categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.13612
| null | null |
http://arxiv.org/pdf/2403.13612v1
|
2024-03-20T14:03:57Z
|
2024-03-20T14:03:57Z
|
Does Differentially Private Synthetic Data Lead to Synthetic
Discoveries?
|
Background: Synthetic data has been proposed as a solution for sharing anonymized versions of sensitive biomedical datasets. Ideally, synthetic data should preserve the structure and statistical properties of the original data, while protecting the privacy of the individual subjects. Differential privacy (DP) is currently considered the gold standard approach for balancing this trade-off. Objectives: The aim of this study is to evaluate the Mann-Whitney U test on DP-synthetic biomedical data in terms of Type I and Type II errors, in order to establish whether statistical hypothesis testing performed on privacy preserving synthetic data is likely to lead to loss of test's validity or decreased power. Methods: We evaluate the Mann-Whitney U test on DP-synthetic data generated from real-world data, including a prostate cancer dataset (n=500) and a cardiovascular dataset (n=70 000), as well as on data drawn from two Gaussian distributions. Five different DP-synthetic data generation methods are evaluated, including two basic DP histogram release methods and MWEM, Private-PGM, and DP GAN algorithms. Conclusion: Most of the tested DP-synthetic data generation methods showed inflated Type I error, especially at privacy budget levels of $epsilonleq 1$. This result calls for caution when releasing and analyzing DP-synthetic data: low p-values may be obtained in statistical tests simply as a byproduct of the noise added to protect privacy. A DP smoothed histogram-based synthetic data generation method was shown to produce valid Type I error for all privacy levels tested but required a large original dataset size and a modest privacy budget ($epsilongeq 5$) in order to have reasonable Type II error levels.
|
[
"['Ileana Montoya Perez' 'Parisa Movahedi' 'Valtteri Nieminen'\n 'Antti Airola' 'Tapio Pahikkala']"
] |
null | null |
2403.13625
| null | null |
http://arxiv.org/abs/2403.13625v1
|
2024-03-20T14:22:19Z
|
2024-03-20T14:22:19Z
|
Enhancing Law Enforcement Training: A Gamified Approach to Detecting
Terrorism Financing
|
Tools for fighting cyber-criminal activities using new technologies are promoted and deployed every day. However, too often, they are unnecessarily complex and hard to use, requiring deep domain and technical knowledge. These characteristics often limit the engagement of law enforcement and end-users in these technologies that, despite their potential, remain misunderstood. For this reason, in this study, we describe our experience in combining learning and training methods and the potential benefits of gamification to enhance technology transfer and increase adult learning. In fact, in this case, participants are experienced practitioners in professions/industries that are exposed to terrorism financing (such as Law Enforcement Officers, Financial Investigation Officers, private investigators, etc.) We define training activities on different levels for increasing the exchange of information about new trends and criminal modus operandi among and within law enforcement agencies, intensifying cross-border cooperation and supporting efforts to combat and prevent terrorism funding activities. On the other hand, a game (hackathon) is designed to address realistic challenges related to the dark net, crypto assets, new payment systems and dark web marketplaces that could be used for terrorist activities. The entire methodology was evaluated using quizzes, contest results, and engagement metrics. In particular, training events show about 60% of participants complete the 11-week training course, while the Hackathon results, gathered in two pilot studies (Madrid and The Hague), show increasing expertise among the participants (progression in the achieved points on average). At the same time, more than 70% of participants positively evaluate the use of the gamification approach, and more than 85% of them consider the implemented Use Cases suitable for their investigations.
|
[
"['Francesco Zola' 'Lander Segurola' 'Erin King' 'Martin Mullins'\n 'Raul Orduna']"
] |
null | null |
2403.13627
| null | null |
http://arxiv.org/pdf/2403.13627v1
|
2024-03-20T14:23:17Z
|
2024-03-20T14:23:17Z
|
Efficient exploration of high-Tc superconductors by a gradient-based
composition design
|
We propose a material design method via gradient-based optimization on compositions, overcoming the limitations of traditional methods: exhaustive database searches and conditional generation models. It optimizes inputs via backpropagation, aligning the model's output closely with the target property and facilitating the discovery of unlisted materials and precise property determination. Our method is also capable of adaptive optimization under new conditions without retraining. Applying to exploring high-Tc superconductors, we identified potential compositions beyond existing databases and discovered new hydrogen superconductors via conditional optimization. This method is versatile and significantly advances material design by enabling efficient, extensive searches and adaptability to new constraints.
