-time data sourced from Twitter, we analyzed language patterns related to
four major types of adverse experiences: loss of employment income (LI), food
scarcity (FS), housing insecurity (HI), and unmet needs for mental health
services (UM). We first formulate a sparsity optimization problem that extracts
low-level language features from social media data sources. Second, we propose
novel constraints on feature similarity exploiting prior knowledge about the
similarity of the language patterns among the adverse experiences. The proposed
problem is challenging to solve due to the non-convexity objective and
non-smoothness penalties. We develop an algorithm based on the alternating
direction method of multipliers (ADMM) framework to solve the proposed
formulation. Extensive experiments and comparisons to other models on
real-world social media and the detection of adverse experiences justify the
efficacy of our model. | [
"Kaiqun Fu",
"Yangxiao Bai",
"Weiwei Zhang",
"Deepthi Kolady"
] | 2023-10-05 23:09:31 | http://arxiv.org/abs/2310.03941v1 | http://arxiv.org/pdf/2310.03941v1 | 2310.03941v1 |
Improving classifier decision boundaries using nearest neighbors | Neural networks are not learning optimal decision boundaries. We show that
decision boundaries are situated in areas of low training data density. They
are impacted by few training samples which can easily lead to overfitting. We
provide a simple algorithm performing a weighted average of the prediction of a
sample and its nearest neighbors' (computed in latent space) leading to a minor
favorable outcomes for a variety of important measures for neural networks. In
our evaluation, we employ various self-trained and pre-trained convolutional
neural networks to show that our approach improves (i) resistance to label
noise, (ii) robustness against adversarial attacks, (iii) classification
accuracy, and to some degree even (iv) interpretability. While improvements are
not necessarily large in all four areas, our approach is conceptually simple,
i.e., improvements come without any modification to network architecture,
training procedure or dataset. Furthermore, they are in stark contrast to prior
works that often require trade-offs among the four objectives or provide
valuable, but non-actionable insights. | [
"Johannes Schneider"
] | 2023-10-05 22:11:52 | http://arxiv.org/abs/2310.03927v1 | http://arxiv.org/pdf/2310.03927v1 | 2310.03927v1 |
Multitask Learning for Time Series Data with 2D Convolution | Multitask learning (MTL) aims to develop a unified model that can handle a
set of closely related tasks simultaneously. By optimizing the model across
multiple tasks, MTL generally surpasses its non-MTL counterparts in terms of
generalizability. Although MTL has been extensively researched in various
domains such as computer vision, natural language processing, and
recommendation systems, its application to time series data has received
limited attention. In this paper, we investigate the application of MTL to the
time series classification (TSC) problem. However, when we integrate the
state-of-the-art 1D convolution-based TSC model with MTL, the performance of
the TSC model actually deteriorates. By comparing the 1D convolution-based
models with the Dynamic Time Warping (DTW) distance function, it appears that
the underwhelming results stem from the limited expressive power of the 1D
convolutional layers. To overcome this challenge, we propose a novel design for
a 2D convolution-based model that enhances the model's expressiveness.
Leveraging this advantage, our proposed method outperforms competing approaches
on both the UCR Archive and an industrial transaction TSC dataset. | [
"Chin-Chia Michael Yeh",
"Xin Dai",
"Yan Zheng",
"Junpeng Wang",
"Huiyuan Chen",
"Yujie Fan",
"Audrey Der",
"Zhongfang Zhuang",
"Liang Wang",
"Wei Zhang"
] | 2023-10-05 22:00:17 | http://arxiv.org/abs/2310.03925v2 | http://arxiv.org/pdf/2310.03925v2 | 2310.03925v2 |
An Efficient Content-based Time Series Retrieval System | A Content-based Time Series Retrieval (CTSR) system is an information
retrieval system for users to interact with time series emerged from multiple
domains, such as finance, healthcare, and manufacturing. For example, users
seeking to learn more about the source of a time series can submit the time
series as a query to the CTSR system and retrieve a list of relevant time
series with associated metadata. By analyzing the retrieved metadata, users can
gather more information about the source of the time series. Because the CTSR
system is required to work with time series data from diverse domains, it needs
a high-capacity model to effectively measure the similarity between different
time series. On top of that, the model within the CTSR system has to compute
the similarity scores in an efficient manner as the users interact with the
system in real-time. In this paper, we propose an effective and efficient CTSR
model that outperforms alternative models, while still providing reasonable
inference runtimes. To demonstrate the capability of the proposed method in
solving business problems, we compare it against alternative models using our
in-house transaction data. Our findings reveal that the proposed model is the
most suitable solution compared to others for our transaction data problem. | [
"Chin-Chia Michael Yeh",
"Huiyuan Chen",
"Xin Dai",
"Yan Zheng",
"Junpeng Wang",
"Vivian Lai",
"Yujie Fan",
"Audrey Der",
"Zhongfang Zhuang",
"Liang Wang",
"Wei Zhang",
"Jeff M. Phillips"
] | 2023-10-05 21:52:19 | http://arxiv.org/abs/2310.03919v1 | http://arxiv.org/pdf/2310.03919v1 | 2310.03919v1 |
Toward a Foundation Model for Time Series Data | A foundation model is a machine learning model trained on a large and diverse
set of data, typically using self-supervised learning-based pre-training
techniques, that can be adapted to various downstream tasks. However, current
research on time series pre-training has mostly focused on models pre-trained
solely on data from a single domain, resulting in a lack of knowledge about
other types of time series. However, current research on time series
pre-training has predominantly focused on models trained exclusively on data
from a single domain. As a result, these models possess domain-specific
knowledge that may not be easily transferable to time series from other
domains. In this paper, we aim to develop an effective time series foundation
model by leveraging unlabeled samples from multiple domains. To achieve this,
we repurposed the publicly available UCR Archive and evaluated four existing
self-supervised learning-based pre-training methods, along with a novel method,
on the datasets. We tested these methods using four popular neural network
architectures for time series to understand how the pre-training methods
interact with different network designs. Our experimental results show that
pre-training improves downstream classification tasks by enhancing the
convergence of the fine-tuning process. Furthermore, we found that the proposed
pre-training method, when combined with the Transformer model, outperforms the
alternatives. | [
"Chin-Chia Michael Yeh",
"Xin Dai",
"Huiyuan Chen",
"Yan Zheng",
"Yujie Fan",
"Audrey Der",
"Vivian Lai",
"Zhongfang Zhuang",
"Junpeng Wang",
"Liang Wang",
"Wei Zhang"
] | 2023-10-05 21:44:50 | http://arxiv.org/abs/2310.03916v1 | http://arxiv.org/pdf/2310.03916v1 | 2310.03916v1 |
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control | Developing autonomous agents that can interact with changing environments is
an open challenge in machine learning. Robustness is particularly important in
these settings as agents are often fit offline on expert demonstrations but
deployed online where they must generalize to the closed feedback loop within
the environment. In this work, we explore the application of recurrent neural
networks to tasks of this nature and understand how a parameterization of their
recurrent connectivity influences robustness in closed-loop settings.
Specifically, we represent the recurrent connectivity as a function of rank and
sparsity and show both theoretically and empirically that modulating these two
variables has desirable effects on network dynamics. The proposed low-rank,
sparse connectivity induces an interpretable prior on the network that proves
to be most amenable for a class of models known as closed-form continuous-time
neural networks (CfCs). We find that CfCs with fewer parameters can outperform
their full-rank, fully-connected counterparts in the online setting under
distribution shift. This yields memory-efficient and robust agents while
opening a new perspective on how we can modulate network dynamics through
connectivity. | [
"Neehal Tumma",
"Mathias Lechner",
"Noel Loo",
"Ramin Hasani",
"Daniela Rus"
] | 2023-10-05 21:44:18 | http://arxiv.org/abs/2310.03915v1 | http://arxiv.org/pdf/2310.03915v1 | 2310.03915v1 |
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels | Bayesian Optimization (BO), guided by Gaussian process (GP) surrogates, has
proven to be an invaluable technique for efficient, high-dimensional, black-box
optimization, a critical problem inherent to many applications such as
industrial design and scientific computing. Recent contributions have
introduced reinforcement learning (RL) to improve the optimization performance
on both single function optimization and \textit{few-shot} multi-objective
optimization. However, even few-shot techniques fail to exploit similarities
shared between closely related objectives. In this paper, we combine recent
developments in Deep Kernel Learning (DKL) and attention-based Transformer
models to improve the modeling powers of GP surrogates with meta-learning. We
propose a novel method for improving meta-learning BO surrogates by
incorporating attention mechanisms into DKL, empowering the surrogates to adapt
to contextual information gathered during the BO process. We combine this
Transformer Deep Kernel with a learned acquisition function trained with
continuous Soft Actor-Critic Reinforcement Learning to aid in exploration. This
Reinforced Transformer Deep Kernel (RTDK-BO) approach yields state-of-the-art
results in continuous high-dimensional optimization problems. | [
"Alexander Shmakov",
"Avisek Naug",
"Vineet Gundecha",
"Sahand Ghorbanpour",
"Ricardo Luna Gutierrez",
"Ashwin Ramesh Babu",
"Antonio Guillen",
"Soumyendu Sarkar"
] | 2023-10-05 21:37:20 | http://arxiv.org/abs/2310.03912v2 | http://arxiv.org/pdf/2310.03912v2 | 2310.03912v2 |
PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability | The increasing global emphasis on sustainability and reducing carbon
emissions is pushing governments and corporations to rethink their approach to
data center design and operation. Given their high energy consumption and
exponentially large computational workloads, data centers are prime candidates
for optimizing power consumption, especially in areas such as cooling and IT
energy usage. A significant challenge in this pursuit is the lack of a
configurable and scalable thermal data center model that offers an end-to-end
pipeline. Data centers consist of multiple IT components whose geometric
configuration and heat dissipation make thermal modeling difficult. This paper
presents PyDCM, a customizable Data Center Model implemented in Python, that
allows users to create unique configurations of IT equipment with custom server
specifications and geometric arrangements of IT cabinets. The use of vectorized
thermal calculations makes PyDCM orders of magnitude faster (30 times) than
current Energy Plus modeling implementations and scales sublinearly with the
number of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning via
the Gymnasium wrapper to optimize data center cooling and offers a
user-friendly platform for testing various data center design prototypes. | [
"Avisek Naug",
"Antonio Guillen",
"Ricardo Luna Gutiérrez",
"Vineet Gundecha",
"Dejan Markovikj",
"Lekhapriya Dheeraj Kashyap",
"Lorenz Krause",
"Sahand Ghorbanpour",
"Sajad Mousavi",
"Ashwin Ramesh Babu",
"Soumyendu Sarkar"
] | 2023-10-05 21:24:54 | http://arxiv.org/abs/2310.03906v3 | http://arxiv.org/pdf/2310.03906v3 | 2310.03906v3 |
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond | Recent research has developed several Monte Carlo methods for estimating the
normalization constant (partition function) based on the idea of annealing.
