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The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning | In this work, we assess the theoretical limitations of determining guaranteed
stability and accuracy of neural networks in classification tasks. We consider
classical distribution-agnostic framework and algorithms minimising empirical
risks and potentially subjected to some weights regularisation. We show that
there is a large family of tasks for which computing and verifying ideal stable
and accurate neural networks in the above settings is extremely challenging, if
at all possible, even when such ideal solutions exist within the given class of
neural architectures. | [
"Alexander Bastounis",
"Alexander N. Gorban",
"Anders C. Hansen",
"Desmond J. Higham",
"Danil Prokhorov",
"Oliver Sutton",
"Ivan Y. Tyukin",
"Qinghua Zhou"
] | 2023-09-13 16:33:27 | http://arxiv.org/abs/2309.07072v1 | http://arxiv.org/pdf/2309.07072v1 | 2309.07072v1 |
Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments | Despite their promise to facilitate new scientific discoveries, the
opaqueness of neural networks presents a challenge in interpreting the logic
behind their findings. Here, we use a eXplainable-AI (XAI) technique called
$inception$ or $deep$ $dreaming$, which has been invented in machine learning
for computer vision. We use this technique to explore what neural networks
learn about quantum optics experiments. Our story begins by training deep
neural networks on the properties of quantum systems. Once trained, we "invert"
the neural network -- effectively asking how it imagines a quantum system with
a specific property, and how it would continuously modify the quantum system to
change a property. We find that the network can shift the initial distribution
of properties of the quantum system, and we can conceptualize the learned
strategies of the neural network. Interestingly, we find that, in the first
layers, the neural network identifies simple properties, while in the deeper
ones, it can identify complex quantum structures and even quantum entanglement.
This is in reminiscence of long-understood properties known in computer vision,
which we now identify in a complex natural science task. Our approach could be
useful in a more interpretable way to develop new advanced AI-based scientific
discovery techniques in quantum physics. | [
"Tareq Jaouni",
"Sören Arlt",
"Carlos Ruiz-Gonzalez",
"Ebrahim Karimi",
"Xuemei Gu",
"Mario Krenn"
] | 2023-09-13 16:13:54 | http://arxiv.org/abs/2309.07056v2 | http://arxiv.org/pdf/2309.07056v2 | 2309.07056v2 |
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck | Markov processes are widely used mathematical models for describing dynamic
systems in various fields. However, accurately simulating large-scale systems
at long time scales is computationally expensive due to the short time steps
required for accurate integration. In this paper, we introduce an inference
process that maps complex systems into a simplified representational space and
models large jumps in time. To achieve this, we propose Time-lagged Information
Bottleneck (T-IB), a principled objective rooted in information theory, which
aims to capture relevant temporal features while discarding high-frequency
information to simplify the simulation task and minimize the inference error.
Our experiments demonstrate that T-IB learns information-optimal
representations for accurately modeling the statistical properties and dynamics
of the original process at a selected time lag, outperforming existing
time-lagged dimensionality reduction methods. | [
"Marco Federici",
"Patrick Forré",
"Ryota Tomioka",
"Bastiaan S. Veeling"
] | 2023-09-13 15:59:14 | http://arxiv.org/abs/2309.07200v1 | http://arxiv.org/pdf/2309.07200v1 | 2309.07200v1 |
An Extreme Learning Machine-Based Method for Computational PDEs in Higher Dimensions | We present two effective methods for solving high-dimensional partial
differential equations (PDE) based on randomized neural networks. Motivated by
the universal approximation property of this type of networks, both methods
extend the extreme learning machine (ELM) approach from low to high dimensions.
With the first method the unknown solution field in $d$ dimensions is
represented by a randomized feed-forward neural network, in which the
hidden-layer parameters are randomly assigned and fixed while the output-layer
parameters are trained. The PDE and the boundary/initial conditions, as well as
the continuity conditions (for the local variant of the method), are enforced
on a set of random interior/boundary collocation points. The resultant linear
or nonlinear algebraic system, through its least squares solution, provides the
trained values for the network parameters. With the second method the
high-dimensional PDE problem is reformulated through a constrained expression
based on an Approximate variant of the Theory of Functional Connections
(A-TFC), which avoids the exponential growth in the number of terms of TFC as
the dimension increases. The free field function in the A-TFC constrained
expression is represented by a randomized neural network and is trained by a
procedure analogous to the first method. We present ample numerical simulations
for a number of high-dimensional linear/nonlinear stationary/dynamic PDEs to
demonstrate their performance. These methods can produce accurate solutions to
high-dimensional PDEs, in particular with their errors reaching levels not far
from the machine accuracy for relatively lower dimensions. Compared with the
physics-informed neural network (PINN) method, the current method is both
cost-effective and more accurate for high-dimensional PDEs. | [
"Yiran Wang",
"Suchuan Dong"
] | 2023-09-13 15:59:02 | http://arxiv.org/abs/2309.07049v1 | http://arxiv.org/pdf/2309.07049v1 | 2309.07049v1 |
Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks | Comparing graphs by means of optimal transport has recently gained
significant attention, as the distances induced by optimal transport provide
both a principled metric between graphs as well as an interpretable description
of the associated changes between graphs in terms of a transport plan. As the
lack of symmetry introduces challenges in the typically considered
formulations, optimal transport distances for graphs have mostly been developed
for undirected graphs. Here, we propose two distance measures to compare
directed graphs based on variants of optimal transport: (i) an earth movers
distance (Wasserstein) and (ii) a Gromov-Wasserstein (GW) distance. We evaluate
these two distances and discuss their relative performance for both simulated
graph data and real-world directed cell-cell communication graphs, inferred
from single-cell RNA-seq data. | [
"James S. Nagai",
"Ivan G. Costa",
"Michael T. Schaub"
] | 2023-09-13 15:36:39 | http://arxiv.org/abs/2309.07030v2 | http://arxiv.org/pdf/2309.07030v2 | 2309.07030v2 |
Unsupervised Contrast-Consistent Ranking with Language Models | Language models contain ranking-based knowledge and are powerful solvers of
in-context ranking tasks. For instance, they may have parametric knowledge
about the ordering of countries by size or may be able to rank reviews by
sentiment. Recent work focuses on pairwise, pointwise, and listwise prompting
techniques to elicit a language model's ranking knowledge. However, we find
that even with careful calibration and constrained decoding, prompting-based
techniques may not always be self-consistent in the rankings they produce. This
motivates us to explore an alternative approach that is inspired by an
unsupervised probing method called Contrast-Consistent Search (CCS). The idea
is to train a probing model guided by a logical constraint: a model's
representation of a statement and its negation must be mapped to contrastive
true-false poles consistently across multiple statements. We hypothesize that
similar constraints apply to ranking tasks where all items are related via
consistent pairwise or listwise comparisons. To this end, we extend the binary
CCS method to Contrast-Consistent Ranking (CCR) by adapting existing ranking
methods such as the Max-Margin Loss, Triplet Loss, and Ordinal Regression
objective. Our results confirm that, for the same language model, CCR probing
outperforms prompting and even performs on a par with prompting much larger
language models. | [
"Niklas Stoehr",
"Pengxiang Cheng",
"Jing Wang",
"Daniel Preotiuc-Pietro",
"Rajarshi Bhowmik"
] | 2023-09-13 14:36:26 | http://arxiv.org/abs/2309.06991v1 | http://arxiv.org/pdf/2309.06991v1 | 2309.06991v1 |
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments | The main premise of federated learning (FL) is that machine learning model
updates are computed locally to preserve user data privacy. This approach
avoids by design user data to ever leave the perimeter of their device. Once
the updates aggregated, the model is broadcast to all nodes in the federation.
However, without proper defenses, compromised nodes can probe the model inside
their local memory in search for adversarial examples, which can lead to
dangerous real-world scenarios. For instance, in image-based applications,
adversarial examples consist of images slightly perturbed to the human eye
getting misclassified by the local model. These adversarial images are then
later presented to a victim node's counterpart model to replay the attack.
Typical examples harness dissemination strategies such as altered traffic signs
(patch attacks) no longer recognized by autonomous vehicles or seemingly
unaltered samples that poison the local dataset of the FL scheme to undermine
its robustness. Pelta is a novel shielding mechanism leveraging Trusted
Execution Environments (TEEs) that reduce the ability of attackers to craft
adversarial samples. Pelta masks inside the TEE the first part of the
back-propagation chain rule, typically exploited by attackers to craft the
malicious samples. We evaluate Pelta on state-of-the-art accurate models using
three well-established datasets: CIFAR-10, CIFAR-100 and ImageNet. We show the
effectiveness of Pelta in mitigating six white-box state-of-the-art adversarial
attacks, such as Projected Gradient Descent, Momentum Iterative Method, Auto
Projected Gradient Descent, the Carlini & Wagner attack. In particular, Pelta
constitutes the first attempt at defending an ensemble model against the
Self-Attention Gradient attack to the best of our knowledge. Our code is
available to the research community at https://github.com/queyrusi/Pelta. | [
"Simon Queyrut",
"Valerio Schiavoni",
"Pascal Felber"
] | 2023-09-13 14:19:29 | http://arxiv.org/abs/2309.07197v1 | http://arxiv.org/pdf/2309.07197v1 | 2309.07197v1 |
MASTERKEY: Practical Backdoor Attack Against Speaker Verification Systems | Speaker Verification (SV) is widely deployed in mobile systems to
authenticate legitimate users by using their voice traits. In this work, we
propose a backdoor attack MASTERKEY, to compromise the SV models. Different
from previous attacks, we focus on a real-world practical setting where the
attacker possesses no knowledge of the intended victim. To design MASTERKEY, we
investigate the limitation of existing poisoning attacks against unseen
targets. Then, we optimize a universal backdoor that is capable of attacking
arbitrary targets. Next, we embed the speaker's characteristics and semantics
information into the backdoor, making it imperceptible. Finally, we estimate
the channel distortion and integrate it into the backdoor. We validate our
attack on 6 popular SV models. Specifically, we poison a total of 53 models and
use our trigger to attack 16,430 enrolled speakers, composed of 310 target
speakers enrolled in 53 poisoned models. Our attack achieves 100% attack
success rate with a 15% poison rate. By decreasing the poison rate to 3%, the
attack success rate remains around 50%. We validate our attack in 3 real-world
scenarios and successfully demonstrate the attack through both over-the-air and
over-the-telephony-line scenarios. | [
"Hanqing Guo",
"Xun Chen",
"Junfeng Guo",
"Li Xiao",
"Qiben Yan"
] | 2023-09-13 14:15:54 | http://arxiv.org/abs/2309.06981v1 | http://arxiv.org/pdf/2309.06981v1 | 2309.06981v1 |
Auto-Regressive Next-Token Predictors are Universal Learners | Large language models display remarkable capabilities in logical and
mathematical reasoning, allowing them to solve complex tasks. Interestingly,
these abilities emerge in networks trained on the simple task of next-token
prediction. In this work, we present a theoretical framework for studying
auto-regressive next-token predictors. We demonstrate that even simple models
such as linear next-token predictors, trained on Chain-of-Thought (CoT) data,
can approximate any function efficiently computed by a Turing machine. We
introduce a new complexity measure -- length complexity -- which measures the
number of intermediate tokens in a CoT sequence required to approximate some
target function, and analyze the interplay between length complexity and other
notions of complexity. Finally, we show experimentally that simple next-token
predictors, such as linear networks and shallow Multi-Layer Perceptrons (MLPs),
display non-trivial performance on text generation and arithmetic tasks. Our
results demonstrate that the power of language models can be attributed, to a
great extent, to the auto-regressive next-token training scheme, and not
necessarily to a particular choice of architecture. | [
"Eran Malach"
] | 2023-09-13 14:15:03 | http://arxiv.org/abs/2309.06979v1 | http://arxiv.org/pdf/2309.06979v1 | 2309.06979v1 |
DNNShifter: An Efficient DNN Pruning System for Edge Computing | Deep neural networks (DNNs) underpin many machine learning applications.
Production quality DNN models achieve high inference accuracy by training
millions of DNN parameters which has a significant resource footprint. This
presents a challenge for resources operating at the extreme edge of the
network, such as mobile and embedded devices that have limited computational
and memory resources. To address this, models are pruned to create lightweight,
more suitable variants for these devices. Existing pruning methods are unable
to provide similar quality models compared to their unpruned counterparts
without significant time costs and overheads or are limited to offline use
cases. Our work rapidly derives suitable model variants while maintaining the
accuracy of the original model. The model variants can be swapped quickly when
system and network conditions change to match workload demand. This paper
presents DNNShifter, an end-to-end DNN training, spatial pruning, and model
switching system that addresses the challenges mentioned above. At the heart of
DNNShifter is a novel methodology that prunes sparse models using structured
pruning. The pruned model variants generated by DNNShifter are smaller in size
and thus faster than dense and sparse model predecessors, making them suitable
for inference at the edge while retaining near similar accuracy as of the
original dense model. DNNShifter generates a portfolio of model variants that
can be swiftly interchanged depending on operational conditions. DNNShifter
produces pruned model variants up to 93x faster than conventional training
methods. Compared to sparse models, the pruned model variants are up to 5.14x
smaller and have a 1.67x inference latency speedup, with no compromise to
sparse model accuracy. In addition, DNNShifter has up to 11.9x lower overhead
for switching models and up to 3.8x lower memory utilisation than existing
approaches. | [
"Bailey J. Eccles",
"Philip Rodgers",
"Peter Kilpatrick",
"Ivor Spence",
"Blesson Varghese"
] | 2023-09-13 14:05:50 | http://arxiv.org/abs/2309.06973v1 | http://arxiv.org/pdf/2309.06973v1 | 2309.06973v1 |
Setting the Right Expectations: Algorithmic Recourse Over Time | Algorithmic systems are often called upon to assist in high-stakes decision
making. In light of this, algorithmic recourse, the principle wherein
individuals should be able to take action against an undesirable outcome made
by an algorithmic system, is receiving growing attention. The bulk of the
literature on algorithmic recourse to-date focuses primarily on how to provide
recourse to a single individual, overlooking a critical element: the effects of
a continuously changing context. Disregarding these effects on recourse is a
significant oversight, since, in almost all cases, recourse consists of an
individual making a first, unfavorable attempt, and then being given an
opportunity to make one or several attempts at a later date - when the context
might have changed. This can create false expectations, as initial recourse
recommendations may become less reliable over time due to model drift and
competition for access to the favorable outcome between individuals.