|
[
"['Akihiro Fujii' 'Koji Shimizu' 'Satoshi Watanabe']"
] |
null | null |
2403.13658
| null | null |
http://arxiv.org/pdf/2403.13658v3
|
2024-07-05T15:42:25Z
|
2024-03-20T15:06:49Z
|
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics
Instability Detection
|
Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these approaches often fall short especially when the size of labeled patient data is limited, a common challenge in the medical domain. Furthermore, only a few studies have explored multimodal methods to study CHDI, which mostly rely on costly modalities such as cardiac MRI and echocardiogram. In response to these limitations, we propose a novel multimodal variational autoencoder ($text{CardioVAE}_text{X,G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset. Specifically, $text{CardioVAE}_text{X,G}$ introduces a novel tri-stream pre-training strategy to learn both shared and modality-specific features, thus enabling fine-tuning with both unimodal and multimodal datasets. We pre-train $text{CardioVAE}_text{X,G}$ on a large, unlabeled dataset of $50,982$ subjects from a subset of MIMIC database and then fine-tune the pre-trained model on a labeled dataset of $795$ subjects from the ASPIRE registry. Comprehensive evaluations against existing methods show that $text{CardioVAE}_text{X,G}$ offers promising performance (AUROC $=0.79$ and Accuracy $=0.77$), representing a significant step forward in non-invasive prediction of CHDI. Our model also excels in producing fine interpretations of predictions directly associated with clinical features, thereby supporting clinical decision-making.
|
[
"['Mohammod N. I. Suvon' 'Prasun C. Tripathi' 'Wenrui Fan' 'Shuo Zhou'\n 'Xianyuan Liu' 'Samer Alabed' 'Venet Osmani' 'Andrew J. Swift'\n 'Chen Chen' 'Haiping Lu']"
] |
null | null |
2403.13672
| null | null |
http://arxiv.org/pdf/2403.13672v1
|
2024-03-20T15:29:59Z
|
2024-03-20T15:29:59Z
|
Machine Learning Optimized Approach for Parameter Selection in MESHFREE
Simulations
|
Meshfree simulation methods are emerging as compelling alternatives to conventional mesh-based approaches, particularly in the fields of Computational Fluid Dynamics (CFD) and continuum mechanics. In this publication, we provide a comprehensive overview of our research combining Machine Learning (ML) and Fraunhofer's MESHFREE software (www.meshfree.eu), a powerful tool utilizing a numerical point cloud in a Generalized Finite Difference Method (GFDM). This tool enables the effective handling of complex flow domains, moving geometries, and free surfaces, while allowing users to finely tune local refinement and quality parameters for an optimal balance between computation time and results accuracy. However, manually determining the optimal parameter combination poses challenges, especially for less experienced users. We introduce a novel ML-optimized approach, using active learning, regression trees, and visualization on MESHFREE simulation data, demonstrating the impact of input combinations on results quality and computation time. This research contributes valuable insights into parameter optimization in meshfree simulations, enhancing accessibility and usability for a broader user base in scientific and engineering applications.
|
[
"['Paulami Banerjee' 'Mohan Padmanabha' 'Chaitanya Sanghavi'\n 'Isabel Michel' 'Simone Gramsch']"
] |
null | null |
2403.13681
| null | null |
http://arxiv.org/pdf/2403.13681v1
|
2024-03-20T15:39:54Z
|
2024-03-20T15:39:54Z
|
PARAMANU-AYN: An Efficient Novel Generative and Instruction-tuned
Language Model for Indian Legal Case Documents
|
In this paper, we present PARAMANU-AYN, a language model based exclusively on case documents of the Supreme Court of India, the Constitution of India, and the Indian Penal Code. The novel Auto Regressive (AR) decoder based model is pretrained from scratch at a context size of 8192. We evaluated our pretrained legal model on perplexity metrics. We also instruction-tuned our pretrained model on a set of 10,763 instructions covering various legal tasks such as legal reasoning, judgement explanation, legal clause generation, legal drafting, legal contract drafting, case summarization, constitutional question-answering, etc. We also evaluated the responses of prompts for instruction-tuned models by GPT-3.5-Turbo on clarity, relevance, completeness, and legal reasoning metrics in a scale of 10. Our model can be run on CPU and achieved 42.46 tokens/sec CPU inference speed. We found that our models, despite not being pretrained on legal books, various legal contracts, and legal documents, were able to learn the domain knowledge required for drafting various legal contracts and legal clauses, and generalize to draft legal contracts and legal clauses with limited instruction tuning. Hence, we conclude that for a strong domain-specialized generative language model (such as legal), very large amounts of data are not required to develop models from scratch. We believe that this work is the first attempt to make a dedicated generative legal language model from scratch for Indian Supreme Court jurisdiction or in legal NLP overall. We plan to release our Paramanu-Ayn model at https://www.bharatgpts.com.