This means sampling successively from a path of distributions that interpolate
between a tractable "proposal" distribution and the unnormalized "target"
distribution. Prominent estimators in this family include annealed importance
sampling and annealed noise-contrastive estimation (NCE). Such methods hinge on
a number of design choices: which estimator to use, which path of distributions
to use and whether to use a path at all; so far, there is no definitive theory
on which choices are efficient. Here, we evaluate each design choice by the
asymptotic estimation error it produces. First, we show that using NCE is more
efficient than the importance sampling estimator, but in the limit of
infinitesimal path steps, the difference vanishes. Second, we find that using
the geometric path brings down the estimation error from an exponential to a
polynomial function of the parameter distance between the target and proposal
distributions. Third, we find that the arithmetic path, while rarely used, can
offer optimality properties over the universally-used geometric path. In fact,
in a particular limit, the optimal path is arithmetic. Based on this theory, we
finally propose a two-step estimator to approximate the optimal path in an
efficient way. | [
"Omar Chehab",
"Aapo Hyvarinen",
"Andrej Risteski"
] | 2023-10-05 21:16:55 | http://arxiv.org/abs/2310.03902v2 | http://arxiv.org/pdf/2310.03902v2 | 2310.03902v2 |
CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention | Determining the structure of a protein has been a decades-long open question.
A protein's three-dimensional structure often poses nontrivial computation
costs, when classical simulation algorithms are utilized. Advances in the
transformer neural network architecture -- such as AlphaFold2 -- achieve
significant improvements for this problem, by learning from a large dataset of
sequence information and corresponding protein structures. Yet, such methods
only focus on sequence information; other available prior knowledge, such as
protein crystallography and partial structure of amino acids, could be
potentially utilized. To the best of our knowledge, we propose the first
transformer-based model that directly utilizes protein crystallography and
partial structure information to predict the electron density maps of proteins.
Via two new datasets of peptide fragments (2-residue and 15-residue) , we
demonstrate our method, dubbed \texttt{CrysFormer}, can achieve accurate
predictions, based on a much smaller dataset size and with reduced computation
costs. | [
"Chen Dun",
"Qiutai Pan",
"Shikai Jin",
"Ria Stevens",
"Mitchell D. Miller",
"George N. Phillips, Jr.",
"Anastasios Kyrillidis"
] | 2023-10-05 21:10:22 | http://arxiv.org/abs/2310.03899v1 | http://arxiv.org/pdf/2310.03899v1 | 2310.03899v1 |
Class-Incremental Learning Using Generative Experience Replay Based on Time-aware Regularization | Learning new tasks accumulatively without forgetting remains a critical
challenge in continual learning. Generative experience replay addresses this
challenge by synthesizing pseudo-data points for past learned tasks and later
replaying them for concurrent training along with the new tasks' data.
Generative replay is the best strategy for continual learning under a strict
class-incremental setting when certain constraints need to be met: (i) constant
model size, (ii) no pre-training dataset, and (iii) no memory buffer for
storing past tasks' data. Inspired by the biological nervous system mechanisms,
we introduce a time-aware regularization method to dynamically fine-tune the
three training objective terms used for generative replay: supervised learning,
latent regularization, and data reconstruction. Experimental results on major
benchmarks indicate that our method pushes the limit of brain-inspired
continual learners under such strict settings, improves memory retention, and
increases the average performance over continually arriving tasks. | [
"Zizhao Hu",
"Mohammad Rostami"
] | 2023-10-05 21:07:45 | http://arxiv.org/abs/2310.03898v1 | http://arxiv.org/pdf/2310.03898v1 | 2310.03898v1 |
Taming Binarized Neural Networks and Mixed-Integer Programs | There has been a great deal of recent interest in binarized neural networks,
especially because of their explainability. At the same time, automatic
differentiation algorithms such as backpropagation fail for binarized neural
networks, which limits their applicability. By reformulating the problem of
training binarized neural networks as a subadditive dual of a mixed-integer
program, we show that binarized neural networks admit a tame representation.
This, in turn, makes it possible to use the framework of Bolte et al. for
implicit differentiation, which offers the possibility for practical
implementation of backpropagation in the context of binarized neural networks.
This approach could also be used for a broader class of mixed-integer programs,
beyond the training of binarized neural networks, as encountered in symbolic
approaches to AI and beyond. | [
"Johannes Aspman",
"Georgios Korpas",
"Jakub Marecek"
] | 2023-10-05 21:04:16 | http://arxiv.org/abs/2310.04469v1 | http://arxiv.org/pdf/2310.04469v1 | 2310.04469v1 |
Accelerated Neural Network Training with Rooted Logistic Objectives | Many neural networks deployed in the real world scenarios are trained using
cross entropy based loss functions. From the optimization perspective, it is
known that the behavior of first order methods such as gradient descent
crucially depend on the separability of datasets. In fact, even in the most
simplest case of binary classification, the rate of convergence depends on two
factors: (1) condition number of data matrix, and (2) separability of the
dataset. With no further pre-processing techniques such as
over-parametrization, data augmentation etc., separability is an intrinsic
quantity of the data distribution under consideration. We focus on the
landscape design of the logistic function and derive a novel sequence of {\em
strictly} convex functions that are at least as strict as logistic loss. The
minimizers of these functions coincide with those of the minimum norm solution
wherever possible. The strict convexity of the derived function can be extended
to finetune state-of-the-art models and applications. In empirical experimental
analysis, we apply our proposed rooted logistic objective to multiple deep
models, e.g., fully-connected neural networks and transformers, on various of
classification benchmarks. Our results illustrate that training with rooted
loss function is converged faster and gains performance improvements.