In this work we propose an agent-based simulation framework for studying the
effects of a continuously changing environment on algorithmic recourse. In
particular, we identify two main effects that can alter the reliability of
recourse for individuals represented by the agents: (1) competition with other
agents acting upon recourse, and (2) competition with new agents entering the
environment. Our findings highlight that only a small set of specific
parameterizations result in algorithmic recourse that is reliable for agents
over time. Consequently, we argue that substantial additional work is needed to
understand recourse reliability over time, and to develop recourse methods that
reward agents' effort. | [
"Joao Fonseca",
"Andrew Bell",
"Carlo Abrate",
"Francesco Bonchi",
"Julia Stoyanovich"
] | 2023-09-13 14:04:15 | http://arxiv.org/abs/2309.06969v1 | http://arxiv.org/pdf/2309.06969v1 | 2309.06969v1 |
Attention-based Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction in Highway Transportation | As one of the important tools for spatial feature extraction, graph
convolution has been applied in a wide range of fields such as traffic flow
prediction. However, current popular works of graph convolution cannot
guarantee spatio-temporal consistency in a long period. The ignorance of
correlational dynamics, convolutional locality and temporal comprehensiveness
would limit predictive accuracy. In this paper, a novel Attention-based Dynamic
Graph Convolutional Recurrent Neural Network (ADGCRNN) is proposed to improve
traffic flow prediction in highway transportation. Three temporal resolutions
of data sequence are effectively integrated by self-attention to extract
characteristics; multi-dynamic graphs and their weights are dynamically created
to compliantly combine the varying characteristics; a dedicated gated kernel
emphasizing highly relative nodes is introduced on these complete graphs to
reduce overfitting for graph convolution operations. Experiments on two public
datasets show our work better than state-of-the-art baselines, and case studies
of a real Web system prove practical benefit in highway transportation. | [
"Tianpu Zhang",
"Weilong Ding",
"Mengda Xing"
] | 2023-09-13 13:57:21 | http://arxiv.org/abs/2309.07196v1 | http://arxiv.org/pdf/2309.07196v1 | 2309.07196v1 |
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization | Open vocabulary keyword spotting is a crucial and challenging task in
automatic speech recognition (ASR) that focuses on detecting user-defined
keywords within a spoken utterance. Keyword spotting methods commonly map the
audio utterance and keyword into a joint embedding space to obtain some
affinity score. In this work, we propose AdaKWS, a novel method for keyword
spotting in which a text encoder is trained to output keyword-conditioned
normalization parameters. These parameters are used to process the auditory
input. We provide an extensive evaluation using challenging and diverse
multi-lingual benchmarks and show significant improvements over recent keyword
spotting and ASR baselines. Furthermore, we study the effectiveness of our
approach on low-resource languages that were unseen during the training. The
results demonstrate a substantial performance improvement compared to baseline
methods. | [
"Aviv Navon",
"Aviv Shamsian",
"Neta Glazer",
"Gill Hetz",
"Joseph Keshet"
] | 2023-09-13 13:49:42 | http://arxiv.org/abs/2309.08561v1 | http://arxiv.org/pdf/2309.08561v1 | 2309.08561v1 |
Implicit Neural Multiple Description for DNA-based data storage | DNA exhibits remarkable potential as a data storage solution due to its
impressive storage density and long-term stability, stemming from its inherent
biomolecular structure. However, developing this novel medium comes with its
own set of challenges, particularly in addressing errors arising from storage
and biological manipulations. These challenges are further conditioned by the
structural constraints of DNA sequences and cost considerations. In response to
these limitations, we have pioneered a novel compression scheme and a
cutting-edge Multiple Description Coding (MDC) technique utilizing neural
networks for DNA data storage. Our MDC method introduces an innovative approach
to encoding data into DNA, specifically designed to withstand errors
effectively. Notably, our new compression scheme overperforms classic image
compression methods for DNA-data storage. Furthermore, our approach exhibits
superiority over conventional MDC methods reliant on auto-encoders. Its
distinctive strengths lie in its ability to bypass the need for extensive model
training and its enhanced adaptability for fine-tuning redundancy levels.
Experimental results demonstrate that our solution competes favorably with the
latest DNA data storage methods in the field, offering superior compression
rates and robust noise resilience. | [
"Trung Hieu Le",
"Xavier Pic",
"Jeremy Mateos",
"Marc Antonini"
] | 2023-09-13 13:42:52 | http://arxiv.org/abs/2309.06956v1 | http://arxiv.org/pdf/2309.06956v1 | 2309.06956v1 |
Effect of hyperparameters on variable selection in random forests | Random forests (RFs) are well suited for prediction modeling and variable
selection in high-dimensional omics studies. The effect of hyperparameters of
the RF algorithm on prediction performance and variable importance estimation
have previously been investigated. However, how hyperparameters impact RF-based
variable selection remains unclear. We evaluate the effects on the Vita and the
Boruta variable selection procedures based on two simulation studies utilizing
theoretical distributions and empirical gene expression data. We assess the
ability of the procedures to select important variables (sensitivity) while
controlling the false discovery rate (FDR). Our results show that the
proportion of splitting candidate variables (mtry.prop) and the sample fraction
(sample.fraction) for the training dataset influence the selection procedures
more than the drawing strategy of the training datasets and the minimal
terminal node size. A suitable setting of the RF hyperparameters depends on the
correlation structure in the data. For weakly correlated predictor variables,
the default value of mtry is optimal, but smaller values of sample.fraction
result in larger sensitivity. In contrast, the difference in sensitivity of the
optimal compared to the default value of sample.fraction is negligible for
strongly correlated predictor variables, whereas smaller values than the
default are better in the other settings. In conclusion, the default values of
the hyperparameters will not always be suitable for identifying important
variables. Thus, adequate values differ depending on whether the aim of the
study is optimizing prediction performance or variable selection. | [
"Cesaire J. K. Fouodo",
"Lea L. Kronziel",
"Inke R. König",
"Silke Szymczak"
] | 2023-09-13 13:26:10 | http://arxiv.org/abs/2309.06943v1 | http://arxiv.org/pdf/2309.06943v1 | 2309.06943v1 |
Collectionless Artificial Intelligence | By and large, the professional handling of huge data collections is regarded
as a fundamental ingredient of the progress of machine learning and of its
spectacular results in related disciplines, with a growing agreement on risks
connected to the centralization of such data collections. This paper sustains
the position that the time has come for thinking of new learning protocols
where machines conquer cognitive skills in a truly human-like context centered
on environmental interactions. This comes with specific restrictions on the
learning protocol according to the collectionless principle, which states that,
at each time instant, data acquired from the environment is processed with the
purpose of contributing to update the current internal representation of the
environment, and that the agent is not given the privilege of recording the
temporal stream. Basically, there is neither permission to store the temporal
information coming from the sensors, thus promoting the development of
self-organized memorization skills at a more abstract level, instead of relying
on bare storage to simulate learning dynamics that are typical of offline
learning algorithms. This purposely extreme position is intended to stimulate
the development of machines that learn to dynamically organize the information
by following human-based schemes. The proposition of this challenge suggests
developing new foundations on computational processes of learning and reasoning
that might open the doors to a truly orthogonal competitive track on AI
technologies that avoid data accumulation by design, thus offering a framework
which is better suited concerning privacy issues, control and customizability.
Finally, pushing towards massively distributed computation, the collectionless
approach to AI will likely reduce the concentration of power in companies and
governments, thus better facing geopolitical issues. | [
"Marco Gori",
"Stefano Melacci"
] | 2023-09-13 13:20:17 | http://arxiv.org/abs/2309.06938v2 | http://arxiv.org/pdf/2309.06938v2 | 2309.06938v2 |
A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning | Online reinforcement learning (RL) methods are often data-inefficient or
unreliable, making them difficult to train on real robotic hardware, especially
quadruped robots. Learning robotic tasks from pre-collected data is a promising
direction. Meanwhile, agile and stable legged robotic locomotion remains an
open question in their general form. Offline reinforcement learning (ORL) has
the potential to make breakthroughs in this challenging field, but its current
bottleneck lies in the lack of diverse datasets for challenging realistic
tasks. To facilitate the development of ORL, we benchmarked 11 ORL algorithms
in the realistic quadrupedal locomotion dataset. Such dataset is collected by
the classic model predictive control (MPC) method, rather than the model-free
online RL method commonly used by previous benchmarks. Extensive experimental
results show that the best-performing ORL algorithms can achieve competitive
performance compared with the model-free RL, and even surpass it in some tasks.
However, there is still a gap between the learning-based methods and MPC,
especially in terms of stability and rapid adaptation. Our proposed benchmark
will serve as a development platform for testing and evaluating the performance
of ORL algorithms in real-world legged locomotion tasks. | [
"Hongyin Zhang",
"Shuyu Yang",
"Donglin Wang"
] | 2023-09-13 13:18:29 | http://arxiv.org/abs/2309.16718v1 | http://arxiv.org/pdf/2309.16718v1 | 2309.16718v1 |
Modeling Dislocation Dynamics Data Using Semantic Web Technologies | Research in the field of Materials Science and Engineering focuses on the
design, synthesis, properties, and performance of materials. An important class
of materials that is widely investigated are crystalline materials, including
metals and semiconductors. Crystalline material typically contains a distinct
type of defect called "dislocation". This defect significantly affects various
material properties, including strength, fracture toughness, and ductility.
Researchers have devoted a significant effort in recent years to understanding
dislocation behavior through experimental characterization techniques and
simulations, e.g., dislocation dynamics simulations. This paper presents how
data from dislocation dynamics simulations can be modeled using semantic web
technologies through annotating data with ontologies. We extend the already
existing Dislocation Ontology by adding missing concepts and aligning it with
two other domain-related ontologies (i.e., the Elementary Multi-perspective
Material Ontology and the Materials Design Ontology) allowing for representing
the dislocation simulation data efficiently. Moreover, we show a real-world use
case by representing the discrete dislocation dynamics data as a knowledge
graph (DisLocKG) that illustrates the relationship between them. We also
developed a SPARQL endpoint that brings extensive flexibility to query
DisLocKG. | [
"Ahmad Zainul Ihsan",
"Said Fathalla",
"Stefan Sandfeld"
] | 2023-09-13 13:03:44 | http://arxiv.org/abs/2309.06930v1 | http://arxiv.org/pdf/2309.06930v1 | 2309.06930v1 |
Investigating the Impact of Action Representations in Policy Gradient Algorithms | Reinforcement learning~(RL) is a versatile framework for learning to solve
complex real-world tasks. However, influences on the learning performance of RL
algorithms are often poorly understood in practice. We discuss different
analysis techniques and assess their effectiveness for investigating the impact
of action representations in RL. Our experiments demonstrate that the action
representation can significantly influence the learning performance on popular
RL benchmark tasks. The analysis results indicate that some of the performance
differences can be attributed to changes in the complexity of the optimization
landscape. Finally, we discuss open challenges of analysis techniques for RL
algorithms. | [
"Jan Schneider",
"Pierre Schumacher",
"Daniel Häufle",
"Bernhard Schölkopf",
"Dieter Büchler"
] | 2023-09-13 12:41:45 | http://arxiv.org/abs/2309.06921v1 | http://arxiv.org/pdf/2309.06921v1 | 2309.06921v1 |
Continual Learning with Dirichlet Generative-based Rehearsal | Recent advancements in data-driven task-oriented dialogue systems (ToDs)
struggle with incremental learning due to computational constraints and
time-consuming issues. Continual Learning (CL) attempts to solve this by
avoiding intensive pre-training, but it faces the problem of catastrophic
forgetting (CF). While generative-based rehearsal CL methods have made
significant strides, generating pseudo samples that accurately reflect the
underlying task-specific distribution is still a challenge. In this paper, we
present Dirichlet Continual Learning (DCL), a novel generative-based rehearsal
strategy for CL. Unlike the traditionally used Gaussian latent variable in the
Conditional Variational Autoencoder (CVAE), DCL leverages the flexibility and
versatility of the Dirichlet distribution to model the latent prior variable.
This enables it to efficiently capture sentence-level features of previous
tasks and effectively guide the generation of pseudo samples. In addition, we
introduce Jensen-Shannon Knowledge Distillation (JSKD), a robust logit-based
knowledge distillation method that enhances knowledge transfer during pseudo
sample generation. Our experiments confirm the efficacy of our approach in both
intent detection and slot-filling tasks, outperforming state-of-the-art
methods. | [
"Min Zeng",
"Wei Xue",
"Qifeng Liu",
"Yike Guo"
] | 2023-09-13 12:30:03 | http://arxiv.org/abs/2309.06917v1 | http://arxiv.org/pdf/2309.06917v1 | 2309.06917v1 |
Towards the TopMost: A Topic Modeling System Toolkit | Topic models have been proposed for decades with various applications and
recently refreshed by the neural variational inference. However, these topic
models adopt totally distinct dataset, implementation, and evaluation settings,
which hinders their quick utilization and fair comparisons. This greatly
hinders the research progress of topic models. To address these issues, in this
paper we propose a Topic Modeling System Toolkit (TopMost). Compared to
existing toolkits, TopMost stands out by covering a wider range of topic
modeling scenarios including complete lifecycles with dataset pre-processing,
model training, testing, and evaluations. The highly cohesive and decoupled
modular design of TopMost enables quick utilization, fair comparisons, and
flexible extensions of different topic models. This can facilitate the research
and applications of topic models. Our code, tutorials, and documentation are
available at https://github.com/bobxwu/topmost. | [
"Xiaobao Wu",
"Fengjun Pan",
"Anh Tuan Luu"
] | 2023-09-13 12:10:54 | http://arxiv.org/abs/2309.06908v1 | http://arxiv.org/pdf/2309.06908v1 | 2309.06908v1 |
Domain-Aware Augmentations for Unsupervised Online General Continual Learning | Continual Learning has been challenging, especially when dealing with
unsupervised scenarios such as Unsupervised Online General Continual Learning
(UOGCL), where the learning agent has no prior knowledge of class boundaries or
task change information. While previous research has focused on reducing
forgetting in supervised setups, recent studies have shown that self-supervised
learners are more resilient to forgetting. This paper proposes a novel approach
that enhances memory usage for contrastive learning in UOGCL by defining and
using stream-dependent data augmentations together with some implementation
tricks. Our proposed method is simple yet effective, achieves state-of-the-art
results compared to other unsupervised approaches in all considered setups, and
reduces the gap between supervised and unsupervised continual learning. Our
domain-aware augmentation procedure can be adapted to other replay-based
methods, making it a promising strategy for continual learning. | [
"Nicolas Michel",
"Romain Negrel",
"Giovanni Chierchia",
"Jean-François Bercher"
] | 2023-09-13 11:45:21 | http://arxiv.org/abs/2309.06896v1 | http://arxiv.org/pdf/2309.06896v1 | 2309.06896v1 |
MagiCapture: High-Resolution Multi-Concept Portrait Customization | Large-scale text-to-image models including Stable Diffusion are capable of
generating high-fidelity photorealistic portrait images. There is an active
research area dedicated to personalizing these models, aiming to synthesize
specific subjects or styles using provided sets of reference images. However,
despite the plausible results from these personalization methods, they tend to
produce images that often fall short of realism and are not yet on a
commercially viable level. This is particularly noticeable in portrait image
generation, where any unnatural artifact in human faces is easily discernible
due to our inherent human bias. To address this, we introduce MagiCapture, a
personalization method for integrating subject and style concepts to generate
high-resolution portrait images using just a few subject and style references.
For instance, given a handful of random selfies, our fine-tuned model can
generate high-quality portrait images in specific styles, such as passport or
profile photos. The main challenge with this task is the absence of ground
truth for the composed concepts, leading to a reduction in the quality of the
final output and an identity shift of the source subject. To address these
issues, we present a novel Attention Refocusing loss coupled with auxiliary
priors, both of which facilitate robust learning within this weakly supervised
learning setting. Our pipeline also includes additional post-processing steps
to ensure the creation of highly realistic outputs. MagiCapture outperforms
other baselines in both quantitative and qualitative evaluations and can also
be generalized to other non-human objects. | [
"Junha Hyung",
"Jaeyo Shin",
"Jaegul Choo"
] | 2023-09-13 11:37:04 | http://arxiv.org/abs/2309.06895v1 | http://arxiv.org/pdf/2309.06895v1 | 2309.06895v1 |
Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit? | Convolutional networks and vision transformers have different forms of
pairwise interactions, pooling across layers and pooling at the end of the
network. Does the latter really need to be different? As a by-product of
pooling, vision transformers provide spatial attention for free, but this is
most often of low quality unless self-supervised, which is not well studied. Is
supervision really the problem?
In this work, we develop a generic pooling framework and then we formulate a
number of existing methods as instantiations. By discussing the properties of
each group of methods, we derive SimPool, a simple attention-based pooling
mechanism as a replacement of the default one for both convolutional and
transformer encoders. We find that, whether supervised or self-supervised, this
improves performance on pre-training and downstream tasks and provides
attention maps delineating object boundaries in all cases. One could thus call
SimPool universal. To our knowledge, we are the first to obtain attention maps
in supervised transformers of at least as good quality as self-supervised,
without explicit losses or modifying the architecture. Code at:
https://github.com/billpsomas/simpool. | [
"Bill Psomas",
"Ioannis Kakogeorgiou",
"Konstantinos Karantzalos",
"Yannis Avrithis"
] | 2023-09-13 11:28:27 | http://arxiv.org/abs/2309.06891v1 | http://arxiv.org/pdf/2309.06891v1 | 2309.06891v1 |
ProMap: Datasets for Product Mapping in E-commerce | The goal of product mapping is to decide, whether two listings from two
different e-shops describe the same products. Existing datasets of matching and
non-matching pairs of products, however, often suffer from incomplete product
information or contain only very distant non-matching products. Therefore,
while predictive models trained on these datasets achieve good results on them,
in practice, they are unusable as they cannot distinguish very similar but
non-matching pairs of products. This paper introduces two new datasets for
product mapping: ProMapCz consisting of 1,495 Czech product pairs and ProMapEn
consisting of 1,555 English product pairs of matching and non-matching products
manually scraped from two pairs of e-shops. The datasets contain both images
and textual descriptions of the products, including their specifications,
making them one of the most complete datasets for product mapping.