|
[
"['Mitodru Niyogi' 'Arnab Bhattacharya']"
] |
null | null |
2403.13695
| null | null |
http://arxiv.org/abs/2403.13695v1
|
2024-03-20T15:57:44Z
|
2024-03-20T15:57:44Z
|
Loss Regularizing Robotic Terrain Classification
|
Locomotion mechanics of legged robots are suitable when pacing through difficult terrains. Recognising terrains for such robots are important to fully yoke the versatility of their movements. Consequently, robotic terrain classification becomes significant to classify terrains in real time with high accuracy. The conventional classifiers suffer from overfitting problem, low accuracy problem, high variance problem, and not suitable for live dataset. On the other hand, classifying a growing dataset is difficult for convolution based terrain classification. Supervised recurrent models are also not practical for this classification. Further, the existing recurrent architectures are still evolving to improve accuracy of terrain classification based on live variable-length sensory data collected from legged robots. This paper proposes a new semi-supervised method for terrain classification of legged robots, avoiding preprocessing of long variable-length dataset. The proposed method has a stacked Long Short-Term Memory architecture, including a new loss regularization. The proposed method solves the existing problems and improves accuracy. Comparison with the existing architectures show the improvements.
|
[
"['Shakti Deo Kumar' 'Sudhanshu Tripathi' 'Krishna Ujjwal'\n 'Sarvada Sakshi Jha' 'Suddhasil De']"
] |
null | null |
2403.13701
| null | null |
http://arxiv.org/pdf/2403.13701v1
|
2024-03-20T16:06:01Z
|
2024-03-20T16:06:01Z
|
What Matters for Active Texture Recognition With Vision-Based Tactile
Sensors
|
This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an implementation of information-theoretic exploration strategies based on minimizing predictive entropy and variance of probabilistic models. Through ablation studies and human experiments, we investigate which components are crucial for quick and reliable texture recognition. Along with the active sampling strategies, we evaluate neural network architectures, representations of uncertainty, influence of data augmentation, and dataset variability. By evaluating our method on a previously published Active Clothing Perception Dataset and on a real robotic system, we establish that the choice of the active exploration strategy has only a minor influence on the recognition accuracy, whereas data augmentation and dropout rate play a significantly larger role. In a comparison study, while humans achieve 66.9% recognition accuracy, our best approach reaches 90.0% in under 5 touches, highlighting that vision-based tactile sensors are highly effective for fabric texture recognition.
|
[
"['Alina Böhm' 'Tim Schneider' 'Boris Belousov' 'Alap Kshirsagar'\n 'Lisa Lin' 'Katja Doerschner' 'Knut Drewing' 'Constantin A. Rothkopf'\n 'Jan Peters']"
] |
null | null |
2403.13704
| null | null |
http://arxiv.org/pdf/2403.13704v1
|
2024-03-20T16:08:27Z
|
2024-03-20T16:08:27Z
|
Improving the Adaptive Moment Estimation (ADAM) stochastic optimizer
through an Implicit-Explicit (IMEX) time-stepping approach
|
The Adam optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. This work shows that the classical Adam algorithm is a first order implicit-explicit (IMEX) Euler discretization of the underlying ODE. Employing the time discretization point of view, we propose new extensions of the Adam scheme obtained by using higher order IMEX methods to solve the ODE. Based on this approach, we derive a new optimization algorithm for neural network training that performs better than classical Adam on several regression and classification problems.
|
[
"['Abhinab Bhattacharjee' 'Andrey A. Popov' 'Arash Sarshar' 'Adrian Sandu']"
] |
null | null |
2403.13724
| null | null |
http://arxiv.org/pdf/2403.13724v1
|
2024-03-20T16:33:06Z
|
2024-03-20T16:33:06Z
|
Probabilistic Forecasting with Stochastic Interpolants and Föllmer
Processes
|
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasting problem as sampling from the conditional distribution of the future system state given its current state. To this end, we leverage the framework of stochastic interpolants, which facilitates the construction of a generative model between an arbitrary base distribution and the target. We design a fictitious, non-physical stochastic dynamics that takes as initial condition the current system state and produces as output a sample from the target conditional distribution in finite time and without bias. This process therefore maps a point mass centered at the current state onto a probabilistic ensemble of forecasts. We prove that the drift coefficient entering the stochastic differential equation (SDE) achieving this task is non-singular, and that it can be learned efficiently by square loss regression over the time-series data. We show that the drift and the diffusion coefficients of this SDE can be adjusted after training, and that a specific choice that minimizes the impact of the estimation error gives a F"ollmer process. We highlight the utility of our approach on several complex, high-dimensional forecasting problems, including stochastically forced Navier-Stokes and video prediction on the KTH and CLEVRER datasets.