Furthermore, we illustrate applications of our novel rooted loss function in
generative modeling based downstream applications, such as finetuning StyleGAN
model with the rooted loss. The code implementing our losses and models can be
found here for open source software development purposes:
https://anonymous.4open.science/r/rooted_loss. | [
"Zhu Wang",
"Praveen Raj Veluswami",
"Harsh Mishra",
"Sathya N. Ravi"
] | 2023-10-05 20:49:48 | http://arxiv.org/abs/2310.03890v1 | http://arxiv.org/pdf/2310.03890v1 | 2310.03890v1 |
Information Geometry for the Working Information Theorist | Information geometry is a study of statistical manifolds, that is, spaces of
probability distributions from a geometric perspective. Its classical
information-theoretic applications relate to statistical concepts such as
Fisher information, sufficient statistics, and efficient estimators. Today,
information geometry has emerged as an interdisciplinary field that finds
applications in diverse areas such as radar sensing, array signal processing,
quantum physics, deep learning, and optimal transport. This article presents an
overview of essential information geometry to initiate an information theorist,
who may be unfamiliar with this exciting area of research. We explain the
concepts of divergences on statistical manifolds, generalized notions of
distances, orthogonality, and geodesics, thereby paving the way for concrete
applications and novel theoretical investigations. We also highlight some
recent information-geometric developments, which are of interest to the broader
information theory community. | [
"Kumar Vijay Mishra",
"M. Ashok Kumar",
"Ting-Kam Leonard Wong"
] | 2023-10-05 20:36:10 | http://arxiv.org/abs/2310.03884v1 | http://arxiv.org/pdf/2310.03884v1 | 2310.03884v1 |
Small batch deep reinforcement learning | In value-based deep reinforcement learning with replay memories, the batch
size parameter specifies how many transitions to sample for each gradient
update. Although critical to the learning process, this value is typically not
adjusted when proposing new algorithms. In this work we present a broad
empirical study that suggests {\em reducing} the batch size can result in a
number of significant performance gains; this is surprising, as the general
tendency when training neural networks is towards larger batch sizes for
improved performance. We complement our experimental findings with a set of
empirical analyses towards better understanding this phenomenon. | [
"Johan Obando-Ceron",
"Marc G. Bellemare",
"Pablo Samuel Castro"
] | 2023-10-05 20:31:37 | http://arxiv.org/abs/2310.03882v1 | http://arxiv.org/pdf/2310.03882v1 | 2310.03882v1 |
Non Commutative Convolutional Signal Models in Neural Networks: Stability to Small Deformations | In this paper we discuss the results recently published in~[1] about
algebraic signal models (ASMs) based on non commutative algebras and their use
in convolutional neural networks. Relying on the general tools from algebraic
signal processing (ASP), we study the filtering and stability properties of non
commutative convolutional filters. We show how non commutative filters can be
stable to small perturbations on the space of operators. We also show that
although the spectral components of the Fourier representation in a non
commutative signal model are associated to spaces of dimension larger than one,
there is a trade-off between stability and selectivity similar to that observed
for commutative models. Our results have direct implications for group neural
networks, multigraph neural networks and quaternion neural networks, among
other non commutative architectures. We conclude by corroborating these results
through numerical experiments. | [
"Alejandro Parada-Mayorga",
"Landon Butler",
"Alejandro Ribeiro"
] | 2023-10-05 20:27:22 | http://arxiv.org/abs/2310.03879v1 | http://arxiv.org/pdf/2310.03879v1 | 2310.03879v1 |
Model Complexity of Program Phases | In resource limited computing systems, sequence prediction models must
operate under tight constraints. Various models are available that cater to
prediction under these conditions that in some way focus on reducing the cost
of implementation. These resource constrained sequence prediction models, in
practice, exhibit a fundamental tradeoff between the cost of implementation and
the quality of its predictions. This fundamental tradeoff seems to be largely
unexplored for models for different tasks. Here we formulate the necessary
theory and an associated empirical procedure to explore this tradeoff space for
a particular family of machine learning models such as deep neural networks. We
anticipate that the knowledge of the behavior of this tradeoff may be
beneficial in understanding the theoretical and practical limits of creation
and deployment of models for resource constrained tasks. | [
"Arjun Karuvally",
"J. Eliot B. Moss"
] | 2023-10-05 19:50:15 | http://arxiv.org/abs/2310.03865v1 | http://arxiv.org/pdf/2310.03865v1 | 2310.03865v1 |
Variational Barycentric Coordinates | We propose a variational technique to optimize for generalized barycentric
coordinates that offers additional control compared to existing models. Prior
work represents barycentric coordinates using meshes or closed-form formulae,
in practice limiting the choice of objective function. In contrast, we directly
parameterize the continuous function that maps any coordinate in a polytope's
interior to its barycentric coordinates using a neural field. This formulation
is enabled by our theoretical characterization of barycentric coordinates,
which allows us to construct neural fields that parameterize the entire
function class of valid coordinates. We demonstrate the flexibility of our
model using a variety of objective functions, including multiple smoothness and
deformation-aware energies; as a side contribution, we also present
mathematically-justified means of measuring and minimizing objectives like
total variation on discontinuous neural fields. We offer a practical
acceleration strategy, present a thorough validation of our algorithm, and
demonstrate several applications. | [
"Ana Dodik",
"Oded Stein",
"Vincent Sitzmann",
"Justin Solomon"
] | 2023-10-05 19:45:06 | http://arxiv.org/abs/2310.03861v1 | http://arxiv.org/pdf/2310.03861v1 | 2310.03861v1 |
Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks | Contrastive Language-Image Pre-training (CLIP) on large image-caption
datasets has achieved remarkable success in zero-shot classification and
enabled transferability to new domains. However, CLIP is extremely more
vulnerable to targeted data poisoning and backdoor attacks, compared to
supervised learning. Perhaps surprisingly, poisoning 0.0001% of CLIP
pre-training data is enough to make targeted data poisoning attacks successful.
This is four orders of magnitude smaller than what is required to poison
supervised models. Despite this vulnerability, existing methods are very
limited in defending CLIP models during pre-training. In this work, we propose
a strong defense, SAFECLIP, to safely pre-train CLIP against targeted data
poisoning and backdoor attacks. SAFECLIP warms up the model by applying
unimodal contrastive learning (CL) on image and text modalities separately.
Then, it carefully divides the data into safe and risky subsets. SAFECLIP
trains on the risky data by applying unimodal CL to image and text modalities
separately, and trains on the safe data using the CLIP loss. By gradually
increasing the size of the safe subset during the training, SAFECLIP
effectively breaks targeted data poisoning and backdoor attacks without harming
the CLIP performance. Our extensive experiments show that SAFECLIP decrease the
attack success rate of targeted data poisoning attacks from 93.75% to 0% and
that of the backdoor attacks from 100% to 0%, without harming the CLIP
performance on various datasets. | [
"Wenhan Yang",
"Jingdong Gao",
"Baharan Mirzasoleiman"
] | 2023-10-05 19:42:03 | http://arxiv.org/abs/2310.05862v1 | http://arxiv.org/pdf/2310.05862v1 | 2310.05862v1 |
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning | In most works on deep incremental learning research, it is assumed that novel
samples are pre-identified for neural network retraining. However, practical
deep classifiers often misidentify these samples, leading to erroneous
predictions. Such misclassifications can degrade model performance. Techniques
like open set recognition offer a means to detect these novel samples,
representing a significant area in the machine learning domain.
In this paper, we introduce a deep class-incremental learning framework
integrated with open set recognition. Our approach refines class-incrementally
learned features to adapt them for distance-based open set recognition.
Experimental results validate that our method outperforms state-of-the-art
incremental learning techniques and exhibits superior performance in open set
recognition compared to baseline methods. | [
"Jiawen Xu",
"Claas Grohnfeldt",
"Odej Kao"
] | 2023-10-05 19:08:08 | http://arxiv.org/abs/2310.03848v1 | http://arxiv.org/pdf/2310.03848v1 | 2310.03848v1 |
Design Principles for Lifelong Learning AI Accelerators | Lifelong learning - an agent's ability to learn throughout its lifetime - is
a hallmark of biological learning systems and a central challenge for
artificial intelligence (AI). The development of lifelong learning algorithms
could lead to a range of novel AI applications, but this will also require the
development of appropriate hardware accelerators, particularly if the models
are to be deployed on edge platforms, which have strict size, weight, and power
constraints. Here, we explore the design of lifelong learning AI accelerators
that are intended for deployment in untethered environments. We identify key
desirable capabilities for lifelong learning accelerators and highlight metrics
to evaluate such accelerators. We then discuss current edge AI accelerators and
explore the future design of lifelong learning accelerators, considering the
role that different emerging technologies could play. | [
"Dhireesha Kudithipudi",
"Anurag Daram",
"Abdullah M. Zyarah",
"Fatima Tuz Zohora",
"James B. Aimone",
"Angel Yanguas-Gil",
"Nicholas Soures",
"Emre Neftci",
"Matthew Mattina",
"Vincenzo Lomonaco",
"Clare D. Thiem",
"Benjamin Epstein"
] | 2023-10-05 19:05:40 | http://arxiv.org/abs/2310.04467v1 | http://arxiv.org/pdf/2310.04467v1 | 2310.04467v1 |
Euclid: Identification of asteroid streaks in simulated images using deep learning | Up to 150000 asteroids will be visible in the images of the ESA Euclid space
telescope, and the instruments of Euclid offer multiband visual to
near-infrared photometry and slitless spectra of these objects. Most asteroids
will appear as streaks in the images. Due to the large number of images and
asteroids, automated detection methods are needed. A non-machine-learning
approach based on the StreakDet software was previously tested, but the results
were not optimal for short and/or faint streaks. We set out to improve the
capability to detect asteroid streaks in Euclid images by using deep learning.
We built, trained, and tested a three-step machine-learning pipeline with
simulated Euclid images. First, a convolutional neural network (CNN) detected
streaks and their coordinates in full images, aiming to maximize the
completeness (recall) of detections. Then, a recurrent neural network (RNN)
merged snippets of long streaks detected in several parts by the CNN. Lastly,
gradient-boosted trees (XGBoost) linked detected streaks between different
Euclid exposures to reduce the number of false positives and improve the purity
(precision) of the sample.