Additionally, the non-matching products were selected in two phases, creating
two types of non-matches -- close non-matches and medium non-matches. Even the
medium non-matches are pairs of products that are much more similar than
non-matches in other datasets -- for example, they still need to have the same
brand and similar name and price. After simple data preprocessing, several
machine learning algorithms were trained on these and two the other datasets to
demonstrate the complexity and completeness of ProMap datasets. ProMap datasets
are presented as a golden standard for further research of product mapping
filling the gaps in existing ones. | [
"Kateřina Macková",
"Martin Pilát"
] | 2023-09-13 11:16:52 | http://arxiv.org/abs/2309.06882v1 | http://arxiv.org/pdf/2309.06882v1 | 2309.06882v1 |
A Robust SINDy Approach by Combining Neural Networks and an Integral Form | The discovery of governing equations from data has been an active field of
research for decades. One widely used methodology for this purpose is sparse
regression for nonlinear dynamics, known as SINDy. Despite several attempts,
noisy and scarce data still pose a severe challenge to the success of the SINDy
approach. In this work, we discuss a robust method to discover nonlinear
governing equations from noisy and scarce data. To do this, we make use of
neural networks to learn an implicit representation based on measurement data
so that not only it produces the output in the vicinity of the measurements but
also the time-evolution of output can be described by a dynamical system.
Additionally, we learn such a dynamic system in the spirit of the SINDy
framework. Leveraging the implicit representation using neural networks, we
obtain the derivative information -- required for SINDy -- using an automatic
differentiation tool. To enhance the robustness of our methodology, we further
incorporate an integral condition on the output of the implicit networks.
Furthermore, we extend our methodology to handle data collected from multiple
initial conditions. We demonstrate the efficiency of the proposed methodology
to discover governing equations under noisy and scarce data regimes by means of
several examples and compare its performance with existing methods. | [
"Ali Forootani",
"Pawan Goyal",
"Peter Benner"
] | 2023-09-13 10:50:04 | http://arxiv.org/abs/2309.07193v1 | http://arxiv.org/pdf/2309.07193v1 | 2309.07193v1 |
The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection | Machine Learning (ML) has emerged as a promising approach in healthcare,
outperforming traditional statistical techniques. However, to establish ML as a
reliable tool in clinical practice, adherence to best practices regarding data
handling, experimental design, and model evaluation is crucial. This work
summarizes and strictly observes such practices to ensure reproducible and
reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection,
which serves as a paradigmatic example of challenging problem in healthcare. We
investigate the impact of different data augmentation techniques and model
complexity on the overall performance. We consider MRI data from ADNI dataset
to address a classification problem employing 3D Convolutional Neural Network
(CNN). The experiments are designed to compensate for data scarcity and initial
random parameters by utilizing cross-validation and multiple training trials.
Within this framework, we train 15 predictive models, considering three
different data augmentation strategies and five distinct 3D CNN architectures,
each varying in the number of convolutional layers. Specifically, the
augmentation strategies are based on affine transformations, such as zoom,
shift, and rotation, applied concurrently or separately. The combined effect of
data augmentation and model complexity leads to a variation in prediction
performance up to 10% of accuracy. When affine transformation are applied
separately, the model is more accurate, independently from the adopted
architecture. For all strategies, the model accuracy followed a concave
behavior at increasing number of convolutional layers, peaking at an
intermediate value of layers. The best model (8 CL, (B)) is the most stable
across cross-validation folds and training trials, reaching excellent
performance both on the testing set and on an external test set. | [
"Rosanna Turrisi",
"Alessandro Verri",
"Annalisa Barla"
] | 2023-09-13 10:40:41 | http://arxiv.org/abs/2309.07192v1 | http://arxiv.org/pdf/2309.07192v1 | 2309.07192v1 |
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning | We propose reinforcement learning to control the dynamical self-assembly of
the dodecagonal quasicrystal (DDQC) from patchy particles. The patchy particles
have anisotropic interactions with other particles and form DDQC. However,
their structures at steady states are significantly influenced by the kinetic
pathways of their structural formation. We estimate the best policy of
temperature control trained by the Q-learning method and demonstrate that we
can generate DDQC with few defects using the estimated policy. The temperature
schedule obtained by reinforcement learning can reproduce the desired structure
more efficiently than the conventional pre-fixed temperature schedule, such as
annealing. To clarify the success of the learning, we also analyse a simple
model describing the kinetics of structural changes through the motion in a
triple-well potential. We have found that reinforcement learning autonomously
discovers the critical temperature at which structural fluctuations enhance the
chance of forming a globally stable state. The estimated policy guides the
system toward the critical temperature to assist the formation of DDQC. | [
"Uyen Tu Lieu",
"Natsuhiko Yoshinaga"
] | 2023-09-13 10:26:08 | http://arxiv.org/abs/2309.06869v1 | http://arxiv.org/pdf/2309.06869v1 | 2309.06869v1 |
Supervised Machine Learning and Physics based Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy | Additive friction stir deposition (AFSD) is a novel solid-state additive
manufacturing technique that circumvents issues of porosity, cracking, and
properties anisotropy that plague traditional powder bed fusion and directed
energy deposition approaches. However, correlations between process parameters,
thermal profiles, and resulting microstructure in AFSD remain poorly
understood. This hinders process optimization for properties. This work employs
a cutting-edge framework combining supervised machine learning (SML) and
physics-informed neural networks (PINNs) to predict peak temperature
distribution in AFSD from process parameters. Eight regression algorithms were
implemented for SML modeling, while four PINNs leveraged governing equations
for transport, wave propagation, heat transfer, and quantum mechanics. Across
multiple statistical measures, ensemble techniques like gradient boosting
proved superior for SML, with lowest MSE of 165.78. The integrated ML approach
was also applied to classify deposition quality from process factors, with
logistic regression delivering robust accuracy. By fusing data-driven learning
and fundamental physics, this dual methodology provides comprehensive insights
into tailoring microstructure through thermal management in AFSD. The work
demonstrates the power of bridging statistical and physics-based modeling for
elucidating AM process-property relationships. | [
"Akshansh Mishra"
] | 2023-09-13 09:39:42 | http://arxiv.org/abs/2309.06838v1 | http://arxiv.org/pdf/2309.06838v1 | 2309.06838v1 |
Safe Reinforcement Learning with Dual Robustness | Reinforcement learning (RL) agents are vulnerable to adversarial
disturbances, which can deteriorate task performance or compromise safety
specifications. Existing methods either address safety requirements under the
assumption of no adversary (e.g., safe RL) or only focus on robustness against
performance adversaries (e.g., robust RL). Learning one policy that is both
safe and robust remains a challenging open problem. The difficulty is how to
tackle two intertwined aspects in the worst cases: feasibility and optimality.
Optimality is only valid inside a feasible region, while identification of
maximal feasible region must rely on learning the optimal policy. To address
this issue, we propose a systematic framework to unify safe RL and robust RL,
including problem formulation, iteration scheme, convergence analysis and
practical algorithm design. This unification is built upon constrained
two-player zero-sum Markov games. A dual policy iteration scheme is proposed,
which simultaneously optimizes a task policy and a safety policy. The
convergence of this iteration scheme is proved. Furthermore, we design a deep
RL algorithm for practical implementation, called dually robust actor-critic
(DRAC). The evaluations with safety-critical benchmarks demonstrate that DRAC
achieves high performance and persistent safety under all scenarios (no
adversary, safety adversary, performance adversary), outperforming all
baselines significantly. | [
"Zeyang Li",
"Chuxiong Hu",
"Yunan Wang",
"Yujie Yang",
"Shengbo Eben Li"
] | 2023-09-13 09:34:21 | http://arxiv.org/abs/2309.06835v1 | http://arxiv.org/pdf/2309.06835v1 | 2309.06835v1 |
Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization | Federated learning algorithms perform reasonably well on independent and
identically distributed (IID) data. They, on the other hand, suffer greatly
from heterogeneous environments, i.e., Non-IID data. Despite the fact that many
research projects have been done to address this issue, recent findings
indicate that they are still sub-optimal when compared to training on IID data.
In this work, we carefully analyze the existing methods in heterogeneous
environments. Interestingly, we find that regularizing the classifier's outputs
is quite effective in preventing performance degradation on Non-IID data.
Motivated by this, we propose Learning from Drift (LfD), a novel method for
effectively training the model in heterogeneous settings. Our scheme
encapsulates two key components: drift estimation and drift regularization.
Specifically, LfD first estimates how different the local model is from the
global model (i.e., drift). The local model is then regularized such that it
does not fall in the direction of the estimated drift. In the experiment, we
evaluate each method through the lens of the five aspects of federated
learning, i.e., Generalization, Heterogeneity, Scalability, Forgetting, and
Efficiency. Comprehensive evaluation results clearly support the superiority of
LfD in federated learning with Non-IID data. | [
"Yeachan Kim",
"Bonggun Shin"
] | 2023-09-13 09:23:09 | http://arxiv.org/abs/2309.07189v1 | http://arxiv.org/pdf/2309.07189v1 | 2309.07189v1 |
UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training | Magnetic resonance imaging~(MRI) have played a crucial role in brain disease
diagnosis, with which a range of computer-aided artificial intelligence methods
have been proposed. However, the early explorations usually focus on the
limited types of brain diseases in one study and train the model on the data in
a small scale, yielding the bottleneck of generalization. Towards a more
effective and scalable paradigm, we propose a hierarchical knowledge-enhanced
pre-training framework for the universal brain MRI diagnosis, termed as
UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770
imaging-report pairs from routine diagnostics. Different from previous
pre-training techniques for the unitary vision or textual feature, or with the
brute-force alignment between vision and language information, we leverage the
unique characteristic of report information in different granularity to build a
hierarchical alignment mechanism, which strengthens the efficiency in feature
learning. Our UniBrain is validated on three real world datasets with severe
class imbalance and the public BraTS2019 dataset. It not only consistently
outperforms all state-of-the-art diagnostic methods by a large margin and
provides a superior grounding performance but also shows comparable performance
compared to expert radiologists on certain disease types. | [
"Jiayu Lei",
"Lisong Dai",
"Haoyun Jiang",
"Chaoyi Wu",
"Xiaoman Zhang",
"Yao Zhang",
"Jiangchao Yao",
"Weidi Xie",
"Yanyong Zhang",
"Yuehua Li",
"Ya Zhang",
"Yanfeng Wang"
] | 2023-09-13 09:22:49 | http://arxiv.org/abs/2309.06828v1 | http://arxiv.org/pdf/2309.06828v1 | 2309.06828v1 |
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models | Contextual Relation Extraction (CRE) is mainly used for constructing a
knowledge graph with a help of ontology. It performs various tasks such as
semantic search, query answering, and textual entailment. Relation extraction
identifies the entities from raw texts and the relations among them. An
efficient and accurate CRE system is essential for creating domain knowledge in
the biomedical industry. Existing Machine Learning and Natural Language
Processing (NLP) techniques are not suitable to predict complex relations from
sentences that consist of more than two relations and unspecified entities
efficiently. In this work, deep learning techniques have been used to identify
the appropriate semantic relation based on the context from multiple sentences.
Even though various machine learning models have been used for relation
extraction, they provide better results only for binary relations, i.e.,
relations occurred exactly between the two entities in a sentence. Machine
learning models are not suited for complex sentences that consist of the words
that have various meanings. To address these issues, hybrid deep learning
models have been used to extract the relations from complex sentence
effectively. This paper explores the analysis of various deep learning models
that are used for relation extraction. | [
"R. Priyadharshini",
"G. Jeyakodi",
"P. Shanthi Bala"
] | 2023-09-13 09:05:09 | http://arxiv.org/abs/2309.06814v1 | http://arxiv.org/pdf/2309.06814v1 | 2309.06814v1 |
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization | Federated Learning (FL) has been successfully adopted for distributed
training and inference of large-scale Deep Neural Networks (DNNs). However,
DNNs are characterized by an extremely large number of parameters, thus,
yielding significant challenges in exchanging these parameters among
distributed nodes and managing the memory. Although recent DNN compression
methods (e.g., sparsification, pruning) tackle such challenges, they do not
holistically consider an adaptively controlled reduction of parameter exchange
while maintaining high accuracy levels. We, therefore, contribute with a novel
FL framework (coined FedDIP), which combines (i) dynamic model pruning with
error feedback to eliminate redundant information exchange, which contributes
to significant performance improvement, with (ii) incremental regularization
that can achieve \textit{extreme} sparsity of models. We provide convergence
analysis of FedDIP and report on a comprehensive performance and comparative
assessment against state-of-the-art methods using benchmark data sets and DNN
models. Our results showcase that FedDIP not only controls the model sparsity
but efficiently achieves similar or better performance compared to other model
pruning methods adopting incremental regularization during distributed model
training. The code is available at: https://github.com/EricLoong/feddip. | [
"Qianyu Long",
"Christos Anagnostopoulos",
"Shameem Puthiya Parambath",
"Daning Bi"
] | 2023-09-13 08:51:19 | http://arxiv.org/abs/2309.06805v1 | http://arxiv.org/pdf/2309.06805v1 | 2309.06805v1 |
Uncertainty-aware Traffic Prediction under Missing Data | Traffic prediction is a crucial topic because of its broad scope of
applications in the transportation domain. Recently, various studies have
achieved promising results. However, most studies assume the prediction
locations have complete or at least partial historical records and cannot be
extended to non-historical recorded locations. In real-life scenarios, the
deployment of sensors could be limited due to budget limitations and
installation availability, which makes most current models not applicable.
Though few pieces of literature tried to impute traffic states at the missing
locations, these methods need the data simultaneously observed at the locations
with sensors, making them not applicable to prediction tasks. Another drawback
is the lack of measurement of uncertainty in prediction, making prior works
unsuitable for risk-sensitive tasks or involving decision-making. To fill the
gap, inspired by the previous inductive graph neural network, this work
proposed an uncertainty-aware framework with the ability to 1) extend
prediction to missing locations with no historical records and significantly
extend spatial coverage of prediction locations while reducing deployment of
sensors and 2) generate probabilistic prediction with uncertainty
quantification to help the management of risk and decision making in the
down-stream tasks. Through extensive experiments on real-life datasets, the
result shows our method achieved promising results on prediction tasks, and the
uncertainty quantification gives consistent results which highly correlated
with the locations with and without historical data. We also show that our
model could help support sensor deployment tasks in the transportation field to
achieve higher accuracy with a limited sensor deployment budget. | [
"Hao Mei",
"Junxian Li",
"Zhiming Liang",
"Guanjie Zheng",
"Bin Shi",
"Hua Wei"
] | 2023-09-13 08:48:00 | http://arxiv.org/abs/2309.06800v4 | http://arxiv.org/pdf/2309.06800v4 | 2309.06800v4 |
Cognitive Mirage: A Review of Hallucinations in Large Language Models | As large language models continue to develop in the field of AI, text
generation systems are susceptible to a worrisome phenomenon known as
hallucination. In this study, we summarize recent compelling insights into
hallucinations in LLMs. We present a novel taxonomy of hallucinations from
various text generation tasks, thus provide theoretical insights, detection
methods and improvement approaches. Based on this, future research directions
are proposed. Our contribution are threefold: (1) We provide a detailed and
complete taxonomy for hallucinations appearing in text generation tasks; (2) We
provide theoretical analyses of hallucinations in LLMs and provide existing
detection and improvement methods; (3) We propose several research directions
that can be developed in the future. As hallucinations garner significant
attention from the community, we will maintain updates on relevant research
progress. | [
"Hongbin Ye",
"Tong Liu",
"Aijia Zhang",
"Wei Hua",
"Weiqiang Jia"
] | 2023-09-13 08:33:09 | http://arxiv.org/abs/2309.06794v1 | http://arxiv.org/pdf/2309.06794v1 | 2309.06794v1 |
Predicting Survival Time of Ball Bearings in the Presence of Censoring | Ball bearings find widespread use in various manufacturing and mechanical
domains, and methods based on machine learning have been widely adopted in the
field to monitor wear and spot defects before they lead to failures. Few
studies, however, have addressed the problem of censored data, in which failure
is not observed. In this paper, we propose a novel approach to predict the time
to failure in ball bearings using survival analysis. First, we analyze bearing
data in the frequency domain and annotate when a bearing fails by comparing the
Kullback-Leibler divergence and the standard deviation between its break-in
frequency bins and its break-out frequency bins. Second, we train several
survival models to estimate the time to failure based on the annotated data and
covariates extracted from the time domain, such as skewness, kurtosis and
entropy. The models give a probabilistic prediction of risk over time and allow
us to compare the survival function between groups of bearings. We demonstrate
our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a
0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best
is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates
further work on incorporating censored data in models for predictive
maintenance. | [
"Christian Marius Lillelund",
"Fernando Pannullo",
"Morten Opprud Jakobsen",
"Christian Fischer Pedersen"
] | 2023-09-13 08:30:31 | http://arxiv.org/abs/2309.07188v1 | http://arxiv.org/pdf/2309.07188v1 | 2309.07188v1 |
Electricity Demand Forecasting through Natural Language Processing with Long Short-Term Memory Networks | Electricity demand forecasting is a well established research field. Usually
this task is performed considering historical loads, weather forecasts,
calendar information and known major events. Recently attention has been given
on the possible use of new sources of information from textual news in order to
improve the performance of these predictions. This paper proposes a Long and
Short-Term Memory (LSTM) network incorporating textual news features that
successfully predicts the deterministic and probabilistic tasks of the UK
national electricity demand. The study finds that public sentiment and word
vector representations related to transport and geopolitics have
time-continuity effects on electricity demand. The experimental results show
that the LSTM with textual features improves by more than 3% compared to the
pure LSTM benchmark and by close to 10% over the official benchmark.