|
[
"['Yifan Chen' 'Mark Goldstein' 'Mengjian Hua' 'Michael S. Albergo'\n 'Nicholas M. Boffi' 'Eric Vanden-Eijnden']"
] |
null | null |
2403.13728
| null | null |
http://arxiv.org/pdf/2403.13728v2
|
2024-04-10T15:25:00Z
|
2024-03-20T16:38:26Z
|
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via
Multiplier Induced Loss Landscape Scheduling
|
We address the online combinatorial choice of weight multipliers for multi-objective optimization of many loss terms parameterized by neural works via a probabilistic graphical model (PGM) for the joint model parameter and multiplier evolution process, with a hypervolume based likelihood promoting multi-objective descent. The corresponding parameter and multiplier estimation as a sequential decision process is then cast into an optimal control problem, where the multi-objective descent goal is dispatched hierarchically into a series of constraint optimization sub-problems. The subproblem constraint automatically adapts itself according to Pareto dominance and serves as the setpoint for the low level multiplier controller to schedule loss landscapes via output feedback of each loss term. Our method is multiplier-free and operates at the timescale of epochs, thus saves tremendous computational resources compared to full training cycle multiplier tuning. It also circumvents the excessive memory requirements and heavy computational burden of existing multi-objective deep learning methods. We applied it to domain invariant variational auto-encoding with 6 loss terms on the PACS domain generalization task, and observed robust performance across a range of controller hyperparameters, as well as different multiplier initial conditions, outperforming other multiplier scheduling methods. We offered modular implementation of our method, admitting extension to custom definition of many loss terms.
|
[
"['Xudong Sun' 'Nutan Chen' 'Alexej Gossmann' 'Yu Xing' 'Carla Feistner'\n 'Emilio Dorigatt' 'Felix Drost' 'Daniele Scarcella' 'Lisa Beer'\n 'Carsten Marr']"
] |
null | null |
2403.13729
| null | null |
http://arxiv.org/pdf/2403.13729v1
|
2024-03-20T16:39:17Z
|
2024-03-20T16:39:17Z
|
Reinforcement Learning for Online Testing of Autonomous Driving Systems:
a Replication and Extension Study
|
In a recent study, Reinforcement Learning (RL) used in combination with many-objective search, has been shown to outperform alternative techniques (random search and many-objective search) for online testing of Deep Neural Network-enabled systems. The empirical evaluation of these techniques was conducted on a state-of-the-art Autonomous Driving System (ADS). This work is a replication and extension of that empirical study. Our replication shows that RL does not outperform pure random test generation in a comparison conducted under the same settings of the original study, but with no confounding factor coming from the way collisions are measured. Our extension aims at eliminating some of the possible reasons for the poor performance of RL observed in our replication: (1) the presence of reward components providing contrasting or useless feedback to the RL agent; (2) the usage of an RL algorithm (Q-learning) which requires discretization of an intrinsically continuous state space. Results show that our new RL agent is able to converge to an effective policy that outperforms random testing. Results also highlight other possible improvements, which open to further investigations on how to best leverage RL for online ADS testing.
|
[
"['Luca Giamattei' 'Matteo Biagiola' 'Roberto Pietrantuono' 'Stefano Russo'\n 'Paolo Tonella']"
] |
null | null |
2403.13740
| null | null |
http://arxiv.org/pdf/2403.13740v1
|
2024-03-20T16:47:28Z
|
2024-03-20T16:47:28Z
|
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable
Neural Networks
|
The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications. Promising approaches to overcome such limitations are Prototype-Based Self-Explainable Neural Networks (PSENNs), whose predictions rely on the similarity between the input at hand and a set of prototypical representations of the output classes, offering therefore a deep, yet transparent-by-design, architecture. So far, such models have been designed by considering pointwise estimates for the prototypes, which remain fixed after the learning phase of the model. In this paper, we introduce a probabilistic reformulation of PSENNs, called Prob-PSENN, which replaces point estimates for the prototypes with probability distributions over their values. This provides not only a more flexible framework for an end-to-end learning of prototypes, but can also capture the explanatory uncertainty of the model, which is a missing feature in previous approaches. In addition, since the prototypes determine both the explanation and the prediction, Prob-PSENNs allow us to detect when the model is making uninformed or uncertain predictions, and to obtain valid explanations for them. Our experiments demonstrate that Prob-PSENNs provide more meaningful and robust explanations than their non-probabilistic counterparts, thus enhancing the explainability and reliability of the models.