The deep-learning pipeline surpasses the completeness and reaches a similar
level of purity of a non-machine-learning pipeline based on the StreakDet
software. Additionally, the deep-learning pipeline can detect asteroids
0.25-0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could
result in a 50% increase in the number of detected asteroids compared to the
StreakDet software. There is still scope for further refinement, particularly
in improving the accuracy of streak coordinates and enhancing the completeness
of the final stage of the pipeline, which involves linking detections across
multiple exposures. | [
"M. Pöntinen",
"M. Granvik",
"A. A. Nucita",
"L. Conversi",
"B. Altieri",
"B. Carry",
"C. M. O'Riordan",
"D. Scott",
"N. Aghanim",
"A. Amara",
"L. Amendola",
"N. Auricchio",
"M. Baldi",
"D. Bonino",
"E. Branchini",
"M. Brescia",
"S. Camera",
"V. Capobianco",
"C. Carbone",
"J. Carretero",
"M. Castellano",
"S. Cavuoti",
"A. Cimatti",
"R. Cledassou",
"G. Congedo",
"Y. Copin",
"L. Corcione",
"F. Courbin",
"M. Cropper",
"A. Da Silva",
"H. Degaudenzi",
"J. Dinis",
"F. Dubath",
"X. Dupac",
"S. Dusini",
"S. Farrens",
"S. Ferriol",
"M. Frailis",
"E. Franceschi",
"M. Fumana",
"S. Galeotta",
"B. Garilli",
"W. Gillard",
"B. Gillis",
"C. Giocoli",
"A. Grazian",
"S. V. H. Haugan",
"W. Holmes",
"F. Hormuth",
"A. Hornstrup",
"K. Jahnke",
"M. Kümmel",
"S. Kermiche",
"A. Kiessling",
"T. Kitching",
"R. Kohley",
"M. Kunz",
"H. Kurki-Suonio",
"S. Ligori",
"P. B. Lilje",
"I. Lloro",
"E. Maiorano",
"O. Mansutti",
"O. Marggraf",
"K. Markovic",
"F. Marulli",
"R. Massey",
"E. Medinaceli",
"S. Mei",
"M. Melchior",
"Y. Mellier",
"M. Meneghetti",
"G. Meylan",
"M. Moresco",
"L. Moscardini",
"E. Munari",
"S. -M. Niemi",
"T. Nutma",
"C. Padilla",
"S. Paltani",
"F. Pasian",
"K. Pedersen",
"V. Pettorino",
"S. Pires",
"G. Polenta",
"M. Poncet",
"F. Raison",
"A. Renzi",
"J. Rhodes",
"G. Riccio",
"E. Romelli",
"M. Roncarelli",
"E. Rossetti",
"R. Saglia",
"D. Sapone",
"B. Sartoris",
"P. Schneider",
"A. Secroun",
"G. Seidel",
"S. Serrano",
"C. Sirignano",
"G. Sirri",
"L. Stanco",
"P. Tallada-Crespí",
"A. N. Taylor",
"I. Tereno",
"R. Toledo-Moreo",
"F. Torradeflot",
"I. Tutusaus",
"L. Valenziano",
"T. Vassallo",
"G. Verdoes Kleijn",
"Y. Wang",
"J. Weller",
"G. Zamorani",
"J. Zoubian",
"V. Scottez"
] | 2023-10-05 19:03:07 | http://arxiv.org/abs/2310.03845v1 | http://arxiv.org/pdf/2310.03845v1 | 2310.03845v1 |
Less is More: On the Feature Redundancy of Pretrained Models When Transferring to Few-shot Tasks | Transferring a pretrained model to a downstream task can be as easy as
conducting linear probing with target data, that is, training a linear
classifier upon frozen features extracted from the pretrained model. As there
may exist significant gaps between pretraining and downstream datasets, one may
ask whether all dimensions of the pretrained features are useful for a given
downstream task. We show that, for linear probing, the pretrained features can
be extremely redundant when the downstream data is scarce, or few-shot. For
some cases such as 5-way 1-shot tasks, using only 1\% of the most important
feature dimensions is able to recover the performance achieved by using the
full representation. Interestingly, most dimensions are redundant only under
few-shot settings and gradually become useful when the number of shots
increases, suggesting that feature redundancy may be the key to characterizing
the "few-shot" nature of few-shot transfer problems. We give a theoretical
understanding of this phenomenon and show how dimensions with high variance and
small distance between class centroids can serve as confounding factors that
severely disturb classification results under few-shot settings. As an attempt
at solving this problem, we find that the redundant features are difficult to
identify accurately with a small number of training samples, but we can instead
adjust feature magnitude with a soft mask based on estimated feature
importance. We show that this method can generally improve few-shot transfer
performance across various pretrained models and downstream datasets. | [
"Xu Luo",
"Difan Zou",
"Lianli Gao",
"Zenglin Xu",
"Jingkuan Song"
] | 2023-10-05 19:00:49 | http://arxiv.org/abs/2310.03843v1 | http://arxiv.org/pdf/2310.03843v1 | 2310.03843v1 |
Contextualized Structural Self-supervised Learning for Ontology Matching | Ontology matching (OM) entails the identification of semantic relationships
between concepts within two or more knowledge graphs (KGs) and serves as a
critical step in integrating KGs from various sources. Recent advancements in
deep OM models have harnessed the power of transformer-based language models
and the advantages of knowledge graph embedding. Nevertheless, these OM models
still face persistent challenges, such as a lack of reference alignments,
runtime latency, and unexplored different graph structures within an end-to-end
framework. In this study, we introduce a novel self-supervised learning OM
framework with input ontologies, called LaKERMap. This framework capitalizes on
the contextual and structural information of concepts by integrating implicit
knowledge into transformers. Specifically, we aim to capture multiple
structural contexts, encompassing both local and global interactions, by
employing distinct training objectives. To assess our methods, we utilize the
Bio-ML datasets and tasks. The findings from our innovative approach reveal
that LaKERMap surpasses state-of-the-art systems in terms of alignment quality
and inference time. Our models and codes are available here:
https://github.com/ellenzhuwang/lakermap. | [
"Zhu Wang"
] | 2023-10-05 18:51:33 | http://arxiv.org/abs/2310.03840v1 | http://arxiv.org/pdf/2310.03840v1 | 2310.03840v1 |
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning | The integration of machine learning (ML) in numerous critical applications
introduces a range of privacy concerns for individuals who provide their
datasets for model training. One such privacy risk is Membership Inference
(MI), in which an attacker seeks to determine whether a particular data sample
was included in the training dataset of a model. Current state-of-the-art MI
attacks capitalize on access to the model's predicted confidence scores to
successfully perform membership inference, and employ data poisoning to further
enhance their effectiveness. In this work, we focus on the less explored and
more realistic label-only setting, where the model provides only the predicted
label on a queried sample. We show that existing label-only MI attacks are
ineffective at inferring membership in the low False Positive Rate (FPR)
regime. To address this challenge, we propose a new attack Chameleon that
leverages a novel adaptive data poisoning strategy and an efficient query
selection method to achieve significantly more accurate membership inference
than existing label-only attacks, especially at low FPRs. | [
"Harsh Chaudhari",
"Giorgio Severi",
"Alina Oprea",
"Jonathan Ullman"
] | 2023-10-05 18:46:27 | http://arxiv.org/abs/2310.03838v1 | http://arxiv.org/pdf/2310.03838v1 | 2310.03838v1 |
Learning A Disentangling Representation For PU Learning | In this paper, we address the problem of learning a binary (positive vs.
negative) classifier given Positive and Unlabeled data commonly referred to as
PU learning. Although rudimentary techniques like clustering,
out-of-distribution detection, or positive density estimation can be used to
solve the problem in low-dimensional settings, their efficacy progressively
deteriorates with higher dimensions due to the increasing complexities in the
data distribution. In this paper we propose to learn a neural network-based
data representation using a loss function that can be used to project the
unlabeled data into two (positive and negative) clusters that can be easily
identified using simple clustering techniques, effectively emulating the
phenomenon observed in low-dimensional settings. We adopt a vector quantization
technique for the learned representations to amplify the separation between the
learned unlabeled data clusters. We conduct experiments on simulated PU data
that demonstrate the improved performance of our proposed method compared to
the current state-of-the-art approaches. We also provide some theoretical
justification for our two cluster-based approach and our algorithmic choices. | [
"Omar Zamzam",
"Haleh Akrami",
"Mahdi Soltanolkotabi",
"Richard Leahy"
] | 2023-10-05 18:33:32 | http://arxiv.org/abs/2310.03833v1 | http://arxiv.org/pdf/2310.03833v1 | 2310.03833v1 |
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights | The use of edge devices together with cloud provides a collaborative
relationship between both classes of devices where one complements the
shortcomings of the other. Resource-constraint edge devices can benefit from
the abundant computing power provided by servers by offloading computationally
intensive tasks to the server. Meanwhile, edge devices can leverage their close
proximity to the data source to perform less computationally intensive tasks on
the data. In this paper, we propose a collaborative edge-cloud paradigm called
ECAvg in which edge devices pre-train local models on their respective datasets
and transfer the models to the server for fine-tuning. The server averages the
pre-trained weights into a global model, which is fine-tuned on the combined
data from the various edge devices. The local (edge) models are then updated
with the weights of the global (server) model. We implement a CIFAR-10
classification task using MobileNetV2, a CIFAR-100 classification task using
ResNet50, and an MNIST classification using a neural network with a single
hidden layer. We observed performance improvement in the CIFAR-10 and CIFAR-100
classification tasks using our approach, where performance improved on the
server model with averaged weights and the edge models had a better performance
after model update. On the MNIST classification, averaging weights resulted in
a drop in performance on both the server and edge models due to negative
transfer learning. From the experiment results, we conclude that our approach
is successful when implemented on deep neural networks such as MobileNetV2 and
ResNet50 instead of simple neural networks. | [
"Atah Nuh Mih",
"Hung Cao",
"Asfia Kawnine",
"Monica Wachowicz"
] | 2023-10-05 18:17:26 | http://arxiv.org/abs/2310.03823v1 | http://arxiv.org/pdf/2310.03823v1 | 2310.03823v1 |
Logical Languages Accepted by Transformer Encoders with Hard Attention | We contribute to the study of formal languages that can be recognized by
transformer encoders. We focus on two self-attention mechanisms: (1) UHAT
(Unique Hard Attention Transformers) and (2) AHAT (Average Hard Attention
Transformers). UHAT encoders are known to recognize only languages inside the
circuit complexity class ${\sf AC}^0$, i.e., accepted by a family of poly-sized
and depth-bounded boolean circuits with unbounded fan-ins. On the other hand,
AHAT encoders can recognize languages outside ${\sf AC}^0$), but their
expressive power still lies within the bigger circuit complexity class ${\sf
TC}^0$, i.e., ${\sf AC}^0$-circuits extended by majority gates. We first show a
negative result that there is an ${\sf AC}^0$-language that cannot be
recognized by an UHAT encoder. On the positive side, we show that UHAT encoders
can recognize a rich fragment of ${\sf AC}^0$-languages, namely, all languages
definable in first-order logic with arbitrary unary numerical predicates. This
logic, includes, for example, all regular languages from ${\sf AC}^0$. We then
show that AHAT encoders can recognize all languages of our logic even when we
enrich it with counting terms. We apply these results to derive new results on
the expressive power of UHAT and AHAT up to permutation of letters (a.k.a.