Furthermore, the proposed model effectively reduces forecasting uncertainty by
narrowing the confidence interval and bringing the forecast distribution closer
to the truth. | [
"Yun Bai",
"Simon Camal",
"Andrea Michiorri"
] | 2023-09-13 08:28:16 | http://arxiv.org/abs/2309.06793v1 | http://arxiv.org/pdf/2309.06793v1 | 2309.06793v1 |
Generative AI | The term "generative AI" refers to computational techniques that are capable
of generating seemingly new, meaningful content such as text, images, or audio
from training data. The widespread diffusion of this technology with examples
such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we
work and communicate with each other. In this article, we provide a
conceptualization of generative AI as an entity in socio-technical systems and
provide examples of models, systems, and applications. Based on that, we
introduce limitations of current generative AI and provide an agenda for
Business & Information Systems Engineering (BISE) research. Different from
previous works, we focus on generative AI in the context of information
systems, and, to this end, we discuss several opportunities and challenges that
are unique to the BISE community and make suggestions for impactful directions
for BISE research. | [
"Stefan Feuerriegel",
"Jochen Hartmann",
"Christian Janiesch",
"Patrick Zschech"
] | 2023-09-13 08:21:59 | http://arxiv.org/abs/2309.07930v1 | http://arxiv.org/pdf/2309.07930v1 | 2309.07930v1 |
Scalable neural network models and terascale datasets for particle-flow reconstruction | We study scalable machine learning models for full event reconstruction in
high-energy electron-positron collisions based on a highly granular detector
simulation. Particle-flow (PF) reconstruction can be formulated as a supervised
learning task using tracks and calorimeter clusters or hits. We compare a graph
neural network and kernel-based transformer and demonstrate that both avoid
quadratic memory allocation and computational cost while achieving realistic PF
reconstruction. We show that hyperparameter tuning on a supercomputer
significantly improves the physics performance of the models. We also
demonstrate that the resulting model is highly portable across hardware
processors, supporting Nvidia, AMD, and Intel Habana cards. Finally, we
demonstrate that the model can be trained on highly granular inputs consisting
of tracks and calorimeter hits, resulting in a competitive physics performance
with the baseline. Datasets and software to reproduce the studies are published
following the findable, accessible, interoperable, and reusable (FAIR)
principles. | [
"Joosep Pata",
"Eric Wulff",
"Farouk Mokhtar",
"David Southwick",
"Mengke Zhang",
"Maria Girone",
"Javier Duarte"
] | 2023-09-13 08:16:15 | http://arxiv.org/abs/2309.06782v1 | http://arxiv.org/pdf/2309.06782v1 | 2309.06782v1 |
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss | Although deep learning (DL) has led to several breakthroughs in many
disciplines as diverse as chemistry, computer science, electrical engineering,
mathematics, medicine, neuroscience, and physics, a comprehensive understanding
of why and how DL is empirically successful remains fundamentally elusive. To
attack this fundamental problem and unravel the mysteries behind DL's empirical
successes, significant innovations toward a unified theory of DL have been
made. These innovations encompass nearly fundamental advances in optimization,
generalization, and approximation. Despite these advances, however, no work to
date has offered a way to quantify the testing performance of a DL-based
algorithm employed to solve a pattern classification problem. To overcome this
fundamental challenge in part, this paper exposes the fundamental testing
performance limits of DL-based binary classifiers trained with hinge loss. For
binary classifiers that are based on deep rectified linear unit (ReLU)
feedforward neural networks (FNNs) and ones that are based on deep FNNs with
ReLU and Tanh activation, we derive their respective novel asymptotic testing
performance limits. The derived testing performance limits are validated by
extensive computer experiments. | [
"Tilahun M. Getu",
"Georges Kaddoum"
] | 2023-09-13 07:49:28 | http://arxiv.org/abs/2309.06774v1 | http://arxiv.org/pdf/2309.06774v1 | 2309.06774v1 |
CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics | In recent years, applying deep learning to solve physics problems has
attracted much attention. Data-driven deep learning methods produce operators
that can learn solutions to the whole system of partial differential equations.
However, the existing methods are only evaluated on simple flow equations
(e.g., Burger's equation), and only consider the generalization ability on
different initial conditions. In this paper, we construct CFDBench, a benchmark
with four classic problems in computational fluid dynamics (CFD): lid-driven
cavity flow, laminar boundary layer flow in circular tubes, dam flows through
the steps, and periodic Karman vortex street. Each flow problem includes data
with different boundary conditions, fluid physical properties, and domain
geometry. Compared to existing datasets, the advantages of CFDBench are (1)
comprehensive. It contains common physical parameters such as velocity,
pressure, and cavity fraction. (2) realistic. It is very suitable for deep
learning solutions of fluid mechanics equations. (3) challenging. It has a
certain learning difficulty, prompting to find models with strong learning
ability. (4) standardized. CFDBench facilitates a comprehensive and fair
comparison of different deep learning methods for CFD. We make appropriate
modifications to popular deep neural networks to apply them to CFDBench and
enable the accommodation of more changing inputs. The evaluation on CFDBench
reveals some new shortcomings of existing works and we propose possible
directions for solving such problems. | [
"Yining Luo",
"Yingfa Chen",
"Zhen Zhang"
] | 2023-09-13 06:30:08 | http://arxiv.org/abs/2310.05963v1 | http://arxiv.org/pdf/2310.05963v1 | 2310.05963v1 |
MTD: Multi-Timestep Detector for Delayed Streaming Perception | Autonomous driving systems require real-time environmental perception to
ensure user safety and experience. Streaming perception is a task of reporting
the current state of the world, which is used to evaluate the delay and
accuracy of autonomous driving systems. In real-world applications, factors
such as hardware limitations and high temperatures inevitably cause delays in
autonomous driving systems, resulting in the offset between the model output
and the world state. In order to solve this problem, this paper propose the
Multi- Timestep Detector (MTD), an end-to-end detector which uses dynamic
routing for multi-branch future prediction, giving model the ability to resist
delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the
existing delay sensing method, continuously monitoring the model inference
stack and calculating the delay trend. Moreover, a novel Timestep Branch Module
(TBM) is constructed, which includes static flow and adaptive flow to
adaptively predict specific timesteps according to the delay trend. The
proposed method has been evaluated on the Argoverse-HD dataset, and the
experimental results show that it has achieved state-of-the-art performance
across various delay settings. | [
"Yihui Huang",
"Ningjiang Chen"
] | 2023-09-13 06:23:58 | http://arxiv.org/abs/2309.06742v1 | http://arxiv.org/pdf/2309.06742v1 | 2309.06742v1 |
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme | Causal inference permits us to discover covert relationships of various
variables in time series. However, in most existing works, the variables
mentioned above are the dimensions. The causality between dimensions could be
cursory, which hinders the comprehension of the internal relationship and the
benefit of the causal graph to the neural networks (NNs). In this paper, we
find that causality exists not only outside but also inside the time series
because it reflects a succession of events in the real world. It inspires us to
seek the relationship between internal subsequences. However, the challenges
are the hardship of discovering causality from subsequences and utilizing the
causal natural structures to improve NNs. To address these challenges, we
propose a novel framework called Mining Causal Natural Structure (MCNS), which
is automatic and domain-agnostic and helps to find the causal natural
structures inside time series via the internal causality scheme. We evaluate
the MCNS framework and impregnation NN with MCNS on time series classification
tasks. Experimental results illustrate that our impregnation, by refining
attention, shape selection classification, and pruning datasets, drives NN,
even the data itself preferable accuracy and interpretability. Besides, MCNS
provides an in-depth, solid summary of the time series and datasets. | [
"Yuanhao Liu",
"Dehui Du",
"Zihan Jiang",
"Anyan Huang",
"Yiyang Li"
] | 2023-09-13 06:15:37 | http://arxiv.org/abs/2309.06739v1 | http://arxiv.org/pdf/2309.06739v1 | 2309.06739v1 |
Prompting Segmentation with Sound is Generalizable Audio-Visual Source Localizer | Never having seen an object and heard its sound simultaneously, can the model
still accurately localize its visual position from the input audio? In this
work, we concentrate on the Audio-Visual Localization and Segmentation tasks
but under the demanding zero-shot and few-shot scenarios. To achieve this goal,
different from existing approaches that mostly employ the
encoder-fusion-decoder paradigm to decode localization information from the
fused audio-visual feature, we introduce the encoder-prompt-decoder paradigm,
aiming to better fit the data scarcity and varying data distribution dilemmas
with the help of abundant knowledge from pre-trained models. Specifically, we
first propose to construct Semantic-aware Audio Prompt (SAP) to help the visual
foundation model focus on sounding objects, meanwhile, the semantic gap between
the visual and audio modalities is also encouraged to shrink. Then, we develop
a Correlation Adapter (ColA) to keep minimal training efforts as well as
maintain adequate knowledge of the visual foundation model. By equipping with
these means, extensive experiments demonstrate that this new paradigm
outperforms other fusion-based methods in both the unseen class and
cross-dataset settings. We hope that our work can further promote the
generalization study of Audio-Visual Localization and Segmentation in practical
application scenarios. | [
"Yaoting Wang",
"Weisong Liu",
"Guangyao Li",
"Jian Ding",
"Di Hu",
"Xi Li"
] | 2023-09-13 05:43:35 | http://arxiv.org/abs/2309.07929v2 | http://arxiv.org/pdf/2309.07929v2 | 2309.07929v2 |
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense | We aim to provide a general framework of for computational photography that
recovers the real scene from imperfect images, via the Deep Nonparametric
Convexified Filtering (DNCF). It is consists of a nonparametric deep network to
resemble the physical equations behind the image formation, such as denoising,
super-resolution, inpainting, and flash. DNCF has no parameterization dependent
on training data, therefore has a strong generalization and robustness to
adversarial image manipulation. During inference, we also encourage the network
parameters to be nonnegative and create a bi-convex function on the input and
parameters, and this adapts to second-order optimization algorithms with
insufficient running time, having 10X acceleration over Deep Image Prior. With
these tools, we empirically verify its capability to defend image
classification deep networks against adversary attack algorithms in real-time. | [
"Jianqiao Wangni"
] | 2023-09-13 04:57:12 | http://arxiv.org/abs/2309.06724v2 | http://arxiv.org/pdf/2309.06724v2 | 2309.06724v2 |
Bias Amplification Enhances Minority Group Performance | Neural networks produced by standard training are known to suffer from poor
accuracy on rare subgroups despite achieving high accuracy on average, due to
the correlations between certain spurious features and labels. Previous
approaches based on worst-group loss minimization (e.g. Group-DRO) are
effective in improving worse-group accuracy but require expensive group
annotations for all the training samples. In this paper, we focus on the more
challenging and realistic setting where group annotations are only available on
a small validation set or are not available at all. We propose BAM, a novel
two-stage training algorithm: in the first stage, the model is trained using a
bias amplification scheme via introducing a learnable auxiliary variable for
each training sample; in the second stage, we upweight the samples that the
bias-amplified model misclassifies, and then continue training the same model
on the reweighted dataset. Empirically, BAM achieves competitive performance
compared with existing methods evaluated on spurious correlation benchmarks in
computer vision and natural language processing. Moreover, we find a simple
stopping criterion based on minimum class accuracy difference that can remove
the need for group annotations, with little or no loss in worst-group accuracy.
We perform extensive analyses and ablations to verify the effectiveness and
robustness of our algorithm in varying class and group imbalance ratios. | [
"Gaotang Li",
"Jiarui Liu",
"Wei Hu"
] | 2023-09-13 04:40:08 | http://arxiv.org/abs/2309.06717v1 | http://arxiv.org/pdf/2309.06717v1 | 2309.06717v1 |
Improving the Performance of R17 Type-II Codebook with Deep Learning | The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain
partial reciprocity between uplink and downlink channels to select part of
angular-delay-domain ports for measuring and feeding back the downlink channel
state information (CSI), where the performance of existing deep learning
enhanced CSI feedback methods is limited due to the deficiency of sparse
structures. To address this issue, we propose two new perspectives of adopting
deep learning to improve the R17 Type-II codebook. Firstly, considering the low
signal-to-noise ratio of uplink channels, deep learning is utilized to
accurately select the dominant angular-delay-domain ports, where the focal loss
is harnessed to solve the class imbalance problem. Secondly, we propose to
adopt deep learning to reconstruct the downlink CSI based on the feedback of
the R17 Type-II codebook at the base station, where the information of sparse
structures can be effectively leveraged. Besides, a weighted shortcut module is
designed to facilitate the accurate reconstruction. Simulation results
demonstrate that our proposed methods could improve the sum rate performance
compared with its traditional R17 Type-II codebook and deep learning
benchmarks. | [
"Ke Ma",
"Yiliang Sang",
"Yang Ming",
"Jin Lian",
"Chang Tian",
"Zhaocheng Wang"
] | 2023-09-13 04:34:32 | http://arxiv.org/abs/2310.05962v1 | http://arxiv.org/pdf/2310.05962v1 | 2310.05962v1 |
Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm | While crystal structure prediction (CSP) remains a longstanding challenge, we
introduce ParetoCSP, a novel algorithm for CSP, which combines a
multi-objective genetic algorithm (MOGA) with a neural network inter-atomic
potential (IAP) model to find energetically optimal crystal structures given
chemical compositions. We enhance the NSGA-III algorithm by incorporating the
genotypic age as an independent optimization criterion and employ the M3GNet
universal IAP to guide the GA search. Compared to GN-OA, a state-of-the-art
neural potential based CSP algorithm, ParetoCSP demonstrated significantly
better predictive capabilities, outperforming by a factor of $2.562$ across
$55$ diverse benchmark structures, as evaluated by seven performance metrics.
Trajectory analysis of the traversed structures of all algorithms shows that
ParetoCSP generated more valid structures than other algorithms, which helped
guide the GA to search more effectively for the optimal structures | [
"Sadman Sadeed Omee",
"Lai Wei",
"Jianjun Hu"
] | 2023-09-13 04:17:28 | http://arxiv.org/abs/2309.06710v1 | http://arxiv.org/pdf/2309.06710v1 | 2309.06710v1 |
Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting | Predicting potential risks associated with the fatigue of key structural
components is crucial in engineering design. However, fatigue often involves
entangled complexities of material microstructures and service conditions,
making diagnosis and prognosis of fatigue damage challenging. We report a
statistical learning framework to predict the growth of fatigue cracks and the
life-to-failure of the components under loading conditions with uncertainties.