|
[
"['Jon Vadillo' 'Roberto Santana' 'Jose A. Lozano' 'Marta Kwiatkowska']"
] |
null | null |
2403.13748
| null | null |
http://arxiv.org/pdf/2403.13748v2
|
2024-06-07T14:43:56Z
|
2024-03-20T16:56:08Z
|
Variational Inference for Uncertainty Quantification: an Analysis of
Trade-offs
|
Given an intractable distribution $p$, the problem of variational inference (VI) is to find the best approximation from some more tractable family $Q$. Commonly, one chooses $Q$ to be a family of factorized distributions (i.e., the mean-field assumption), even though~$p$ itself does not factorize. We show that this mismatch leads to an impossibility theorem: if $p$ does not factorize, then any factorized approximation $qin Q$ can correctly estimate at most one of the following three measures of uncertainty: (i) the marginal variances, (ii) the marginal precisions, or (iii) the generalized variance (which can be related to the entropy). In practice, the best variational approximation in $Q$ is found by minimizing some divergence $D(q,p)$ between distributions, and so we ask: how does the choice of divergence determine which measure of uncertainty, if any, is correctly estimated by VI? We consider the classic Kullback-Leibler divergences, the more general R'enyi divergences, and a score-based divergence which compares $nabla log p$ and $nabla log q$. We provide a thorough theoretical analysis in the setting where $p$ is a Gaussian and $q$ is a (factorized) Gaussian. We show that all the considered divergences can be textit{ordered} based on the estimates of uncertainty they yield as objective functions for~VI. Finally, we empirically evaluate the validity of this ordering when the target distribution $p$ is not Gaussian.
|
[
"['Charles C. Margossian' 'Loucas Pillaud-Vivien' 'Lawrence K. Saul']"
] |
null | null |
2403.13749
| null | null |
http://arxiv.org/pdf/2403.13749v1
|
2024-03-20T16:58:28Z
|
2024-03-20T16:58:28Z
|
Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph
Representational Learning
|
We introduce $r$-loopy Weisfeiler-Leman ($r$-$ell{}$WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, $r$-$ell{}$MPNN, that can count cycles up to length $r + 2$. Most notably, we show that $r$-$ell{}$WL can count homomorphisms of cactus graphs. This strictly extends classical 1-WL, which can only count homomorphisms of trees and, in fact, is incomparable to $k$-WL for any fixed $k$. We empirically validate the expressive and counting power of the proposed $r$-$ell{}$MPNN on several synthetic datasets and present state-of-the-art predictive performance on various real-world datasets. The code is available at https://github.com/RPaolino/loopy
|
[
"['Raffaele Paolino' 'Sohir Maskey' 'Pascal Welke' 'Gitta Kutyniok']"
] |
null | null |
2403.13765
| null | null |
http://arxiv.org/pdf/2403.13765v1
|
2024-03-20T17:28:17Z
|
2024-03-20T17:28:17Z
|
Towards Principled Representation Learning from Videos for Reinforcement
Learning
|
We study pre-training representations for decision-making using video data, which is abundantly available for tasks such as game agents and software testing. Even though significant empirical advances have been made on this problem, a theoretical understanding remains absent. We initiate the theoretical investigation into principled approaches for representation learning and focus on learning the latent state representations of the underlying MDP using video data. We study two types of settings: one where there is iid noise in the observation, and a more challenging setting where there is also the presence of exogenous noise, which is non-iid noise that is temporally correlated, such as the motion of people or cars in the background. We study three commonly used approaches: autoencoding, temporal contrastive learning, and forward modeling. We prove upper bounds for temporal contrastive learning and forward modeling in the presence of only iid noise. We show that these approaches can learn the latent state and use it to do efficient downstream RL with polynomial sample complexity. When exogenous noise is also present, we establish a lower bound result showing that the sample complexity of learning from video data can be exponentially worse than learning from action-labeled trajectory data. This partially explains why reinforcement learning with video pre-training is hard. We evaluate these representational learning methods in two visual domains, yielding results that are consistent with our theoretical findings.