Parikh images). | [
"Pablo Barcelo",
"Alexander Kozachinskiy",
"Anthony Widjaja Lin",
"Vladimir Podolskii"
] | 2023-10-05 18:13:40 | http://arxiv.org/abs/2310.03817v1 | http://arxiv.org/pdf/2310.03817v1 | 2310.03817v1 |
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs | Set-based learning is an essential component of modern deep learning and
network science. Graph Neural Networks (GNNs) and their edge-free counterparts
Deepsets have proven remarkably useful on ragged and topologically challenging
datasets. The key to learning informative embeddings for set members is a
specified aggregation function, usually a sum, max, or mean. We propose
Fishnets, an aggregation strategy for learning information-optimal embeddings
for sets of data for both Bayesian inference and graph aggregation. We
demonstrate that i) Fishnets neural summaries can be scaled optimally to an
arbitrary number of data objects, ii) Fishnets aggregations are robust to
changes in data distribution, unlike standard deepsets, iii) Fishnets saturate
Bayesian information content and extend to regimes where MCMC techniques fail
and iv) Fishnets can be used as a drop-in aggregation scheme within GNNs. We
show that by adopting a Fishnets aggregation scheme for message passing, GNNs
can achieve state-of-the-art performance versus architecture size on
ogbn-protein data over existing benchmarks with a fraction of learnable
parameters and faster training time. | [
"T. Lucas Makinen",
"Justin Alsing",
"Benjamin D. Wandelt"
] | 2023-10-05 18:01:04 | http://arxiv.org/abs/2310.03812v1 | http://arxiv.org/pdf/2310.03812v1 | 2310.03812v1 |
Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks | A key property of deep neural networks (DNNs) is their ability to learn new
features during training. This intriguing aspect of deep learning stands out
most clearly in recently reported Grokking phenomena. While mainly reflected as
a sudden increase in test accuracy, Grokking is also believed to be a beyond
lazy-learning/Gaussian Process (GP) phenomenon involving feature learning. Here
we apply a recent development in the theory of feature learning, the adaptive
kernel approach, to two teacher-student models with cubic-polynomial and
modular addition teachers. We provide analytical predictions on feature
learning and Grokking properties of these models and demonstrate a mapping
between Grokking and the theory of phase transitions. We show that after
Grokking, the state of the DNN is analogous to the mixed phase following a
first-order phase transition. In this mixed phase, the DNN generates useful
internal representations of the teacher that are sharply distinct from those
before the transition. | [
"Noa Rubin",
"Inbar Seroussi",
"Zohar Ringel"
] | 2023-10-05 18:00:01 | http://arxiv.org/abs/2310.03789v1 | http://arxiv.org/pdf/2310.03789v1 | 2310.03789v1 |
Improved Baselines with Visual Instruction Tuning | Large multimodal models (LMM) have recently shown encouraging progress with
visual instruction tuning. In this note, we show that the fully-connected
vision-language cross-modal connector in LLaVA is surprisingly powerful and
data-efficient. With simple modifications to LLaVA, namely, using
CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA
data with simple response formatting prompts, we establish stronger baselines
that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint
uses merely 1.2M publicly available data, and finishes full training in ~1 day
on a single 8-A100 node. We hope this can make state-of-the-art LMM research
more accessible. Code and model will be publicly available. | [
"Haotian Liu",
"Chunyuan Li",
"Yuheng Li",
"Yong Jae Lee"
] | 2023-10-05 17:59:56 | http://arxiv.org/abs/2310.03744v1 | http://arxiv.org/pdf/2310.03744v1 | 2310.03744v1 |
The Un-Kidnappable Robot: Acoustic Localization of Sneaking People | How easy is it to sneak up on a robot? We examine whether we can detect
people using only the incidental sounds they produce as they move, even when
they try to be quiet. We collect a robotic dataset of high-quality 4-channel
audio paired with 360 degree RGB data of people moving in different indoor
settings. We train models that predict if there is a moving person nearby and
their location using only audio. We implement our method on a robot, allowing
it to track a single person moving quietly with only passive audio sensing. For
demonstration videos, see our project page:
https://sites.google.com/view/unkidnappable-robot | [
"Mengyu Yang",
"Patrick Grady",
"Samarth Brahmbhatt",
"Arun Balajee Vasudevan",
"Charles C. Kemp",
"James Hays"
] | 2023-10-05 17:59:55 | http://arxiv.org/abs/2310.03743v1 | http://arxiv.org/pdf/2310.03743v1 | 2310.03743v1 |
ContactGen: Generative Contact Modeling for Grasp Generation | This paper presents a novel object-centric contact representation ContactGen
for hand-object interaction. The ContactGen comprises three components: a
contact map indicates the contact location, a part map represents the contact
hand part, and a direction map tells the contact direction within each part.
Given an input object, we propose a conditional generative model to predict
ContactGen and adopt model-based optimization to predict diverse and
geometrically feasible grasps. Experimental results demonstrate our method can
generate high-fidelity and diverse human grasps for various objects. Project
page: https://stevenlsw.github.io/contactgen/ | [
"Shaowei Liu",
"Yang Zhou",
"Jimei Yang",
"Saurabh Gupta",
"Shenlong Wang"
] | 2023-10-05 17:59:45 | http://arxiv.org/abs/2310.03740v1 | http://arxiv.org/pdf/2310.03740v1 | 2310.03740v1 |
Aligning Text-to-Image Diffusion Models with Reward Backpropagation | Text-to-image diffusion models have recently emerged at the forefront of
image generation, powered by very large-scale unsupervised or weakly supervised
text-to-image training datasets. Due to their unsupervised training,
controlling their behavior in downstream tasks, such as maximizing
human-perceived image quality, image-text alignment, or ethical image
generation, is difficult. Recent works finetune diffusion models to downstream
reward functions using vanilla reinforcement learning, notorious for the high
variance of the gradient estimators. In this paper, we propose AlignProp, a
method that aligns diffusion models to downstream reward functions using
end-to-end backpropagation of the reward gradient through the denoising
process. While naive implementation of such backpropagation would require
prohibitive memory resources for storing the partial derivatives of modern
text-to-image models, AlignProp finetunes low-rank adapter weight modules and
uses gradient checkpointing, to render its memory usage viable. We test
AlignProp in finetuning diffusion models to various objectives, such as
image-text semantic alignment, aesthetics, compressibility and controllability
of the number of objects present, as well as their combinations. We show
AlignProp achieves higher rewards in fewer training steps than alternatives,
while being conceptually simpler, making it a straightforward choice for
optimizing diffusion models for differentiable reward functions of interest.
Code and Visualization results are available at https://align-prop.github.io/. | [
"Mihir Prabhudesai",
"Anirudh Goyal",
"Deepak Pathak",
"Katerina Fragkiadaki"
] | 2023-10-05 17:59:18 | http://arxiv.org/abs/2310.03739v1 | http://arxiv.org/pdf/2310.03739v1 | 2310.03739v1 |
Stylist: Style-Driven Feature Ranking for Robust Novelty Detection | Novelty detection aims at finding samples that differ in some form from the
distribution of seen samples. But not all changes are created equal. Data can
suffer a multitude of distribution shifts, and we might want to detect only
some types of relevant changes. Similar to works in out-of-distribution
generalization, we propose to use the formalization of separating into semantic
or content changes, that are relevant to our task, and style changes, that are
irrelevant. Within this formalization, we define the robust novelty detection
as the task of finding semantic changes while being robust to style
distributional shifts. Leveraging pretrained, large-scale model
representations, we introduce Stylist, a novel method that focuses on dropping
environment-biased features. First, we compute a per-feature score based on the
feature distribution distances between environments. Next, we show that our
selection manages to remove features responsible for spurious correlations and
improve novelty detection performance. For evaluation, we adapt domain
generalization datasets to our task and analyze the methods behaviors. We
additionally built a large synthetic dataset where we have control over the
spurious correlations degree. We prove that our selection mechanism improves
novelty detection algorithms across multiple datasets, containing both
stylistic and content shifts. | [
"Stefan Smeu",
"Elena Burceanu",
"Emanuela Haller",
"Andrei Liviu Nicolicioiu"
] | 2023-10-05 17:58:32 | http://arxiv.org/abs/2310.03738v1 | http://arxiv.org/pdf/2310.03738v1 | 2310.03738v1 |
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning | The recently released GPT-4 Code Interpreter has demonstrated remarkable
proficiency in solving challenging math problems, primarily attributed to its
ability to seamlessly reason with natural language, generate code, execute
code, and continue reasoning based on the execution output. In this paper, we
present a method to fine-tune open-source language models, enabling them to use
code for modeling and deriving math equations and, consequently, enhancing
their mathematical reasoning abilities. We propose a method of generating novel
and high-quality datasets with math problems and their code-based solutions,
referred to as MathCodeInstruct. Each solution interleaves natural language,
code, and execution results. We also introduce a customized supervised
fine-tuning and inference approach. This approach yields the MathCoder models,
a family of models capable of generating code-based solutions for solving
challenging math problems. Impressively, the MathCoder models achieve
state-of-the-art scores among open-source LLMs on the MATH (45.2%) and GSM8K
(83.9%) datasets, substantially outperforming other open-source alternatives.