Digital libraries of fatigue crack patterns and the remaining life are
constructed by high-fidelity physical simulations. Dimensionality reduction and
neural network architectures are then used to learn the history dependence and
nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques
are introduced to handle the statistical noises and rare events. The predicted
fatigue crack patterns are self-updated and self-corrected by the evolving
crack patterns. The end-to-end approach is validated by representative examples
with fatigue cracks in plates, which showcase the digital-twin scenario in
real-time structural health monitoring and fatigue life prediction for
maintenance management decision-making. | [
"Yingjie Zhao",
"Yong Liu",
"Zhiping Xu"
] | 2023-09-13 04:13:11 | http://arxiv.org/abs/2309.06708v1 | http://arxiv.org/pdf/2309.06708v1 | 2309.06708v1 |
VLSlice: Interactive Vision-and-Language Slice Discovery | Recent work in vision-and-language demonstrates that large-scale pretraining
can learn generalizable models that are efficiently transferable to downstream
tasks. While this may improve dataset-scale aggregate metrics, analyzing
performance around hand-crafted subgroups targeting specific bias dimensions
reveals systemic undesirable behaviors. However, this subgroup analysis is
frequently stalled by annotation efforts, which require extensive time and
resources to collect the necessary data. Prior art attempts to automatically
discover subgroups to circumvent these constraints but typically leverages
model behavior on existing task-specific annotations and rapidly degrades on
more complex inputs beyond "tabular" data, none of which study
vision-and-language models. This paper presents VLSlice, an interactive system
enabling user-guided discovery of coherent representation-level subgroups with
consistent visiolinguistic behavior, denoted as vision-and-language slices,
from unlabeled image sets. We show that VLSlice enables users to quickly
generate diverse high-coherency slices in a user study (n=22) and release the
tool publicly. | [
"Eric Slyman",
"Minsuk Kahng",
"Stefan Lee"
] | 2023-09-13 04:02:38 | http://arxiv.org/abs/2309.06703v1 | http://arxiv.org/pdf/2309.06703v1 | 2309.06703v1 |
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization | Federated learning (FL) is a privacy-preserving paradigm for collaboratively
training a global model from decentralized clients. However, the performance of
FL is hindered by non-independent and identically distributed (non-IID) data
and device heterogeneity. In this work, we revisit this key challenge through
the lens of gradient conflicts on the server side. Specifically, we first
investigate the gradient conflict phenomenon among multiple clients and reveal
that stronger heterogeneity leads to more severe gradient conflicts. To tackle
this issue, we propose FedGH, a simple yet effective method that mitigates
local drifts through Gradient Harmonization. This technique projects one
gradient vector onto the orthogonal plane of the other within conflicting
client pairs. Extensive experiments demonstrate that FedGH consistently
enhances multiple state-of-the-art FL baselines across diverse benchmarks and
non-IID scenarios. Notably, FedGH yields more significant improvements in
scenarios with stronger heterogeneity. As a plug-and-play module, FedGH can be
seamlessly integrated into any FL framework without requiring hyperparameter
tuning. | [
"Xinyu Zhang",
"Weiyu Sun",
"Ying Chen"
] | 2023-09-13 03:27:21 | http://arxiv.org/abs/2309.06692v1 | http://arxiv.org/pdf/2309.06692v1 | 2309.06692v1 |
Attention Loss Adjusted Prioritized Experience Replay | Prioritized Experience Replay (PER) is a technical means of deep
reinforcement learning by selecting experience samples with more knowledge
quantity to improve the training rate of neural network. However, the
non-uniform sampling used in PER inevitably shifts the state-action space
distribution and brings the estimation error of Q-value function. In this
paper, an Attention Loss Adjusted Prioritized (ALAP) Experience Replay
algorithm is proposed, which integrates the improved Self-Attention network
with Double-Sampling mechanism to fit the hyperparameter that can regulate the
importance sampling weights to eliminate the estimation error caused by PER. In
order to verify the effectiveness and generality of the algorithm, the ALAP is
tested with value-function based, policy-gradient based and multi-agent
reinforcement learning algorithms in OPENAI gym, and comparison studies verify
the advantage and efficiency of the proposed training framework. | [
"Zhuoying Chen",
"Huiping Li",
"Rizhong Wang"
] | 2023-09-13 02:49:32 | http://arxiv.org/abs/2309.06684v2 | http://arxiv.org/pdf/2309.06684v2 | 2309.06684v2 |
Federated PAC-Bayesian Learning on Non-IID data | Existing research has either adapted the Probably Approximately Correct (PAC)
Bayesian framework for federated learning (FL) or used information-theoretic
PAC-Bayesian bounds while introducing their theorems, but few considering the
non-IID challenges in FL. Our work presents the first non-vacuous federated
PAC-Bayesian bound tailored for non-IID local data. This bound assumes unique
prior knowledge for each client and variable aggregation weights. We also
introduce an objective function and an innovative Gibbs-based algorithm for the
optimization of the derived bound. The results are validated on real-world
datasets. | [
"Zihao Zhao",
"Yang Liu",
"Wenbo Ding",
"Xiao-Ping Zhang"
] | 2023-09-13 02:44:01 | http://arxiv.org/abs/2309.06683v1 | http://arxiv.org/pdf/2309.06683v1 | 2309.06683v1 |
Generalizable improvement of the Spalart-Allmaras model through assimilation of experimental data | This study focuses on the use of model and data fusion for improving the
Spalart-Allmaras (SA) closure model for Reynolds-averaged Navier-Stokes
solutions of separated flows. In particular, our goal is to develop of models
that not-only assimilate sparse experimental data to improve performance in
computational models, but also generalize to unseen cases by recovering
classical SA behavior. We achieve our goals using data assimilation, namely the
Ensemble Kalman Filtering approach (EnKF), to calibrate the coefficients of the
SA model for separated flows. A holistic calibration strategy is implemented
via a parameterization of the production, diffusion, and destruction terms.
This calibration relies on the assimilation of experimental data collected
velocity profiles, skin friction, and pressure coefficients for separated
flows. Despite using of observational data from a single flow condition around
a backward-facing step (BFS), the recalibrated SA model demonstrates
generalization to other separated flows, including cases such as the 2D-bump
and modified BFS. Significant improvement is observed in the quantities of
interest, i.e., skin friction coefficient ($C_f$) and pressure coefficient
($C_p$) for each flow tested. Finally, it is also demonstrated that the newly
proposed model recovers SA proficiency for external, unseparated flows, such as
flow around a NACA-0012 airfoil without any danger of extrapolation, and that
the individually calibrated terms in the SA model are targeted towards specific
flow-physics wherein the calibrated production term improves the re-circulation
zone while destruction improves the recovery zone. | [
"Deepinder Jot Singh Aulakh",
"Romit Maulik"
] | 2023-09-13 02:34:21 | http://arxiv.org/abs/2309.06679v1 | http://arxiv.org/pdf/2309.06679v1 | 2309.06679v1 |
Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition | Chlorophyll concentration can well reflect the nutritional status and algal
blooms of water bodies, and is an important indicator for evaluating water
quality. The prediction of chlorophyll concentration change trend is of great
significance to environmental protection and aquaculture. However, there is a
complex and indistinguishable nonlinear relationship between many factors
affecting chlorophyll concentration. In order to effectively mine the nonlinear
features contained in the data. This paper proposes a time-series decomposition
adaptive graph-time convolutional network ( AGTCNSD ) prediction model.
Firstly, the original sequence is decomposed into trend component and periodic
component by moving average method. Secondly, based on the graph convolutional
neural network, the water quality parameter data is modeled, and a parameter
embedding matrix is defined. The idea of matrix decomposition is used to assign
weight parameters to each node. The adaptive graph convolution learns the
relationship between different water quality parameters, updates the state
information of each parameter, and improves the learning ability of the update
relationship between nodes. Finally, time dependence is captured by time
convolution to achieve multi-step prediction of chlorophyll concentration. The
validity of the model is verified by the water quality data of the coastal city
Beihai. The results show that the prediction effect of this method is better
than other methods. It can be used as a scientific resource for environmental
management decision-making. | [
"Ying Chen",
"Xiao Li",
"Hongbo Zhang",
"Wenyang Song",
"Chongxuan Xv"
] | 2023-09-13 02:15:02 | http://arxiv.org/abs/2309.07187v1 | http://arxiv.org/pdf/2309.07187v1 | 2309.07187v1 |
Analysis and Detection against Network Attacks in the Overlapping Phenomenon of Behavior Attribute | The proliferation of network attacks poses a significant threat. Researchers
propose datasets for network attacks to support research in related fields.
Then, many attack detection methods based on these datasets are proposed. These
detection methods, whether two-classification or multi-classification, belong
to single-label learning, i.e., only one label is given to each sample.
However, we discover that there is a noteworthy phenomenon of behavior
attribute overlap between attacks, The presentation of this phenomenon in a
dataset is that there are multiple samples with the same features but different
labels. In this paper, we verify the phenomenon in well-known
datasets(UNSW-NB15, CCCS-CIC-AndMal-2020) and re-label these data. In addition,
detecting network attacks in a multi-label manner can obtain more information,
providing support for tracing the attack source and building IDS. Therefore, we
propose a multi-label detection model based on deep learning, MLD-Model, in
which Wasserstein-Generative-Adversarial- Network-with-Gradient-Penalty
(WGAN-GP) with improved loss performs data enhancement to alleviate the class
imbalance problem, and Auto-Encoder (AE) performs classifier parameter
pre-training. Experimental results demonstrate that MLD-Model can achieve
excellent classification performance. It can achieve F1=80.06% in UNSW-NB15 and
F1=83.63% in CCCS-CIC-AndMal-2020. Especially, MLD-Model is 5.99%-7.97% higher
in F1 compared with the related single-label methods. | [
"Jiang Xie",
"Shuhao Li",
"Yongzheng Zhanga",
"Peishuai Sun",
"Hongbo Xu"
] | 2023-09-13 01:59:26 | http://arxiv.org/abs/2310.10660v1 | http://arxiv.org/pdf/2310.10660v1 | 2310.10660v1 |
Sound field decomposition based on two-stage neural networks | A method for sound field decomposition based on neural networks is proposed.
The method comprises two stages: a sound field separation stage and a
single-source localization stage. In the first stage, the sound pressure at
microphones synthesized by multiple sources is separated into one excited by
each sound source. In the second stage, the source location is obtained as a
regression from the sound pressure at microphones consisting of a single sound
source. The estimated location is not affected by discretization because the
second stage is designed as a regression rather than a classification. Datasets
are generated by simulation using Green's function, and the neural network is
trained for each frequency. Numerical experiments reveal that, compared with
conventional methods, the proposed method can achieve higher
source-localization accuracy and higher sound-field-reconstruction accuracy. | [
"Ryo Matsuda",
"Makoto Otani"
] | 2023-09-13 01:32:46 | http://arxiv.org/abs/2309.06661v1 | http://arxiv.org/pdf/2309.06661v1 | 2309.06661v1 |
Large Language Models Can Infer Psychological Dispositions of Social Media Users | As Large Language Models (LLMs) demonstrate increasingly human-like abilities
in various natural language processing (NLP) tasks that are bound to become
integral to personalized technologies, understanding their capabilities and
inherent biases is crucial. Our study investigates the potential of LLMs like
ChatGPT to infer psychological dispositions of individuals from their digital
footprints. Specifically, we assess the ability of GPT-3.5 and GPT-4 to derive
the Big Five personality traits from users' Facebook status updates in a
zero-shot learning scenario. Our results show an average correlation of r = .29
(range = [.22, .33]) between LLM-inferred and self-reported trait scores.
Furthermore, our findings suggest biases in personality inferences with regard
to gender and age: inferred scores demonstrated smaller errors for women and
younger individuals on several traits, suggesting a potential systematic bias
stemming from the underlying training data or differences in online
self-expression. | [
"Heinrich Peters",
"Sandra Matz"
] | 2023-09-13 01:27:48 | http://arxiv.org/abs/2309.08631v1 | http://arxiv.org/pdf/2309.08631v1 | 2309.08631v1 |
Generalizable Neural Fields as Partially Observed Neural Processes | Neural fields, which represent signals as a function parameterized by a
neural network, are a promising alternative to traditional discrete vector or
grid-based representations. Compared to discrete representations, neural
representations both scale well with increasing resolution, are continuous, and
can be many-times differentiable. However, given a dataset of signals that we
would like to represent, having to optimize a separate neural field for each
signal is inefficient, and cannot capitalize on shared information or
structures among signals. Existing generalization methods view this as a
meta-learning problem and employ gradient-based meta-learning to learn an
initialization which is then fine-tuned with test-time optimization, or learn
hypernetworks to produce the weights of a neural field. We instead propose a
new paradigm that views the large-scale training of neural representations as a
part of a partially-observed neural process framework, and leverage neural
process algorithms to solve this task. We demonstrate that this approach
outperforms both state-of-the-art gradient-based meta-learning approaches and
hypernetwork approaches. | [
"Jeffrey Gu",
"Kuan-Chieh Wang",
"Serena Yeung"
] | 2023-09-13 01:22:16 | http://arxiv.org/abs/2309.06660v1 | http://arxiv.org/pdf/2309.06660v1 | 2309.06660v1 |
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets | Imitation learning enables the synthesis of controllers for complex
objectives and highly uncertain plant models. However, methods to provide
stability guarantees to imitation learned controllers often rely on large
amounts of data and/or known plant models. In this paper, we explore an
input-output (IO) stability approach to dissipative imitation learning, which
achieves stability with sparse data sets and with little known about the plant
model. A closed-loop stable dynamic output feedback controller is learned using
expert data, a coarse IO plant model, and a new constraint to enforce
dissipativity on the learned controller. While the learning objective is
nonconvex, iterative convex overbounding (ICO) and projected gradient descent
(PGD) are explored as methods to successfully learn the controller. This new
imitation learning method is applied to two unknown plants and compared to
traditionally learned dynamic output feedback controller and neural network
controller. With little knowledge of the plant model and a small data set, the
dissipativity constrained learned controller achieves closed loop stability and
successfully mimics the behavior of the expert controller, while other methods
often fail to maintain stability and achieve good performance. | [
"Amy K. Strong",
"Ethan J. LoCicero",
"Leila J. Bridgeman"
] | 2023-09-13 01:13:33 | http://arxiv.org/abs/2309.06658v1 | http://arxiv.org/pdf/2309.06658v1 | 2309.06658v1 |
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL | In this study, we aim to enhance the arithmetic reasoning ability of Large
Language Models (LLMs) through zero-shot prompt optimization. We identify a
previously overlooked objective of query dependency in such optimization and
elucidate two ensuing challenges that impede the successful and economical
design of prompt optimization techniques. One primary issue is the absence of
an effective method to evaluate prompts during inference when the golden answer
is unavailable. Concurrently, learning via interactions with the LLMs to
navigate the expansive natural language prompting space proves to be
resource-intensive. To address this, we introduce Prompt-OIRL, which harnesses
offline inverse reinforcement learning to draw insights from offline prompting
demonstration data. Such data exists as by-products when diverse prompts are
benchmarked on open-accessible datasets. With Prompt-OIRL, the query-dependent
prompt optimization objective is achieved by first learning an offline reward
model. This model can evaluate any query-prompt pairs without accessing LLMs.
Subsequently, a best-of-N strategy is deployed to recommend the optimal prompt.
Our experimental evaluations across various LLM scales and arithmetic reasoning
datasets underscore both the efficacy and economic viability of the proposed
approach. | [
"Hao Sun",
"Alihan Hüyük",
"Mihaela van der Schaar"
] | 2023-09-13 01:12:52 | http://arxiv.org/abs/2309.06553v3 | http://arxiv.org/pdf/2309.06553v3 | 2309.06553v3 |
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models | In order for robots to safely navigate in unseen scenarios using
learning-based methods, it is important to accurately detect
out-of-training-distribution (OoD) situations online. Recently, Gaussian
process state-space models (GPSSMs) have proven useful to discriminate
unexpected observations by comparing them against probabilistic predictions.