|
[
"['Dipendra Misra' 'Akanksha Saran' 'Tengyang Xie' 'Alex Lamb'\n 'John Langford']"
] |
null | null |
2403.13771
| null | null |
http://arxiv.org/pdf/2403.13771v1
|
2024-03-20T17:33:02Z
|
2024-03-20T17:33:02Z
|
Describe-and-Dissect: Interpreting Neurons in Vision Networks with
Language Models
|
In this paper, we propose Describe-and-Dissect (DnD), a novel method to describe the roles of hidden neurons in vision networks. DnD utilizes recent advancements in multimodal deep learning to produce complex natural language descriptions, without the need for labeled training data or a predefined set of concepts to choose from. Additionally, DnD is training-free, meaning we don't train any new models and can easily leverage more capable general purpose models in the future. We have conducted extensive qualitative and quantitative analysis to show that DnD outperforms prior work by providing higher quality neuron descriptions. Specifically, our method on average provides the highest quality labels and is more than 2 times as likely to be selected as the best explanation for a neuron than the best baseline.
|
[
"['Nicholas Bai' 'Rahul A. Iyer' 'Tuomas Oikarinen' 'Tsui-Wei Weng']"
] |
null | null |
2403.13781
| null | null |
http://arxiv.org/pdf/2403.13781v1
|
2024-03-20T17:43:58Z
|
2024-03-20T17:43:58Z
|
Sparse Implementation of Versatile Graph-Informed Layers
|
Graph Neural Networks (GNNs) have emerged as effective tools for learning tasks on graph-structured data. Recently, Graph-Informed (GI) layers were introduced to address regression tasks on graph nodes, extending their applicability beyond classic GNNs. However, existing implementations of GI layers lack efficiency due to dense memory allocation. This paper presents a sparse implementation of GI layers, leveraging the sparsity of adjacency matrices to reduce memory usage significantly. Additionally, a versatile general form of GI layers is introduced, enabling their application to subsets of graph nodes. The proposed sparse implementation improves the concrete computational efficiency and scalability of the GI layers, permitting to build deeper Graph-Informed Neural Networks (GINNs) and facilitating their scalability to larger graphs.
|
[
"['Francesco Della Santa']"
] |
null | null |
2403.13784
| null | null |
http://arxiv.org/pdf/2403.13784v3
|
2024-06-03T16:44:31Z
|
2024-03-20T17:47:08Z
|
The Model Openness Framework: Promoting Completeness and Openness for
Reproducibility, Transparency, and Usability in Artificial Intelligence
|
Generative AI (GAI) offers unprecedented opportunities for research and innovation, but its commercialization has raised concerns about transparency, reproducibility, and safety. Many open GAI models lack the necessary components for full understanding and reproducibility, and some use restrictive licenses whilst claiming to be ``open-source''. To address these concerns, we propose the Model Openness Framework (MOF), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. The MOF requires specific components of the model development lifecycle to be included and released under appropriate open licenses. This framework aims to prevent misrepresentation of models claiming to be open, guide researchers and developers in providing all model components under permissive licenses, and help individuals and organizations identify models that can be safely adopted without restrictions. By promoting transparency and reproducibility, the MOF combats ``openwashing'' practices and establishes completeness and openness as primary criteria alongside the core tenets of responsible AI. Wide adoption of the MOF will foster a more open AI ecosystem, benefiting research, innovation, and adoption of state-of-the-art models.
|
[
"['Matt White' 'Ibrahim Haddad' 'Cailean Osborne' 'Xiao-Yang Liu Yanglet'\n 'Ahmed Abdelmonsef' 'Sachin Varghese']"
] |
null | null |
2403.13785
| null | null |
http://arxiv.org/pdf/2403.13785v1
|
2024-03-20T17:47:25Z
|
2024-03-20T17:47:25Z
|
Towards an extension of Fault Trees in the Predictive Maintenance
Scenario
|
One of the most appreciated features of Fault Trees (FTs) is their simplicity, making them fit into industrial processes. As such processes evolve in time, considering new aspects of large modern systems, modelling techniques based on FTs have adapted to these needs. This paper proposes an extension of FTs to take into account the problem of Predictive Maintenance, one of the challenges of the modern dependability field of study. The paper sketches the Predictive Fault Tree language and proposes some use cases to support their modelling and analysis in concrete industrial settings.