Notably, the MathCoder model not only surpasses ChatGPT-3.5 and PaLM-2 on GSM8K
and MATH but also outperforms GPT-4 on the competition-level MATH dataset. The
dataset and models will be released at https://github.com/mathllm/MathCoder. | [
"Ke Wang",
"Houxing Ren",
"Aojun Zhou",
"Zimu Lu",
"Sichun Luo",
"Weikang Shi",
"Renrui Zhang",
"Linqi Song",
"Mingjie Zhan",
"Hongsheng Li"
] | 2023-10-05 17:52:09 | http://arxiv.org/abs/2310.03731v1 | http://arxiv.org/pdf/2310.03731v1 | 2310.03731v1 |
Stochastic interpolants with data-dependent couplings | Generative models inspired by dynamical transport of measure -- such as flows
and diffusions -- construct a continuous-time map between two probability
densities. Conventionally, one of these is the target density, only accessible
through samples, while the other is taken as a simple base density that is
data-agnostic. In this work, using the framework of stochastic interpolants, we
formalize how to \textit{couple} the base and the target densities. This
enables us to incorporate information about class labels or continuous
embeddings to construct dynamical transport maps that serve as conditional
generative models. We show that these transport maps can be learned by solving
a simple square loss regression problem analogous to the standard independent
setting. We demonstrate the usefulness of constructing dependent couplings in
practice through experiments in super-resolution and in-painting. | [
"Michael S. Albergo",
"Mark Goldstein",
"Nicholas M. Boffi",
"Rajesh Ranganath",
"Eric Vanden-Eijnden"
] | 2023-10-05 17:46:31 | http://arxiv.org/abs/2310.03725v1 | http://arxiv.org/pdf/2310.03725v1 | 2310.03725v1 |
Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance | In 1976, Lai constructed a nontrivial confidence sequence for the mean $\mu$
of a Gaussian distribution with unknown variance $\sigma$. Curiously, he
employed both an improper (right Haar) mixture over $\sigma$ and an improper
(flat) mixture over $\mu$. Here, we elaborate carefully on the details of his
construction, which use generalized nonintegrable martingales and an extended
Ville's inequality. While this does yield a sequential t-test, it does not
yield an ``e-process'' (due to the nonintegrability of his martingale). In this
paper, we develop two new e-processes and confidence sequences for the same
setting: one is a test martingale in a reduced filtration, while the other is
an e-process in the canonical data filtration. These are respectively obtained
by swapping Lai's flat mixture for a Gaussian mixture, and swapping the right
Haar mixture over $\sigma$ with the maximum likelihood estimate under the null,
as done in universal inference. We also analyze the width of resulting
confidence sequences, which have a curious dependence on the error probability
$\alpha$. Numerical experiments are provided along the way to compare and
contrast the various approaches. | [
"Hongjian Wang",
"Aaditya Ramdas"
] | 2023-10-05 17:43:26 | http://arxiv.org/abs/2310.03722v2 | http://arxiv.org/pdf/2310.03722v2 | 2310.03722v2 |
HeaP: Hierarchical Policies for Web Actions using LLMs | Large language models (LLMs) have demonstrated remarkable capabilities in
performing a range of instruction following tasks in few and zero-shot
settings. However, teaching LLMs to perform tasks on the web presents
fundamental challenges -- combinatorially large open-world tasks and variations
across web interfaces. We tackle these challenges by leveraging LLMs to
decompose web tasks into a collection of sub-tasks, each of which can be solved
by a low-level, closed-loop policy. These policies constitute a shared grammar
across tasks, i.e., new web tasks can be expressed as a composition of these
policies. We propose a novel framework, Hierarchical Policies for Web Actions
using LLMs (HeaP), that learns a set of hierarchical LLM prompts from
demonstrations for planning high-level tasks and executing them via a sequence
of low-level policies. We evaluate HeaP against a range of baselines on a suite
of web tasks, including MiniWoB++, WebArena, a mock airline CRM, as well as
live website interactions, and show that it is able to outperform prior works
using orders of magnitude less data. | [
"Paloma Sodhi",
"S. R. K. Branavan",
"Ryan McDonald"
] | 2023-10-05 17:40:09 | http://arxiv.org/abs/2310.03720v1 | http://arxiv.org/pdf/2310.03720v1 | 2310.03720v1 |
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning | Safe reinforcement learning (RL) focuses on training reward-maximizing agents
subject to pre-defined safety constraints. Yet, learning versatile safe
policies that can adapt to varying safety constraint requirements during
deployment without retraining remains a largely unexplored and challenging
area. In this work, we formulate the versatile safe RL problem and consider two
primary requirements: training efficiency and zero-shot adaptation capability.
To address them, we introduce the Conditioned Constrained Policy Optimization
(CCPO) framework, consisting of two key modules: (1) Versatile Value Estimation
(VVE) for approximating value functions under unseen threshold conditions, and
(2) Conditioned Variational Inference (CVI) for encoding arbitrary constraint
thresholds during policy optimization. Our extensive experiments demonstrate
that CCPO outperforms the baselines in terms of safety and task performance
while preserving zero-shot adaptation capabilities to different constraint
thresholds data-efficiently. This makes our approach suitable for real-world
dynamic applications. | [
"Yihang Yao",
"Zuxin Liu",
"Zhepeng Cen",
"Jiacheng Zhu",
"Wenhao Yu",
"Tingnan Zhang",
"Ding Zhao"
] | 2023-10-05 17:39:02 | http://arxiv.org/abs/2310.03718v1 | http://arxiv.org/pdf/2310.03718v1 | 2310.03718v1 |
A Long Way to Go: Investigating Length Correlations in RLHF | Great successes have been reported using Reinforcement Learning from Human
Feedback (RLHF) to align large language models. Open-source preference datasets
and reward models have enabled wider experimentation beyond generic chat
settings, particularly to make systems more "helpful" for tasks like web
question answering, summarization, and multi-turn dialogue. When optimizing for
helpfulness, RLHF has been consistently observed to drive models to produce
longer outputs. This paper demonstrates that optimizing for response length is
a significant factor behind RLHF's reported improvements in these settings.
First, we study the relationship between reward and length for reward models
trained on three open-source preference datasets for helpfulness. Here, length
correlates strongly with reward, and improvements in reward score are driven in
large part by shifting the distribution over output lengths. We then explore
interventions during both RL and reward model learning to see if we can achieve
the same downstream improvements as RLHF without increasing length. While our
interventions mitigate length increases, they aren't uniformly effective across
settings. Furthermore, we find that even running RLHF with a reward based
solely on length can reproduce most of the downstream improvements over the
initial policy model, showing that reward models in these settings have a long
way to go. | [
"Prasann Singhal",
"Tanya Goyal",
"Jiacheng Xu",
"Greg Durrett"
] | 2023-10-05 17:38:28 | http://arxiv.org/abs/2310.03716v1 | http://arxiv.org/pdf/2310.03716v1 | 2310.03716v1 |
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines | The ML community is rapidly exploring techniques for prompting language
models (LMs) and for stacking them into pipelines that solve complex tasks.
Unfortunately, existing LM pipelines are typically implemented using hard-coded
"prompt templates", i.e. lengthy strings discovered via trial and error. Toward
a more systematic approach for developing and optimizing LM pipelines, we
introduce DSPy, a programming model that abstracts LM pipelines as text
transformation graphs, i.e. imperative computational graphs where LMs are
invoked through declarative modules. DSPy modules are parameterized, meaning
they can learn (by creating and collecting demonstrations) how to apply
compositions of prompting, finetuning, augmentation, and reasoning techniques.
We design a compiler that will optimize any DSPy pipeline to maximize a given
metric. We conduct two case studies, showing that succinct DSPy programs can
express and optimize sophisticated LM pipelines that reason about math word
problems, tackle multi-hop retrieval, answer complex questions, and control
agent loops. Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and
llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot
prompting (generally by over 25% and 65%, respectively) and pipelines with
expert-created demonstrations (by up to 5-46% and 16-40%, respectively). On top
of that, DSPy programs compiled to open and relatively small LMs like
770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely
on expert-written prompt chains for proprietary GPT-3.5. DSPy is available at
https://github.com/stanfordnlp/dspy | [
"Omar Khattab",
"Arnav Singhvi",
"Paridhi Maheshwari",
"Zhiyuan Zhang",
"Keshav Santhanam",
"Sri Vardhamanan",
"Saiful Haq",
"Ashutosh Sharma",
"Thomas T. Joshi",
"Hanna Moazam",
"Heather Miller",
"Matei Zaharia",
"Christopher Potts"
] | 2023-10-05 17:37:25 | http://arxiv.org/abs/2310.03714v1 | http://arxiv.org/pdf/2310.03714v1 | 2310.03714v1 |
Agent Instructs Large Language Models to be General Zero-Shot Reasoners | We introduce a method to improve the zero-shot reasoning abilities of large
language models on general language understanding tasks. Specifically, we build
an autonomous agent to instruct the reasoning process of large language models.