However, the capability for the model to correctly distinguish between in- and
out-of-training distribution observations hinges on the accuracy of these
predictions, primarily affected by the class of functions the GPSSM kernel can
represent. In this paper, we propose (i) a novel approach to embed existing
domain knowledge in the kernel and (ii) an OoD online runtime monitor, based on
receding-horizon predictions. Domain knowledge is provided in the form of a
dataset, collected either in simulation or by using a nominal model. Numerical
results show that the informed kernel yields better regression quality with
smaller datasets, as compared to standard kernel choices. We demonstrate the
effectiveness of the OoD monitor on a real quadruped navigating an indoor
setting, which reliably classifies previously unseen terrains. | [
"Alonso Marco",
"Elias Morley",
"Claire J. Tomlin"
] | 2023-09-13 01:02:42 | http://arxiv.org/abs/2309.06655v2 | http://arxiv.org/pdf/2309.06655v2 | 2309.06655v2 |
ConR: Contrastive Regularizer for Deep Imbalanced Regression | Imbalanced distributions are ubiquitous in real-world data. They create
constraints on Deep Neural Networks to represent the minority labels and avoid
bias towards majority labels. The extensive body of imbalanced approaches
address categorical label spaces but fail to effectively extend to regression
problems where the label space is continuous. Local and global correlations
among continuous labels provide valuable insights towards effectively modelling
relationships in feature space. In this work, we propose ConR, a contrastive
regularizer that models global and local label similarities in feature space
and prevents the features of minority samples from being collapsed into their
majority neighbours. ConR discerns the disagreements between the label space
and feature space and imposes a penalty on these disagreements. ConR addresses
the continuous nature of label space with two main strategies in a contrastive
manner: incorrect proximities are penalized proportionate to the label
similarities and the correct ones are encouraged to model local similarities.
ConR consolidates essential considerations into a generic, easy-to-integrate,
and efficient method that effectively addresses deep imbalanced regression.
Moreover, ConR is orthogonal to existing approaches and smoothly extends to
uni- and multi-dimensional label spaces. Our comprehensive experiments show
that ConR significantly boosts the performance of all the state-of-the-art
methods on four large-scale deep imbalanced regression benchmarks. Our code is
publicly available in https://github.com/BorealisAI/ConR. | [
"Mahsa Keramati",
"Lili Meng",
"R. David Evans"
] | 2023-09-13 00:30:32 | http://arxiv.org/abs/2309.06651v2 | http://arxiv.org/pdf/2309.06651v2 | 2309.06651v2 |
Bregman Graph Neural Network | Numerous recent research on graph neural networks (GNNs) has focused on
formulating GNN architectures as an optimization problem with the smoothness
assumption. However, in node classification tasks, the smoothing effect induced
by GNNs tends to assimilate representations and over-homogenize labels of
connected nodes, leading to adverse effects such as over-smoothing and
misclassification. In this paper, we propose a novel bilevel optimization
framework for GNNs inspired by the notion of Bregman distance. We demonstrate
that the GNN layer proposed accordingly can effectively mitigate the
over-smoothing issue by introducing a mechanism reminiscent of the "skip
connection". We validate our theoretical results through comprehensive
empirical studies in which Bregman-enhanced GNNs outperform their original
counterparts in both homophilic and heterophilic graphs. Furthermore, our
experiments also show that Bregman GNNs can produce more robust learning
accuracy even when the number of layers is high, suggesting the effectiveness
of the proposed method in alleviating the over-smoothing issue. | [
"Jiayu Zhai",
"Lequan Lin",
"Dai Shi",
"Junbin Gao"
] | 2023-09-12 23:54:24 | http://arxiv.org/abs/2309.06645v1 | http://arxiv.org/pdf/2309.06645v1 | 2309.06645v1 |
Audio-Based Classification of Respiratory Diseases using Advanced Signal Processing and Machine Learning for Assistive Diagnosis Support | In global healthcare, respiratory diseases are a leading cause of mortality,
underscoring the need for rapid and accurate diagnostics. To advance rapid
screening techniques via auscultation, our research focuses on employing one of
the largest publicly available medical database of respiratory sounds to train
multiple machine learning models able to classify different health conditions.
Our method combines Empirical Mode Decomposition (EMD) and spectral analysis to
extract physiologically relevant biosignals from acoustic data, closely tied to
cardiovascular and respiratory patterns, making our approach apart in its
departure from conventional audio feature extraction practices. We use Power
Spectral Density analysis and filtering techniques to select Intrinsic Mode
Functions (IMFs) strongly correlated with underlying physiological phenomena.
These biosignals undergo a comprehensive feature extraction process for
predictive modeling. Initially, we deploy a binary classification model that
demonstrates a balanced accuracy of 87% in distinguishing between healthy and
diseased individuals. Subsequently, we employ a six-class classification model
that achieves a balanced accuracy of 72% in diagnosing specific respiratory
conditions like pneumonia and chronic obstructive pulmonary disease (COPD). For
the first time, we also introduce regression models that estimate age and body
mass index (BMI) based solely on acoustic data, as well as a model for gender
classification. Our findings underscore the potential of this approach to
significantly enhance assistive and remote diagnostic capabilities. | [
"Constantino Álvarez Casado",
"Manuel Lage Cañellas",
"Matteo Pedone",
"Xiaoting Wu",
"Miguel Bordallo López"
] | 2023-09-12 23:54:00 | http://arxiv.org/abs/2309.07183v1 | http://arxiv.org/pdf/2309.07183v1 | 2309.07183v1 |
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models | Inverse problems arise in a multitude of applications, where the goal is to
recover a clean signal from noisy and possibly (non)linear observations. The
difficulty of a reconstruction problem depends on multiple factors, such as the
structure of the ground truth signal, the severity of the degradation, the
implicit bias of the reconstruction model and the complex interactions between
the above factors. This results in natural sample-by-sample variation in the
difficulty of a reconstruction task, which is often overlooked by contemporary
techniques. Recently, diffusion-based inverse problem solvers have established
new state-of-the-art in various reconstruction tasks. However, they have the
drawback of being computationally prohibitive. Our key observation in this
paper is that most existing solvers lack the ability to adapt their compute
power to the difficulty of the reconstruction task, resulting in long inference
times, subpar performance and wasteful resource allocation. We propose a novel
method that we call severity encoding, to estimate the degradation severity of
noisy, degraded signals in the latent space of an autoencoder. We show that the
estimated severity has strong correlation with the true corruption level and
can give useful hints at the difficulty of reconstruction problems on a
sample-by-sample basis. Furthermore, we propose a reconstruction method based
on latent diffusion models that leverages the predicted degradation severities
to fine-tune the reverse diffusion sampling trajectory and thus achieve
sample-adaptive inference times. We utilize latent diffusion posterior sampling
to maintain data consistency with observations. We perform experiments on both
linear and nonlinear inverse problems and demonstrate that our technique
achieves performance comparable to state-of-the-art diffusion-based techniques,
with significant improvements in computational efficiency. | [
"Zalan Fabian",
"Berk Tinaz",
"Mahdi Soltanolkotabi"
] | 2023-09-12 23:41:29 | http://arxiv.org/abs/2309.06642v1 | http://arxiv.org/pdf/2309.06642v1 | 2309.06642v1 |
Quantum Data Center: Perspectives | A quantum version of data centers might be significant in the quantum era. In
this paper, we introduce Quantum Data Center (QDC), a quantum version of
existing classical data centers, with a specific emphasis on combining Quantum
Random Access Memory (QRAM) and quantum networks. We argue that QDC will
provide significant benefits to customers in terms of efficiency, security, and
precision, and will be helpful for quantum computing, communication, and
sensing. We investigate potential scientific and business opportunities along
this novel research direction through hardware realization and possible
specific applications. We show the possible impacts of QDCs in business and
science, especially the machine learning and big data industries. | [
"Junyu Liu",
"Liang Jiang"
] | 2023-09-12 23:24:38 | http://arxiv.org/abs/2309.06641v1 | http://arxiv.org/pdf/2309.06641v1 | 2309.06641v1 |
PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions | Jet tagging is a classification problem in high-energy physics experiments
that aims to identify the collimated sprays of subatomic particles, jets, from
particle collisions and tag them to their emitter particle. Advances in jet
tagging present opportunities for searches of new physics beyond the Standard
Model. Current approaches use deep learning to uncover hidden patterns in
complex collision data. However, the representation of jets as inputs to a deep
learning model have been varied, and often, informative features are withheld
from models. In this study, we propose a graph-based representation of a jet
that encodes the most information possible. To learn best from this
representation, we design Particle Chebyshev Network (PCN), a graph neural
network (GNN) using Chebyshev graph convolutions (ChebConv). ChebConv has been
demonstrated as an effective alternative to classical graph convolutions in
GNNs and has yet to be explored in jet tagging. PCN achieves a substantial
improvement in accuracy over existing taggers and opens the door to future
studies into graph-based representations of jets and ChebConv layers in
high-energy physics experiments. Code is available at
https://github.com/YVSemlani/PCN-Jet-Tagging. | [
"Yash Semlani",
"Mihir Relan",
"Krithik Ramesh"
] | 2023-09-12 23:20:19 | http://arxiv.org/abs/2309.08630v1 | http://arxiv.org/pdf/2309.08630v1 | 2309.08630v1 |
Sleep Stage Classification Using a Pre-trained Deep Learning Model | One of the common human diseases is sleep disorders. The classification of
sleep stages plays a fundamental role in diagnosing sleep disorders, monitoring
treatment effectiveness, and understanding the relationship between sleep
stages and various health conditions. A precise and efficient classification of
these stages can significantly enhance our understanding of sleep-related
phenomena and ultimately lead to improved health outcomes and disease
treatment.
Models others propose are often time-consuming and lack sufficient accuracy,
especially in stage N1. The main objective of this research is to present a
machine-learning model called "EEGMobile". This model utilizes pre-trained
models and learns from electroencephalogram (EEG) spectrograms of brain
signals. The model achieved an accuracy of 86.97% on a publicly available
dataset named "Sleep-EDF20", outperforming other models proposed by different
researchers. Moreover, it recorded an accuracy of 56.4% in stage N1, which is
better than other models. These findings demonstrate that this model has the
potential to achieve better results for the treatment of this disease. | [
"Hassan Ardeshir",
"Mohammad Araghi"
] | 2023-09-12 23:02:19 | http://arxiv.org/abs/2309.07182v2 | http://arxiv.org/pdf/2309.07182v2 | 2309.07182v2 |
$G$-Mapper: Learning a Cover in the Mapper Construction | The Mapper algorithm is a visualization technique in topological data
analysis (TDA) that outputs a graph reflecting the structure of a given
dataset. The Mapper algorithm requires tuning several parameters in order to
generate a "nice" Mapper graph. The paper focuses on selecting the cover
parameter. We present an algorithm that optimizes the cover of a Mapper graph
by splitting a cover repeatedly according to a statistical test for normality.
Our algorithm is based on $G$-means clustering which searches for the optimal
number of clusters in $k$-means by conducting iteratively the Anderson-Darling
test. Our splitting procedure employs a Gaussian mixture model in order to
choose carefully the cover based on the distribution of a given data.
Experiments for synthetic and real-world datasets demonstrate that our
algorithm generates covers so that the Mapper graphs retain the essence of the
datasets. | [
"Enrique Alvarado",
"Robin Belton",
"Emily Fischer",
"Kang-Ju Lee",
"Sourabh Palande",
"Sarah Percival",
"Emilie Purvine"
] | 2023-09-12 22:51:16 | http://arxiv.org/abs/2309.06634v1 | http://arxiv.org/pdf/2309.06634v1 | 2309.06634v1 |
Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning | Emulator embedded neural networks, which are a type of physics informed
neural network, leverage multi-fidelity data sources for efficient design
exploration of aerospace engineering systems. Multiple realizations of the
neural network models are trained with different random initializations. The
ensemble of model realizations is used to assess epistemic modeling uncertainty
caused due to lack of training samples. This uncertainty estimation is crucial
information for successful goal-oriented adaptive learning in an aerospace
system design exploration. However, the costs of training the ensemble models
often become prohibitive and pose a computational challenge, especially when
the models are not trained in parallel during adaptive learning. In this work,
a new type of emulator embedded neural network is presented using the rapid
neural network paradigm. Unlike the conventional neural network training that
optimizes the weights and biases of all the network layers by using
gradient-based backpropagation, rapid neural network training adjusts only the
last layer connection weights by applying a linear regression technique. It is
found that the proposed emulator embedded neural network trains
near-instantaneously, typically without loss of prediction accuracy. The
proposed method is demonstrated on multiple analytical examples, as well as an
aerospace flight parameter study of a generic hypersonic vehicle. | [
"Atticus Beachy",
"Harok Bae",
"Jose Camberos",
"Ramana Grandhi"
] | 2023-09-12 22:34:34 | http://arxiv.org/abs/2309.06628v1 | http://arxiv.org/pdf/2309.06628v1 | 2309.06628v1 |
A Sequentially Fair Mechanism for Multiple Sensitive Attributes | In the standard use case of Algorithmic Fairness, the goal is to eliminate
the relationship between a sensitive variable and a corresponding score.
Throughout recent years, the scientific community has developed a host of
definitions and tools to solve this task, which work well in many practical
applications. However, the applicability and effectivity of these tools and
definitions becomes less straightfoward in the case of multiple sensitive
attributes. To tackle this issue, we propose a sequential framework, which
allows to progressively achieve fairness across a set of sensitive features. We
accomplish this by leveraging multi-marginal Wasserstein barycenters, which
extends the standard notion of Strong Demographic Parity to the case with
multiple sensitive characteristics. This method also provides a closed-form
solution for the optimal, sequentially fair predictor, permitting a clear
interpretation of inter-sensitive feature correlations. Our approach seamlessly
extends to approximate fairness, enveloping a framework accommodating the
trade-off between risk and unfairness. This extension permits a targeted
prioritization of fairness improvements for a specific attribute within a set
of sensitive attributes, allowing for a case specific adaptation. A data-driven
estimation procedure for the derived solution is developed, and comprehensive
numerical experiments are conducted on both synthetic and real datasets. Our
empirical findings decisively underscore the practical efficacy of our
post-processing approach in fostering fair decision-making. | [
"François Hu",
"Philipp Ratz",
"Arthur Charpentier"
] | 2023-09-12 22:31:57 | http://arxiv.org/abs/2309.06627v1 | http://arxiv.org/pdf/2309.06627v1 | 2309.06627v1 |
Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity | The demand for efficient processing of deep neural networks (DNNs) on
embedded devices is a significant challenge limiting their deployment.
Exploiting sparsity in the network's feature maps is one of the ways to reduce
its inference latency. It is known that unstructured sparsity results in lower
accuracy degradation with respect to structured sparsity but the former needs
extensive inference engine changes to get latency benefits. To tackle this
challenge, we propose a solution to induce semi-structured activation sparsity
exploitable through minor runtime modifications. To attain high speedup levels
at inference time, we design a sparse training procedure with awareness of the
final position of the activations while computing the General Matrix
Multiplication (GEMM). We extensively evaluate the proposed solution across
various models for image classification and object detection tasks. Remarkably,
our approach yields a speed improvement of $1.25 \times$ with a minimal
accuracy drop of $1.1\%$ for the ResNet18 model on the ImageNet dataset.
Furthermore, when combined with a state-of-the-art structured pruning method,
the resulting models provide a good latency-accuracy trade-off, outperforming
models that solely employ structured pruning techniques. | [
"Matteo Grimaldi",
"Darshan C. Ganji",
"Ivan Lazarevich",
"Sudhakar Sah"
] | 2023-09-12 22:28:53 | http://arxiv.org/abs/2309.06626v2 | http://arxiv.org/pdf/2309.06626v2 | 2309.06626v2 |
On the Contraction Coefficient of the Schrödinger Bridge for Stochastic Linear Systems | Schr\"{o}dinger bridge is a stochastic optimal control problem to steer a
given initial state density to another, subject to controlled diffusion and
deadline constraints. A popular method to numerically solve the Schr\"{o}dinger
bridge problems, in both classical and in the linear system settings, is via
contractive fixed point recursions. These recursions can be seen as dynamic
versions of the well-known Sinkhorn iterations, and under mild assumptions,
they solve the so-called Schr\"{o}dinger systems with guaranteed linear
convergence. In this work, we study a priori estimates for the contraction
coefficients associated with the convergence of respective Schr\"{o}dinger
systems. We provide new geometric and control-theoretic interpretations for the
same. Building on these newfound interpretations, we point out the possibility
of improved computation for the worst-case contraction coefficients of linear
SBPs by preconditioning the endpoint support sets. | [
"Alexis M. H. Teter",
"Yongxin Chen",
"Abhishek Halder"
] | 2023-09-12 22:24:05 | http://arxiv.org/abs/2309.06622v1 | http://arxiv.org/pdf/2309.06622v1 | 2309.06622v1 |
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models | Recent advancements in language models (LMs) have gained substantial
attentions on their capability to generate human-like responses. Though
exhibiting a promising future for various applications such as conversation AI,
these LMs face deployment challenges on various devices due to their extreme
computational cost and unpredictable inference latency. Such varied inference
latency, identified as a consequence of uncertainty intrinsic to the nature of
language, can lead to computational inefficiency and degrade the overall
performance of LMs, especially under high-traffic workloads. Unfortunately, the
bandwidth of these uncertainty sources is extensive, complicating the
prediction of latency and the effects emanating from such uncertainties. To
understand and mitigate the impact of uncertainty on real-time
response-demanding systems, we take the first step to comprehend, quantify and
optimize these uncertainty-induced latency performance variations in LMs.