|
[
"['Roberta De Fazio' 'Stefano Marrone' 'Laura Verde' 'Vincenzo Reccia'\n 'Paolo Valletta']"
] |
null | null |
2403.13787
| null | null |
http://arxiv.org/pdf/2403.13787v2
|
2024-06-08T16:40:12Z
|
2024-03-20T17:49:54Z
|
RewardBench: Evaluating Reward Models for Language Modeling
|
Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in them. Resources for reward model training and understanding are sparse in the nascent open-source community around them. To enhance scientific understanding of reward models, we present RewardBench, a benchmark dataset and code-base for evaluation. The RewardBench dataset is a collection of prompt-chosen-rejected trios spanning chat, reasoning, and safety, to benchmark how reward models perform on challenging, structured and out-of-distribution queries. We create specific comparison datasets for RMs that have subtle, but verifiable reasons (e.g. bugs, incorrect facts) why one answer should be preferred to another. On the RewardBench leaderboard, we evaluate reward models trained with a variety of methods, such as the direct MLE training of classifiers and the implicit reward modeling of Direct Preference Optimization (DPO). We present many findings on propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models towards a better understanding of the RLHF process.
|
[
"['Nathan Lambert' 'Valentina Pyatkin' 'Jacob Morrison' 'LJ Miranda'\n 'Bill Yuchen Lin' 'Khyathi Chandu' 'Nouha Dziri' 'Sachin Kumar'\n 'Tom Zick' 'Yejin Choi' 'Noah A. Smith' 'Hannaneh Hajishirzi']"
] |
null | null |
2403.13793
| null | null |
http://arxiv.org/pdf/2403.13793v2
|
2024-04-05T12:26:11Z
|
2024-03-20T17:54:26Z
|
Evaluating Frontier Models for Dangerous Capabilities
|
To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evaluations and pilot them on Gemini 1.0 models. Our evaluations cover four areas: (1) persuasion and deception; (2) cyber-security; (3) self-proliferation; and (4) self-reasoning. We do not find evidence of strong dangerous capabilities in the models we evaluated, but we flag early warning signs. Our goal is to help advance a rigorous science of dangerous capability evaluation, in preparation for future models.
|
[
"['Mary Phuong' 'Matthew Aitchison' 'Elliot Catt' 'Sarah Cogan'\n 'Alexandre Kaskasoli' 'Victoria Krakovna' 'David Lindner' 'Matthew Rahtz'\n 'Yannis Assael' 'Sarah Hodkinson' 'Heidi Howard' 'Tom Lieberum'\n 'Ramana Kumar' 'Maria Abi Raad' 'Albert Webson' 'Lewis Ho' 'Sharon Lin'\n 'Sebastian Farquhar' 'Marcus Hutter' 'Gregoire Deletang' 'Anian Ruoss'\n 'Seliem El-Sayed' 'Sasha Brown' 'Anca Dragan' 'Rohin Shah' 'Allan Dafoe'\n 'Toby Shevlane']"
] |
null | null |
2403.13795
| null | null |
http://arxiv.org/abs/2403.13795v2
|
2024-03-21T08:14:36Z
|
2023-11-22T14:18:30Z
|
PyVRP: a high-performance VRP solver package
|
We introduce PyVRP, a Python package that implements hybrid genetic search in a state-of-the-art vehicle routing problem (VRP) solver. The package is designed for the VRP with time windows (VRPTW), but can be easily extended to support other VRP variants. PyVRP combines the flexibility of Python with the performance of C++, by implementing (only) performance critical parts of the algorithm in C++, while being fully customisable at the Python level. PyVRP is a polished implementation of the algorithm that ranked 1st in the 2021 DIMACS VRPTW challenge and, after improvements, ranked 1st on the static variant of the EURO meets NeurIPS 2022 vehicle routing competition. The code follows good software engineering practices, and is well-documented and unit tested. PyVRP is freely available under the liberal MIT license. Through numerical experiments we show that PyVRP achieves state-of-the-art results on the VRPTW and capacitated VRP. We hope that PyVRP enables researchers and practitioners to easily and quickly build on a state-of-the-art VRP solver.
|
[
"['Niels A. Wouda' 'Leon Lan' 'Wouter Kool']"
] |
null | null |
2403.13797
| null | null |
http://arxiv.org/pdf/2403.13797v1
|
2024-03-20T17:54:58Z
|
2024-03-20T17:54:58Z
|
Bridge the Modality and Capacity Gaps in Vision-Language Model Selection
|
Vision Language Models (VLMs) excel in zero-shot image classification by pairing images with textual category names. The expanding variety of Pre-Trained VLMs enhances the likelihood of identifying a suitable VLM for specific tasks. Thus, a promising zero-shot image classification strategy is selecting the most appropriate Pre-Trained VLM from the VLM Zoo, relying solely on the text data of the target dataset without access to the dataset's images. In this paper, we analyze two inherent challenges in assessing the ability of a VLM in this Language-Only VLM selection: the "Modality Gap" -- the disparity in VLM's embeddings across two different modalities, making text a less reliable substitute for images; and the "Capability Gap" -- the discrepancy between the VLM's overall ranking and its ranking for target dataset, hindering direct prediction of a model's dataset-specific performance from its general performance. We propose VLM Selection With gAp Bridging (SWAB) to mitigate the negative impact of these two gaps. SWAB first adopts optimal transport to capture the relevance between open-source datasets and target dataset with a transportation matrix. It then uses this matrix to transfer useful statistics of VLMs from open-source datasets to the target dataset for bridging those two gaps and enhancing the VLM's capacity estimation for VLM selection. Experiments across various VLMs and image classification datasets validate SWAB's effectiveness.