We show this approach further unleashes the zero-shot reasoning abilities of
large language models to more tasks. We study the performance of our method on
a wide set of datasets spanning generation, classification, and reasoning. We
show that our method generalizes to most tasks and obtains state-of-the-art
zero-shot performance on 20 of the 29 datasets that we evaluate. For instance,
our method boosts the performance of state-of-the-art large language models by
a large margin, including Vicuna-13b (13.3%), Llama-2-70b-chat (23.2%), and
GPT-3.5 Turbo (17.0%). Compared to zero-shot chain of thought, our improvement
in reasoning is striking, with an average increase of 10.5%. With our method,
Llama-2-70b-chat outperforms zero-shot GPT-3.5 Turbo by 10.2%. | [
"Nicholas Crispino",
"Kyle Montgomery",
"Fankun Zeng",
"Dawn Song",
"Chenguang Wang"
] | 2023-10-05 17:36:16 | http://arxiv.org/abs/2310.03710v1 | http://arxiv.org/pdf/2310.03710v1 | 2310.03710v1 |
Beyond One-Preference-for-All: Multi-Objective Direct Preference Optimization for Language Models | A single language model (LM), despite aligning well with an average labeler
through reinforcement learning from human feedback (RLHF), may not universally
suit diverse human preferences. Recent approaches thus pursue customization,
training separate principle-based reward models to represent different
alignment objectives (e.g. helpfulness, harmlessness, or honesty). Different
LMs can then be trained for different preferences through multi-objective RLHF
(MORLHF) with different objective weightings. Yet, RLHF is unstable and
resource-heavy, especially for MORLHF with diverse and usually conflicting
objectives. In this paper, we present Multi-Objective Direct Preference
Optimization (MODPO), an RL-free algorithm that extends Direct Preference
Optimization (DPO) for multiple alignment objectives. Essentially, MODPO folds
LM learning directly into reward modeling, aligning LMs with the weighted sum
of all principle-based rewards using pure cross-entropy loss. While
theoretically guaranteed to produce the same optimal solutions as MORLHF, MODPO
is practically more stable and computationally efficient, obviating value
function modeling and online sample collection. Empirical results in safety
alignment and long-form question answering confirm that MODPO matches or
outperforms existing methods, consistently producing one of the most
competitive LM fronts that cater to diverse preferences with 3 times fewer
computations compared with MORLHF. | [
"Zhanhui Zhou",
"Jie Liu",
"Chao Yang",
"Jing Shao",
"Yu Liu",
"Xiangyu Yue",
"Wanli Ouyang",
"Yu Qiao"
] | 2023-10-05 17:35:26 | http://arxiv.org/abs/2310.03708v2 | http://arxiv.org/pdf/2310.03708v2 | 2310.03708v2 |
OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks | Evasion Attacks (EA) are used to test the robustness of trained neural
networks by distorting input data to misguide the model into incorrect
classifications. Creating these attacks is a challenging task, especially with
the ever-increasing complexity of models and datasets. In this work, we
introduce a self-supervised, computationally economical method for generating
adversarial examples, designed for the unseen black-box setting. Adapting
techniques from representation learning, our method generates on-manifold EAs
that are encouraged to resemble the data distribution. These attacks are
comparable in effectiveness compared to the state-of-the-art when attacking the
model trained on, but are significantly more effective when attacking unseen
models, as the attacks are more related to the data rather than the model
itself. Our experiments consistently demonstrate the method is effective across
various models, unseen data categories, and even defended models, suggesting a
significant role for on-manifold EAs when targeting unseen models. | [
"Ofir Bar Tal",
"Adi Haviv",
"Amit H. Bermano"
] | 2023-10-05 17:34:47 | http://arxiv.org/abs/2310.03707v1 | http://arxiv.org/pdf/2310.03707v1 | 2310.03707v1 |
Banach Space Optimality of Neural Architectures With Multivariate Nonlinearities | We investigate the variational optimality (specifically, the Banach space
optimality) of a large class of neural architectures with multivariate
nonlinearities/activation functions. To that end, we construct a new family of
Banach spaces defined via a regularization operator and the $k$-plane
transform. We prove a representer theorem that states that the solution sets to
learning problems posed over these Banach spaces are completely characterized
by neural architectures with multivariate nonlinearities. These optimal
architectures have skip connections and are tightly connected to orthogonal
weight normalization and multi-index models, both of which have received
considerable interest in the neural network community. Our framework is
compatible with a number of classical nonlinearities including the rectified
linear unit (ReLU) activation function, the norm activation function, and the
radial basis functions found in the theory of thin-plate/polyharmonic splines.
We also show that the underlying spaces are special instances of reproducing
kernel Banach spaces and variation spaces. Our results shed light on the
regularity of functions learned by neural networks trained on data,
particularly with multivariate nonlinearities, and provide new theoretical
motivation for several architectural choices found in practice. | [
"Rahul Parhi",
"Michael Unser"
] | 2023-10-05 17:13:16 | http://arxiv.org/abs/2310.03696v1 | http://arxiv.org/pdf/2310.03696v1 | 2310.03696v1 |
Multimarginal generative modeling with stochastic interpolants | Given a set of $K$ probability densities, we consider the multimarginal
generative modeling problem of learning a joint distribution that recovers
these densities as marginals. The structure of this joint distribution should
identify multi-way correspondences among the prescribed marginals. We formalize
an approach to this task within a generalization of the stochastic interpolant
framework, leading to efficient learning algorithms built upon dynamical
transport of measure. Our generative models are defined by velocity and score
fields that can be characterized as the minimizers of simple quadratic
objectives, and they are defined on a simplex that generalizes the time
variable in the usual dynamical transport framework. The resulting transport on
the simplex is influenced by all marginals, and we show that multi-way
correspondences can be extracted. The identification of such correspondences
has applications to style transfer, algorithmic fairness, and data
decorruption. In addition, the multimarginal perspective enables an efficient
algorithm for reducing the dynamical transport cost in the ordinary
two-marginal setting. We demonstrate these capacities with several numerical
examples. | [
"Michael S. Albergo",
"Nicholas M. Boffi",
"Michael Lindsey",
"Eric Vanden-Eijnden"
] | 2023-10-05 17:12:38 | http://arxiv.org/abs/2310.03695v1 | http://arxiv.org/pdf/2310.03695v1 | 2310.03695v1 |
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! | Optimizing large language models (LLMs) for downstream use cases often
involves the customization of pre-trained LLMs through further fine-tuning.
Meta's open release of Llama models and OpenAI's APIs for fine-tuning GPT-3.5
Turbo on custom datasets also encourage this practice. But, what are the safety
costs associated with such custom fine-tuning? We note that while existing
safety alignment infrastructures can restrict harmful behaviors of LLMs at
inference time, they do not cover safety risks when fine-tuning privileges are
extended to end-users. Our red teaming studies find that the safety alignment
of LLMs can be compromised by fine-tuning with only a few adversarially
designed training examples. For instance, we jailbreak GPT-3.5 Turbo's safety
guardrails by fine-tuning it on only 10 such examples at a cost of less than
$0.20 via OpenAI's APIs, making the model responsive to nearly any harmful
instructions. Disconcertingly, our research also reveals that, even without
malicious intent, simply fine-tuning with benign and commonly used datasets can
also inadvertently degrade the safety alignment of LLMs, though to a lesser
extent. These findings suggest that fine-tuning aligned LLMs introduces new
safety risks that current safety infrastructures fall short of addressing --
even if a model's initial safety alignment is impeccable, it is not necessarily
to be maintained after custom fine-tuning. We outline and critically analyze
potential mitigations and advocate for further research efforts toward
reinforcing safety protocols for the custom fine-tuning of aligned LLMs. | [
"Xiangyu Qi",
"Yi Zeng",
"Tinghao Xie",
"Pin-Yu Chen",
"Ruoxi Jia",
"Prateek Mittal",
"Peter Henderson"
] | 2023-10-05 17:12:17 | http://arxiv.org/abs/2310.03693v1 | http://arxiv.org/pdf/2310.03693v1 | 2310.03693v1 |
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks | Despite efforts to align large language models (LLMs) with human values,
widely-used LLMs such as GPT, Llama, Claude, and PaLM are susceptible to
jailbreaking attacks, wherein an adversary fools a targeted LLM into generating
objectionable content. To address this vulnerability, we propose SmoothLLM, the
first algorithm designed to mitigate jailbreaking attacks on LLMs. Based on our
finding that adversarially-generated prompts are brittle to character-level
changes, our defense first randomly perturbs multiple copies of a given input
prompt, and then aggregates the corresponding predictions to detect adversarial
inputs. SmoothLLM reduces the attack success rate on numerous popular LLMs to
below one percentage point, avoids unnecessary conservatism, and admits
provable guarantees on attack mitigation. Moreover, our defense uses
exponentially fewer queries than existing attacks and is compatible with any
LLM. | [
"Alexander Robey",
"Eric Wong",
"Hamed Hassani",
"George J. Pappas"
] | 2023-10-05 17:01:53 | http://arxiv.org/abs/2310.03684v2 | http://arxiv.org/pdf/2310.03684v2 | 2310.03684v2 |
Hadamard Domain Training with Integers for Class Incremental Quantized Learning | Continual learning is a desirable feature in many modern machine learning
applications, which allows in-field adaptation and updating, ranging from
accommodating distribution shift, to fine-tuning, and to learning new tasks.