Specifically, we present RT-LM, an uncertainty-aware resource management
ecosystem for real-time inference of LMs. RT-LM innovatively quantifies how
specific input uncertainties, adversely affect latency, often leading to an
increased output length. Exploiting these insights, we devise a lightweight yet
effective method to dynamically correlate input text uncertainties with output
length at runtime. Utilizing this quantification as a latency heuristic, we
integrate the uncertainty information into a system-level scheduler which
explores several uncertainty-induced optimization opportunities, including
uncertainty-aware prioritization, dynamic consolidation, and strategic CPU
offloading. Quantitative experiments across five state-of-the-art LMs on two
hardware platforms demonstrates that RT-LM can significantly reduce the average
response time and improve throughput while incurring a rather small runtime
overhead. | [
"Yufei Li",
"Zexin Li",
"Wei Yang",
"Cong Liu"
] | 2023-09-12 22:22:10 | http://arxiv.org/abs/2309.06619v1 | http://arxiv.org/pdf/2309.06619v1 | 2309.06619v1 |
The Grand Illusion: The Myth of Software Portability and Implications for ML Progress | Pushing the boundaries of machine learning often requires exploring different
hardware and software combinations. However, the freedom to experiment across
different tooling stacks can be at odds with the drive for efficiency, which
has produced increasingly specialized AI hardware and incentivized
consolidation around a narrow set of ML frameworks. Exploratory research can be
restricted if software and hardware are co-evolving, making it even harder to
stray away from mainstream ideas that work well with popular tooling stacks.
While this friction increasingly impacts the rate of innovation in machine
learning, to our knowledge the lack of portability in tooling has not been
quantified. In this work, we ask: How portable are popular ML software
frameworks? We conduct a large-scale study of the portability of mainstream ML
frameworks across different hardware types. Our findings paint an uncomfortable
picture -- frameworks can lose more than 40% of their key functions when ported
to other hardware. Worse, even when functions are portable, the slowdown in
their performance can be extreme and render performance untenable.
Collectively, our results reveal how costly straying from a narrow set of
hardware-software combinations can be - and suggest that specialization of
hardware impedes innovation in machine learning research. | [
"Fraser Mince",
"Dzung Dinh",
"Jonas Kgomo",
"Neil Thompson",
"Sara Hooker"
] | 2023-09-12 22:11:55 | http://arxiv.org/abs/2309.07181v1 | http://arxiv.org/pdf/2309.07181v1 | 2309.07181v1 |
SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery | Machine learning, and representation learning in particular, has the
potential to facilitate drug discovery by screening a large chemical space in
silico. A successful approach for representing molecules is to treat them as a
graph and utilize graph neural networks. One of the key limitations of such
methods is the necessity to represent compounds with different numbers of
atoms, which requires aggregating the atom's information. Common aggregation
operators, such as averaging, result in loss of information at the atom level.
In this work, we propose a novel aggregating approach where each atom is
weighted non-linearly using the Boltzmann distribution with a hyperparameter
analogous to temperature. We show that using this weighted aggregation improves
the ability of the gold standard message-passing neural network to predict
antibiotic activity. Moreover, by changing the temperature hyperparameter, our
approach can reveal the atoms that are important for activity prediction in a
smooth and consistent way, thus providing a novel, regulated attention
mechanism for graph neural networks. We further validate our method by showing
that it recapitulates the functional group in beta-Lactam antibiotics. The
ability of our approach to rank the atoms' importance for a desired function
can be used within any graph neural network to provide interpretability of the
results and predictions at the node level. | [
"Ronen Taub",
"Yonatan Savir"
] | 2023-09-12 22:04:24 | http://arxiv.org/abs/2310.03028v1 | http://arxiv.org/pdf/2310.03028v1 | 2310.03028v1 |
Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials | In this study, Cu-Cr composites were studied by nanoindentation. Arrays of
indents were placed over large areas of the samples resulting in datasets
consisting of several hundred measurements of Young's modulus and hardness at
varying indentation depths. The unsupervised learning technique, Gaussian
mixture model, was employed to analyze the data, which helped to determine the
number of "mechanical phases" and the respective mechanical properties.
Additionally, a cross-validation approach was introduced to infer whether the
data quantity was adequate and to suggest the amount of data required for
reliable predictions -- one of the often encountered but difficult to resolve
issues in machine learning of materials science problems. | [
"Chen Zhang",
"Clémence Bos",
"Stefan Sandfeld",
"Ruth Schwaiger"
] | 2023-09-12 21:45:33 | http://arxiv.org/abs/2309.06613v1 | http://arxiv.org/pdf/2309.06613v1 | 2309.06613v1 |
CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis | Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful
analytical tool for studying molecular structure and dynamics in chemistry and
biology. However, the processing of raw data acquired from NMR spectrometers
and subsequent quantitative analysis involves various specialized tools, which
necessitates comprehensive knowledge in programming and NMR. Particularly, the
emerging deep learning tools is hard to be widely used in NMR due to the
sophisticated setup of computation. Thus, NMR processing is not an easy task
for chemist and biologists. In this work, we present CloudBrain-NMR, an
intelligent online cloud computing platform designed for NMR data reading,
processing, reconstruction, and quantitative analysis. The platform is
conveniently accessed through a web browser, eliminating the need for any
program installation on the user side. CloudBrain-NMR uses parallel computing
with graphics processing units and central processing units, resulting in
significantly shortened computation time. Furthermore, it incorporates
state-of-the-art deep learning-based algorithms offering comprehensive
functionalities that allow users to complete the entire processing procedure
without relying on additional software. This platform has empowered NMR
applications with advanced artificial intelligence processing. CloudBrain-NMR
is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.html | [
"Di Guo",
"Sijin Li",
"Jun Liu",
"Zhangren Tu",
"Tianyu Qiu",
"Jingjing Xu",
"Liubin Feng",
"Donghai Lin",
"Qing Hong",
"Meijin Lin",
"Yanqin Lin",
"Xiaobo Qu"
] | 2023-09-12 21:40:51 | http://arxiv.org/abs/2309.07178v1 | http://arxiv.org/pdf/2309.07178v1 | 2309.07178v1 |
Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices | The recent surge of interest surrounding Multimodal Neural Networks (MM-NN)
is attributed to their ability to effectively process and integrate multiscale
information from diverse data sources. MM-NNs extract and fuse features from
multiple modalities using adequate unimodal backbones and specific fusion
networks. Although this helps strengthen the multimodal information
representation, designing such networks is labor-intensive. It requires tuning
the architectural parameters of the unimodal backbones, choosing the fusing
point, and selecting the operations for fusion. Furthermore, multimodality AI
is emerging as a cutting-edge option in Internet of Things (IoT) systems where
inference latency and energy consumption are critical metrics in addition to
accuracy. In this paper, we propose Harmonic-NAS, a framework for the joint
optimization of unimodal backbones and multimodal fusion networks with hardware
awareness on resource-constrained devices. Harmonic-NAS involves a two-tier
optimization approach for the unimodal backbone architectures and fusion
strategy and operators. By incorporating the hardware dimension into the
optimization, evaluation results on various devices and multimodal datasets
have demonstrated the superiority of Harmonic-NAS over state-of-the-art
approaches achieving up to 10.9% accuracy improvement, 1.91x latency reduction,
and 2.14x energy efficiency gain. | [
"Mohamed Imed Eddine Ghebriout",
"Halima Bouzidi",
"Smail Niar",
"Hamza Ouarnoughi"
] | 2023-09-12 21:37:26 | http://arxiv.org/abs/2309.06612v2 | http://arxiv.org/pdf/2309.06612v2 | 2309.06612v2 |
Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach | Algorithm selection and hyperparameter tuning are critical steps in both
academic and applied machine learning. On the other hand, these steps are
becoming ever increasingly delicate due to the extensive rise in the number,
diversity, and distributedness of machine learning resources. Multi-agent
systems, when applied to the design of machine learning platforms, bring about
several distinctive characteristics such as scalability, flexibility, and
robustness, just to name a few. This paper proposes a fully automatic and
collaborative agent-based mechanism for selecting distributedly organized
machine learning algorithms and simultaneously tuning their hyperparameters.
Our method builds upon an existing agent-based hierarchical machine-learning
platform and augments its query structure to support the aforementioned
functionalities without being limited to specific learning, selection, and
tuning mechanisms. We have conducted theoretical assessments, formal
verification, and analytical study to demonstrate the correctness, resource
utilization, and computational efficiency of our technique. According to the
results, our solution is totally correct and exhibits linear time and space
complexity in relation to the size of available resources. To provide concrete
examples of how the proposed methodologies can effectively adapt and perform
across a range of algorithmic options and datasets, we have also conducted a
series of experiments using a system comprised of 24 algorithms and 9 datasets. | [
"Ahmad Esmaeili",
"Julia T. Rayz",
"Eric T. Matson"
] | 2023-09-12 21:07:23 | http://arxiv.org/abs/2309.06604v2 | http://arxiv.org/pdf/2309.06604v2 | 2309.06604v2 |
Reasoning with Latent Diffusion in Offline Reinforcement Learning | Offline reinforcement learning (RL) holds promise as a means to learn
high-reward policies from a static dataset, without the need for further
environment interactions. However, a key challenge in offline RL lies in
effectively stitching portions of suboptimal trajectories from the static
dataset while avoiding extrapolation errors arising due to a lack of support in
the dataset. Existing approaches use conservative methods that are tricky to
tune and struggle with multi-modal data (as we show) or rely on noisy Monte
Carlo return-to-go samples for reward conditioning. In this work, we propose a
novel approach that leverages the expressiveness of latent diffusion to model
in-support trajectory sequences as compressed latent skills. This facilitates
learning a Q-function while avoiding extrapolation error via
batch-constraining. The latent space is also expressive and gracefully copes
with multi-modal data. We show that the learned temporally-abstract latent
space encodes richer task-specific information for offline RL tasks as compared
to raw state-actions. This improves credit assignment and facilitates faster
reward propagation during Q-learning. Our method demonstrates state-of-the-art
performance on the D4RL benchmarks, particularly excelling in long-horizon,
sparse-reward tasks. | [
"Siddarth Venkatraman",
"Shivesh Khaitan",
"Ravi Tej Akella",
"John Dolan",
"Jeff Schneider",
"Glen Berseth"
] | 2023-09-12 20:58:21 | http://arxiv.org/abs/2309.06599v1 | http://arxiv.org/pdf/2309.06599v1 | 2309.06599v1 |
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning | The widespread adoption of commercial autonomous vehicles (AVs) and advanced
driver assistance systems (ADAS) may largely depend on their acceptance by
society, for which their perceived trustworthiness and interpretability to
riders are crucial. In general, this task is challenging because modern
autonomous systems software relies heavily on black-box artificial intelligence
models. Towards this goal, this paper introduces a novel dataset, Rank2Tell, a
multi-modal ego-centric dataset for Ranking the importance level and Telling
the reason for the importance. Using various close and open-ended visual
question answering, the dataset provides dense annotations of various semantic,
spatial, temporal, and relational attributes of various important objects in
complex traffic scenarios. The dense annotations and unique attributes of the
dataset make it a valuable resource for researchers working on visual scene
understanding and related fields. Further, we introduce a joint model for joint
importance level ranking and natural language captions generation to benchmark
our dataset and demonstrate performance with quantitative evaluations. | [
"Enna Sachdeva",
"Nakul Agarwal",
"Suhas Chundi",
"Sean Roelofs",
"Jiachen Li",
"Behzad Dariush",
"Chiho Choi",
"Mykel Kochenderfer"
] | 2023-09-12 20:51:07 | http://arxiv.org/abs/2309.06597v1 | http://arxiv.org/pdf/2309.06597v1 | 2309.06597v1 |
Optimal and Fair Encouragement Policy Evaluation and Learning | In consequential domains, it is often impossible to compel individuals to
take treatment, so that optimal policy rules are merely suggestions in the
presence of human non-adherence to treatment recommendations. In these same
domains, there may be heterogeneity both in who responds in taking-up
treatment, and heterogeneity in treatment efficacy. While optimal treatment
rules can maximize causal outcomes across the population, access parity
constraints or other fairness considerations can be relevant in the case of
encouragement. For example, in social services, a persistent puzzle is the gap
in take-up of beneficial services among those who may benefit from them the
most. When in addition the decision-maker has distributional preferences over
both access and average outcomes, the optimal decision rule changes. We study
causal identification, statistical variance-reduced estimation, and robust
estimation of optimal treatment rules, including under potential violations of
positivity. We consider fairness constraints such as demographic parity in
treatment take-up, and other constraints, via constrained optimization. Our
framework can be extended to handle algorithmic recommendations under an
often-reasonable covariate-conditional exclusion restriction, using our
robustness checks for lack of positivity in the recommendation. We develop a
two-stage algorithm for solving over parametrized policy classes under general
constraints to obtain variance-sensitive regret bounds. We illustrate the
methods in two case studies based on data from randomized encouragement to
enroll in insurance and from pretrial supervised release with electronic
monitoring. | [
"Angela Zhou"
] | 2023-09-12 20:45:30 | http://arxiv.org/abs/2309.07176v1 | http://arxiv.org/pdf/2309.07176v1 | 2309.07176v1 |
Convergence of Gradient-based MAML in LQR | The main objective of this research paper is to investigate the local
convergence characteristics of Model-agnostic Meta-learning (MAML) when applied
to linear system quadratic optimal control (LQR). MAML and its variations have
become popular techniques for quickly adapting to new tasks by leveraging
previous learning knowledge in areas like regression, classification, and
reinforcement learning. However, its theoretical guarantees remain unknown due
to non-convexity and its structure, making it even more challenging to ensure
stability in the dynamic system setting. This study focuses on exploring MAML
in the LQR setting, providing its local convergence guarantees while
maintaining the stability of the dynamical system. The paper also presents
simple numerical results to demonstrate the convergence properties of MAML in
LQR tasks. | [
"Negin Musavi",
"Geir E. Dullerud"
] | 2023-09-12 20:24:37 | http://arxiv.org/abs/2309.06588v2 | http://arxiv.org/pdf/2309.06588v2 | 2309.06588v2 |
Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction | Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading
cause of death in the US, underlining the importance of accurate ADRD risk
prediction. While recent advancement in ADRD risk prediction have primarily
relied on imaging analysis, yet not all patients undergo medical imaging before
an ADRD diagnosis. Merging machine learning with claims data can reveal
additional risk factors and uncover interconnections among diverse medical
codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for
ADRD risk prediction. Addressing the lack of human-interpretable reasons behind
these predictions, we introduce an innovative method to evaluate relationship
importance and its influence on ADRD risk prediction, ensuring comprehensive
interpretation.
We employed Variationally Regularized Encoder-decoder Graph Neural Network
(VGNN) for estimating ADRD likelihood. We created three scenarios to assess the
model's efficiency, using Random Forest and Light Gradient Boost Machine as
baselines. We further used our relation importance method to clarify the key
relationships for ADRD risk prediction. VGNN surpassed other baseline models by
10% in the area under the receiver operating characteristic. The integration of
the GNN model and relation importance interpretation could potentially play an
essential role in providing valuable insight into factors that may contribute
to or delay ADRD progression.
Employing a GNN approach with claims data enhances ADRD risk prediction and
provides insights into the impact of interconnected medical code relationships.