For applications with privacy and low latency requirements, the compute and
memory demands imposed by continual learning can be cost-prohibitive for
resource-constraint edge platforms. Reducing computational precision through
fully quantized training (FQT) simultaneously reduces memory footprint and
increases compute efficiency for both training and inference. However,
aggressive quantization especially integer FQT typically degrades model
accuracy to unacceptable levels. In this paper, we propose a technique that
leverages inexpensive Hadamard transforms to enable low-precision training with
only integer matrix multiplications. We further determine which tensors need
stochastic rounding and propose tiled matrix multiplication to enable low-bit
width accumulators. We demonstrate the effectiveness of our technique on
several human activity recognition datasets and CIFAR100 in a class incremental
learning setting. We achieve less than 0.5% and 3% accuracy degradation while
we quantize all matrix multiplications inputs down to 4-bits with 8-bit
accumulators. | [
"Martin Schiemer",
"Clemens JS Schaefer",
"Jayden Parker Vap",
"Mark James Horeni",
"Yu Emma Wang",
"Juan Ye",
"Siddharth Joshi"
] | 2023-10-05 16:52:59 | http://arxiv.org/abs/2310.03675v1 | http://arxiv.org/pdf/2310.03675v1 | 2310.03675v1 |
Strategic Evaluation: Subjects, Evaluators, and Society | A broad current application of algorithms is in formal and quantitative
measures of murky concepts -- like merit -- to make decisions. When people
strategically respond to these sorts of evaluations in order to gain favorable
decision outcomes, their behavior can be subjected to moral judgments. They may
be described as 'gaming the system' or 'cheating,' or (in other cases)
investing 'honest effort' or 'improving.' Machine learning literature on
strategic behavior has tried to describe these dynamics by emphasizing the
efforts expended by decision subjects hoping to obtain a more favorable
assessment -- some works offer ways to preempt or prevent such manipulations,
some differentiate 'gaming' from 'improvement' behavior, while others aim to
measure the effort burden or disparate effects of classification systems. We
begin from a different starting point: that the design of an evaluation itself
can be understood as furthering goals held by the evaluator which may be
misaligned with broader societal goals. To develop the idea that evaluation
represents a strategic interaction in which both the evaluator and the subject
of their evaluation are operating out of self-interest, we put forward a model
that represents the process of evaluation using three interacting agents: a
decision subject, an evaluator, and society, representing a bundle of values
and oversight mechanisms. We highlight our model's applicability to a number of
social systems where one or two players strategically undermine the others'
interests to advance their own. Treating evaluators as themselves strategic
allows us to re-cast the scrutiny directed at decision subjects, towards the
incentives that underpin institutional designs of evaluations. The moral
standing of strategic behaviors often depend on the moral standing of the
evaluations and incentives that provoke such behaviors. | [
"Benjamin Laufer",
"Jon Kleinberg",
"Karen Levy",
"Helen Nissenbaum"
] | 2023-10-05 16:33:08 | http://arxiv.org/abs/2310.03655v1 | http://arxiv.org/pdf/2310.03655v1 | 2310.03655v1 |
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics | Data-driven constitutive modeling with neural networks has received increased
interest in recent years due to its ability to easily incorporate physical and
mechanistic constraints and to overcome the challenging and time-consuming task
of formulating phenomenological constitutive laws that can accurately capture
the observed material response. However, even though neural network-based
constitutive laws have been shown to generalize proficiently, the generated
representations are not easily interpretable due to their high number of
trainable parameters. Sparse regression approaches exist that allow to
obtaining interpretable expressions, but the user is tasked with creating a
library of model forms which by construction limits their expressiveness to the
functional forms provided in the libraries. In this work, we propose to train
regularized physics-augmented neural network-based constitutive models
utilizing a smoothed version of $L^{0}$-regularization. This aims to maintain
the trustworthiness inherited by the physical constraints, but also enables
interpretability which has not been possible thus far on any type of machine
learning-based constitutive model where model forms were not assumed a-priory
but were actually discovered. During the training process, the network
simultaneously fits the training data and penalizes the number of active
parameters, while also ensuring constitutive constraints such as thermodynamic
consistency. We show that the method can reliably obtain interpretable and
trustworthy constitutive models for compressible and incompressible
hyperelasticity, yield functions, and hardening models for elastoplasticity,
for synthetic and experimental data. | [
"Jan N. Fuhg",
"Reese E. Jones",
"Nikolaos Bouklas"
] | 2023-10-05 16:28:58 | http://arxiv.org/abs/2310.03652v1 | http://arxiv.org/pdf/2310.03652v1 | 2310.03652v1 |
Rethinking Fairness for Human-AI Collaboration | Existing approaches to algorithmic fairness aim to ensure equitable outcomes
if human decision-makers comply perfectly with algorithmic decisions. However,
perfect compliance with the algorithm is rarely a reality or even a desirable
outcome in human-AI collaboration. Yet, recent studies have shown that
selective compliance with fair algorithms can amplify discrimination relative
to the prior human policy. As a consequence, ensuring equitable outcomes
requires fundamentally different algorithmic design principles that ensure
robustness to the decision-maker's (a priori unknown) compliance pattern. We
define the notion of compliance-robustly fair algorithmic recommendations that
are guaranteed to (weakly) improve fairness in decisions, regardless of the
human's compliance pattern. We propose a simple optimization strategy to
identify the best performance-improving compliance-robustly fair policy.
However, we show that it may be infeasible to design algorithmic
recommendations that are simultaneously fair in isolation, compliance-robustly
fair, and more accurate than the human policy; thus, if our goal is to improve
the equity and accuracy of human-AI collaboration, it may not be desirable to
enforce traditional fairness constraints. | [
"Haosen Ge",
"Hamsa Bastani",
"Osbert Bastani"
] | 2023-10-05 16:21:42 | http://arxiv.org/abs/2310.03647v1 | http://arxiv.org/pdf/2310.03647v1 | 2310.03647v1 |
TRAM: Bridging Trust Regions and Sharpness Aware Minimization | By reducing the curvature of the loss surface in the parameter space,
Sharpness-aware minimization (SAM) yields widespread robustness improvement
under domain transfer. Instead of focusing on parameters, however, this work
considers the transferability of representations as the optimization target for
out-of-domain generalization in a fine-tuning setup. To encourage the retention
of transferable representations, we consider trust region-based fine-tuning
methods, which exploit task-specific skills without forgetting task-agnostic
representations from pre-training. We unify parameter- and representation-space
smoothing approaches by using trust region bounds to inform SAM-style
regularizers on both of these optimization surfaces. We propose Trust Region
Aware Minimization (TRAM), a fine-tuning algorithm that optimizes for flat
minima and smooth, informative representations without forgetting pre-trained
structure. We find that TRAM outperforms both sharpness-aware and trust
region-based optimization methods on cross-domain language modeling and
cross-lingual transfer, where robustness to domain transfer and representation
generality are critical for success. TRAM establishes a new standard in
training generalizable models with minimal additional computation. | [
"Tom Sherborne",
"Naomi Saphra",
"Pradeep Dasigi",
"Hao Peng"
] | 2023-10-05 16:21:36 | http://arxiv.org/abs/2310.03646v1 | http://arxiv.org/pdf/2310.03646v1 | 2310.03646v1 |
HandMeThat: Human-Robot Communication in Physical and Social Environments | We introduce HandMeThat, a benchmark for a holistic evaluation of instruction
understanding and following in physical and social environments. While previous
datasets primarily focused on language grounding and planning, HandMeThat
considers the resolution of human instructions with ambiguities based on the
physical (object states and relations) and social (human actions and goals)
information. HandMeThat contains 10,000 episodes of human-robot interactions.
In each episode, the robot first observes a trajectory of human actions towards
her internal goal. Next, the robot receives a human instruction and should take
actions to accomplish the subgoal set through the instruction. In this paper,
we present a textual interface for our benchmark, where the robot interacts
with a virtual environment through textual commands. We evaluate several
baseline models on HandMeThat, and show that both offline and online
reinforcement learning algorithms perform poorly on HandMeThat, suggesting
significant room for future work on physical and social human-robot
communications and interactions. | [
"Yanming Wan",
"Jiayuan Mao",
"Joshua B. Tenenbaum"
] | 2023-10-05 16:14:46 | http://arxiv.org/abs/2310.03779v1 | http://arxiv.org/pdf/2310.03779v1 | 2310.03779v1 |
Distributional PAC-Learning from Nisan's Natural Proofs | (Abridged) Carmosino et al. (2016) demonstrated that natural proofs of
circuit lower bounds for \Lambda imply efficient algorithms for learning
\Lambda-circuits, but only over the uniform distribution, with membership
queries, and provided \AC^0[p] \subseteq \Lambda. We consider whether this
implication can be generalized to \Lambda \not\supseteq \AC^0[p], and to
learning algorithms in Valiant's PAC model, which use only random examples and
learn over arbitrary example distributions. We give results of both positive
and negative flavor.
On the negative side, we observe that if, for every circuit class \Lambda,
the implication from natural proofs for \Lambda to learning \Lambda-circuits in
Valiant's PAC model holds, then there is a polynomial time solution to
O(n^{1.5})-uSVP (unique Shortest Vector Problem), and polynomial time quantum
solutions to O(n^{1.5})-SVP (Shortest Vector Problem) and O(n^{1.5})-SIVP
(Shortest Independent Vector Problem). This indicates that whether natural
proofs for \Lambda imply efficient learning algorithms for \Lambda in Valiant's
PAC model may depend on \Lambda.
On the positive side, our main result is that specific natural proofs arising
from a type of communication complexity argument (e.g., Nisan (1993), for
depth-2 majority circuits) imply PAC-learning algorithms in a new
distributional variant of Valiant's model. Our distributional PAC model is
stronger than the average-case prediction model of Blum et al (1993) and the
heuristic PAC model of Nanashima (2021), and has several important properties
which make it of independent interest, such as being boosting-friendly. The
main applications of our result are new distributional PAC-learning algorithms
for depth-2 majority circuits, polytopes and DNFs over natural target
distributions, as well as the nonexistence of encoded-input weak PRFs that can
be evaluated by depth-2 majority circuits. | [
"Ari Karchmer"
] | 2023-10-05 16:13:29 | http://arxiv.org/abs/2310.03641v1 | http://arxiv.org/pdf/2310.03641v1 | 2310.03641v1 |
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