This methodology not only enables ADRD risk modeling but also shows potential
for other image analysis predictions using claims data. | [
"Xinyue Hu",
"Zenan Sun",
"Yi Nian",
"Yifang Dang",
"Fang Li",
"Jingna Feng",
"Evan Yu",
"Cui Tao"
] | 2023-09-12 20:12:08 | http://arxiv.org/abs/2309.06584v3 | http://arxiv.org/pdf/2309.06584v3 | 2309.06584v3 |
Electron Energy Regression in the CMS High-Granularity Calorimeter Prototype | We present a new publicly available dataset that contains simulated data of a
novel calorimeter to be installed at the CERN Large Hadron Collider. This
detector will have more than six-million channels with each channel capable of
position, ionisation and precision time measurement. Reconstructing these
events in an efficient way poses an immense challenge which is being addressed
with the latest machine learning techniques. As part of this development a
large prototype with 12,000 channels was built and a beam of high-energy
electrons incident on it. Using machine learning methods we have reconstructed
the energy of incident electrons from the energies of three-dimensional hits,
which is known to some precision. By releasing this data publicly we hope to
encourage experts in the application of machine learning to develop efficient
and accurate image reconstruction of these electrons. | [
"Roger Rusack",
"Bhargav Joshi",
"Alpana Alpana",
"Seema Sharma",
"Thomas Vadnais"
] | 2023-09-12 20:09:59 | http://arxiv.org/abs/2309.06582v1 | http://arxiv.org/pdf/2309.06582v1 | 2309.06582v1 |
Promises of Deep Kernel Learning for Control Synthesis | Deep Kernel Learning (DKL) combines the representational power of neural
networks with the uncertainty quantification of Gaussian Processes. Hence, it
is potentially a promising tool to learn and control complex dynamical systems.
In this work, we develop a scalable abstraction-based framework that enables
the use of DKL for control synthesis of stochastic dynamical systems against
complex specifications. Specifically, we consider temporal logic specifications
and create an end-to-end framework that uses DKL to learn an unknown system
from data and formally abstracts the DKL model into an Interval Markov Decision
Process (IMDP) to perform control synthesis with correctness guarantees.
Furthermore, we identify a deep architecture that enables accurate learning and
efficient abstraction computation. The effectiveness of our approach is
illustrated on various benchmarks, including a 5-D nonlinear stochastic system,
showing how control synthesis with DKL can substantially outperform
state-of-the-art competitive methods. | [
"Robert Reed",
"Luca Laurenti",
"Morteza Lahijanian"
] | 2023-09-12 20:04:16 | http://arxiv.org/abs/2309.06569v1 | http://arxiv.org/pdf/2309.06569v1 | 2309.06569v1 |
MELAGE: A purely python based Neuroimaging software (Neonatal) | MELAGE, a pioneering Python-based neuroimaging software, emerges as a
versatile tool for the visualization, processing, and analysis of medical
images. Initially conceived to address the unique challenges of processing 3D
ultrasound and MRI brain images during the neonatal period, MELAGE exhibits
remarkable adaptability, extending its utility to the domain of adult human
brain imaging. At its core, MELAGE features a semi-automatic brain extraction
tool empowered by a deep learning module, ensuring precise and efficient brain
structure extraction from MRI and 3D Ultrasound data. Moreover, MELAGE offers a
comprehensive suite of features, encompassing dynamic 3D visualization,
accurate measurements, and interactive image segmentation. This transformative
software holds immense promise for researchers and clinicians, offering
streamlined image analysis, seamless integration with deep learning algorithms,
and broad applicability in the realm of medical imaging. | [
"Bahram Jafrasteh",
"Simón Pedro Lubián López",
"Isabel Benavente Fernández"
] | 2023-09-12 19:54:35 | http://arxiv.org/abs/2309.07175v1 | http://arxiv.org/pdf/2309.07175v1 | 2309.07175v1 |
Commands as AI Conversations | Developers and data scientists often struggle to write command-line inputs,
even though graphical interfaces or tools like ChatGPT can assist. The
solution? "ai-cli," an open-source system inspired by GitHub Copilot that
converts natural language prompts into executable commands for various Linux
command-line tools. By tapping into OpenAI's API, which allows interaction
through JSON HTTP requests, "ai-cli" transforms user queries into actionable
command-line instructions. However, integrating AI assistance across multiple
command-line tools, especially in open source settings, can be complex.
Historically, operating systems could mediate, but individual tool
functionality and the lack of a unified approach have made centralized
integration challenging. The "ai-cli" tool, by bridging this gap through
dynamic loading and linking with each program's Readline library API, makes
command-line interfaces smarter and more user-friendly, opening avenues for
further enhancement and cross-platform applicability. | [
"Diomidis Spinellis"
] | 2023-09-12 19:52:27 | http://arxiv.org/abs/2309.06551v1 | http://arxiv.org/pdf/2309.06551v1 | 2309.06551v1 |
HurriCast: An Automatic Framework Using Machine Learning and Statistical Modeling for Hurricane Forecasting | Hurricanes present major challenges in the U.S. due to their devastating
impacts. Mitigating these risks is important, and the insurance industry is
central in this effort, using intricate statistical models for risk assessment.
However, these models often neglect key temporal and spatial hurricane patterns
and are limited by data scarcity. This study introduces a refined approach
combining the ARIMA model and K-MEANS to better capture hurricane trends, and
an Autoencoder for enhanced hurricane simulations. Our experiments show that
this hybrid methodology effectively simulate historical hurricane behaviors
while providing detailed projections of potential future trajectories and
intensities. Moreover, by leveraging a comprehensive yet selective dataset, our
simulations enrich the current understanding of hurricane patterns and offer
actionable insights for risk management strategies. | [
"Shouwei Gao",
"Meiyan Gao",
"Yuepeng Li",
"Wenqian Dong"
] | 2023-09-12 19:48:52 | http://arxiv.org/abs/2309.07174v1 | http://arxiv.org/pdf/2309.07174v1 | 2309.07174v1 |
Distributionally Robust Transfer Learning | Many existing transfer learning methods rely on leveraging information from
source data that closely resembles the target data. However, this approach
often overlooks valuable knowledge that may be present in different yet
potentially related auxiliary samples. When dealing with a limited amount of
target data and a diverse range of source models, our paper introduces a novel
approach, Distributionally Robust Optimization for Transfer Learning
(TransDRO), that breaks free from strict similarity constraints. TransDRO is
designed to optimize the most adversarial loss within an uncertainty set,
defined as a collection of target populations generated as a convex combination
of source distributions that guarantee excellent prediction performances for
the target data. TransDRO effectively bridges the realms of transfer learning
and distributional robustness prediction models. We establish the
identifiability of TransDRO and its interpretation as a weighted average of
source models closest to the baseline model. We also show that TransDRO
achieves a faster convergence rate than the model fitted with the target data.
Our comprehensive numerical studies and analysis of multi-institutional
electronic health records data using TransDRO further substantiate the
robustness and accuracy of TransDRO, highlighting its potential as a powerful
tool in transfer learning applications. | [
"Xin Xiong",
"Zijian Guo",
"Tianxi Cai"
] | 2023-09-12 19:18:52 | http://arxiv.org/abs/2309.06534v1 | http://arxiv.org/pdf/2309.06534v1 | 2309.06534v1 |
Hierarchical Multi-Task Learning Framework for Session-based Recommendations | While session-based recommender systems (SBRSs) have shown superior
recommendation performance, multi-task learning (MTL) has been adopted by SBRSs
to enhance their prediction accuracy and generalizability further. Hierarchical
MTL (H-MTL) sets a hierarchical structure between prediction tasks and feeds
outputs from auxiliary tasks to main tasks. This hierarchy leads to richer
input features for main tasks and higher interpretability of predictions,
compared to existing MTL frameworks. However, the H-MTL framework has not been
investigated in SBRSs yet. In this paper, we propose HierSRec which
incorporates the H-MTL architecture into SBRSs. HierSRec encodes a given
session with a metadata-aware Transformer and performs next-category prediction
(i.e., auxiliary task) with the session encoding. Next, HierSRec conducts
next-item prediction (i.e., main task) with the category prediction result and
session encoding. For scalable inference, HierSRec creates a compact set of
candidate items (e.g., 4% of total items) per test example using the category
prediction. Experiments show that HierSRec outperforms existing SBRSs as per
next-item prediction accuracy on two session-based recommendation datasets. The
accuracy of HierSRec measured with the carefully-curated candidate items aligns
with the accuracy of HierSRec calculated with all items, which validates the
usefulness of our candidate generation scheme via H-MTL. | [
"Sejoon Oh",
"Walid Shalaby",
"Amir Afsharinejad",
"Xiquan Cui"
] | 2023-09-12 19:11:34 | http://arxiv.org/abs/2309.06533v1 | http://arxiv.org/pdf/2309.06533v1 | 2309.06533v1 |
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers | For machine learning with tabular data, Table Transformer (TabTransformer) is
a state-of-the-art neural network model, while Differential Privacy (DP) is an
essential component to ensure data privacy. In this paper, we explore the
benefits of combining these two aspects together in the scenario of transfer
learning -- differentially private pre-training and fine-tuning of
TabTransformers with a variety of parameter-efficient fine-tuning (PEFT)
methods, including Adapter, LoRA, and Prompt Tuning. Our extensive experiments
on the ACSIncome dataset show that these PEFT methods outperform traditional
approaches in terms of the accuracy of the downstream task and the number of
trainable parameters, thus achieving an improved trade-off among parameter
efficiency, privacy, and accuracy. Our code is available at
github.com/IBM/DP-TabTransformer. | [
"Xilong Wang",
"Chia-Mu Yu",
"Pin-Yu Chen"
] | 2023-09-12 19:08:26 | http://arxiv.org/abs/2309.06526v1 | http://arxiv.org/pdf/2309.06526v1 | 2309.06526v1 |
Using Unsupervised and Supervised Learning and Digital Twin for Deep Convective Ice Storm Classification | Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a primary
radar intelligently targets ice storms based on information collected by a
lookahead radiometer. Critical to the intelligent targeting is accurate
identification of storm/cloud types from eight bands of radiance collected by
the radiometer. The cloud types of interest are: clear sky, thin cirrus,
cirrus, rainy anvil, and convection core.
We describe multi-step use of Machine Learning and Digital Twin of the
Earth's atmosphere to derive such a classifier. First, a digital twin of
Earth's atmosphere called a Weather Research Forecast (WRF) is used generate
simulated lookahead radiometer data as well as deeper "science" hidden
variables. The datasets simulate a tropical region over the Caribbean and a
non-tropical region over the Atlantic coast of the United States. A K-means
clustering over the scientific hidden variables was utilized by human experts
to generate an automatic labelling of the data - mapping each physical data
point to cloud types by scientists informed by mean/centroids of hidden
variables of the clusters. Next, classifiers were trained with the inputs of
the simulated radiometer data and its corresponding label. The classifiers of a
random decision forest (RDF), support vector machine (SVM), Gaussian na\"ive
bayes, feed forward artificial neural network (ANN), and a convolutional neural
network (CNN) were trained. Over the tropical dataset, the best performing
classifier was able to identify non-storm and storm clouds with over 80%
accuracy in each class for a held-out test set. Over the non-tropical dataset,
the best performing classifier was able to classify non-storm clouds with over
90% accuracy and storm clouds with over 40% accuracy. Additionally both sets of
classifiers were shown to be resilient to instrument noise. | [
"Jason Swope",
"Steve Chien",
"Emily Dunkel",
"Xavier Bosch-Lluis",
"Qing Yue",
"William Deal"
] | 2023-09-12 19:00:55 | http://arxiv.org/abs/2309.07173v1 | http://arxiv.org/pdf/2309.07173v1 | 2309.07173v1 |
A Q-learning Approach for Adherence-Aware Recommendations | In many real-world scenarios involving high-stakes and safety implications, a
human decision-maker (HDM) may receive recommendations from an artificial
intelligence while holding the ultimate responsibility of making decisions. In
this letter, we develop an "adherence-aware Q-learning" algorithm to address
this problem. The algorithm learns the "adherence level" that captures the
frequency with which an HDM follows the recommended actions and derives the
best recommendation policy in real time. We prove the convergence of the
proposed Q-learning algorithm to the optimal value and evaluate its performance
across various scenarios. | [
"Ioannis Faros",
"Aditya Dave",
"Andreas A. Malikopoulos"
] | 2023-09-12 18:50:24 | http://arxiv.org/abs/2309.06519v1 | http://arxiv.org/pdf/2309.06519v1 | 2309.06519v1 |
Bayesian longitudinal tensor response regression for modeling neuroplasticity | A major interest in longitudinal neuroimaging studies involves investigating
voxel-level neuroplasticity due to treatment and other factors across visits.
However, traditional voxel-wise methods are beset with several pitfalls, which
can compromise the accuracy of these approaches. We propose a novel Bayesian
tensor response regression approach for longitudinal imaging data, which pools
information across spatially-distributed voxels to infer significant changes
while adjusting for covariates. The proposed method, which is implemented using
Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to
reduce dimensionality and preserve spatial configurations of voxels when
estimating coefficients. It also enables feature selection via joint credible
regions which respect the shape of the posterior distributions for more
accurate inference. In addition to group level inferences, the method is able
to infer individual-level neuroplasticity, allowing for examination of
personalized disease or recovery trajectories. The advantages of the proposed
approach in terms of prediction and feature selection over voxel-wise
regression are highlighted via extensive simulation studies. Subsequently, we
apply the approach to a longitudinal Aphasia dataset consisting of task
functional MRI images from a group of subjects who were administered either a
control intervention or intention treatment at baseline and were followed up
over subsequent visits. Our analysis revealed that while the control therapy
showed long-term increases in brain activity, the intention treatment produced
predominantly short-term changes, both of which were concentrated in distinct
localized regions. In contrast, the voxel-wise regression failed to detect any
significant neuroplasticity after multiplicity adjustments, which is
biologically implausible and implies lack of power. | [
"Suprateek Kundu",
"Alec Reinhardt",
"Serena Song",
"Joo Han",
"M. Lawson Meadows",
"Bruce Crosson",
"Venkatagiri Krishnamurthy"
] | 2023-09-12 18:48:18 | http://arxiv.org/abs/2309.10065v2 | http://arxiv.org/pdf/2309.10065v2 | 2309.10065v2 |
Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets | The COVID-19 pandemic has presented significant challenges to the healthcare
industry and society as a whole. With the rapid development of COVID-19
vaccines, social media platforms have become a popular medium for discussions
on vaccine-related topics. Identifying vaccine-related tweets and analyzing
them can provide valuable insights for public health research-ers and
policymakers. However, manual annotation of a large number of tweets is
time-consuming and expensive. In this study, we evaluate the usage of Large
Language Models, in this case GPT-4 (March 23 version), and weak supervision,
to identify COVID-19 vaccine-related tweets, with the purpose of comparing
performance against human annotators. We leveraged a manu-ally curated
gold-standard dataset and used GPT-4 to provide labels without any additional
fine-tuning or instructing, in a single-shot mode (no additional prompting). | [
"Ramya Tekumalla",
"Juan M. Banda"
] | 2023-09-12 18:18:23 | http://arxiv.org/abs/2309.06503v1 | http://arxiv.org/pdf/2309.06503v1 | 2309.06503v1 |
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale | Shampoo is an online and stochastic optimization algorithm belonging to the
AdaGrad family of methods for training neural networks. It constructs a
block-diagonal preconditioner where each block consists of a coarse Kronecker
product approximation to full-matrix AdaGrad for each parameter of the neural
network. In this work, we provide a complete description of the algorithm as
well as the performance optimizations that our implementation leverages to
train deep networks at-scale in PyTorch. Our implementation enables fast
multi-GPU distributed data-parallel training by distributing the memory and
computation associated with blocks of each parameter via PyTorch's DTensor data
structure and performing an AllGather primitive on the computed search
directions at each iteration. This major performance enhancement enables us to
achieve at most a 10% performance reduction in per-step wall-clock time
compared against standard diagonal-scaling-based adaptive gradient methods. We
validate our implementation by performing an ablation study on training
ImageNet ResNet50, demonstrating Shampoo's superiority over standard training
recipes with minimal hyperparameter tuning. | [
"Hao-Jun Michael Shi",
"Tsung-Hsien Lee",
"Shintaro Iwasaki",
"Jose Gallego-Posada",
"Zhijing Li",
"Kaushik Rangadurai",
"Dheevatsa Mudigere",
"Michael Rabbat"
] | 2023-09-12 18:11:10 | http://arxiv.org/abs/2309.06497v1 | http://arxiv.org/pdf/2309.06497v1 | 2309.06497v1 |
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