|
[
"['Chao Yi' 'De-Chuan Zhan' 'Han-Jia Ye']"
] |
null | null |
2403.13798
| null | null |
http://arxiv.org/pdf/2403.13798v2
|
2024-05-24T17:44:11Z
|
2024-03-20T17:55:21Z
|
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable
Action Quality Assessment
|
Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://github.com/laurenok24/NSAQA.
|
[
"['Lauren Okamoto' 'Paritosh Parmar']"
] |
null | null |
2403.13802
| null | null |
http://arxiv.org/pdf/2403.13802v2
|
2024-04-01T17:58:02Z
|
2024-03-20T17:59:14Z
|
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
|
The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the scan scheme of Mamba. Secondly, building upon this insight, we introduce a simple, plug-and-play, zero-parameter method named Zigzag Mamba, which outperforms Mamba-based baselines and demonstrates improved speed and memory utilization compared to transformer-based baselines. Lastly, we integrate Zigzag Mamba with the Stochastic Interpolant framework to investigate the scalability of the model on large-resolution visual datasets, such as FacesHQ $1024times 1024$ and UCF101, MultiModal-CelebA-HQ, and MS COCO $256times 256$ . Code will be released at https://taohu.me/zigma/
|
[
"['Vincent Tao Hu' 'Stefan Andreas Baumann' 'Ming Gui' 'Olga Grebenkova'\n 'Pingchuan Ma' 'Johannes Fischer' 'Björn Ommer']"
] |
null | null |
2403.13804
| null | null |
http://arxiv.org/pdf/2403.13804v1
|
2024-03-20T17:59:43Z
|
2024-03-20T17:59:43Z
|
Learning from Models and Data for Visual Grounding
|
We introduce SynGround, a novel framework that combines data-driven learning and knowledge transfer from various large-scale pretrained models to enhance the visual grounding capabilities of a pretrained vision-and-language model. The knowledge transfer from the models initiates the generation of image descriptions through an image description generator. These descriptions serve dual purposes: they act as prompts for synthesizing images through a text-to-image generator, and as queries for synthesizing text, from which phrases are extracted using a large language model. Finally, we leverage an open-vocabulary object detector to generate synthetic bounding boxes for the synthetic images and texts. We finetune a pretrained vision-and-language model on this dataset by optimizing a mask-attention consistency objective that aligns region annotations with gradient-based model explanations. The resulting model improves the grounding capabilities of an off-the-shelf vision-and-language model. Particularly, SynGround improves the pointing game accuracy of ALBEF on the Flickr30k dataset from 79.38% to 87.26%, and on RefCOCO+ Test A from 69.35% to 79.06% and on RefCOCO+ Test B from 53.77% to 63.67%.
|
[
"['Ruozhen He' 'Paola Cascante-Bonilla' 'Ziyan Yang' 'Alexander C. Berg'\n 'Vicente Ordonez']"
] |
null | null |
2403.13805
| null | null |
http://arxiv.org/pdf/2403.13805v1
|
2024-03-20T17:59:55Z
|
2024-03-20T17:59:55Z
|
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
|
CLIP (Contrastive Language-Image Pre-training) uses contrastive learning from noise image-text pairs to excel at recognizing a wide array of candidates, yet its focus on broad associations hinders the precision in distinguishing subtle differences among fine-grained items. Conversely, Multimodal Large Language Models (MLLMs) excel at classifying fine-grained categories, thanks to their substantial knowledge from pre-training on web-level corpora. However, the performance of MLLMs declines with an increase in category numbers, primarily due to growing complexity and constraints of limited context window size. To synergize the strengths of both approaches and enhance the few-shot/zero-shot recognition abilities for datasets characterized by extensive and fine-grained vocabularies, this paper introduces RAR, a Retrieving And Ranking augmented method for MLLMs. We initially establish a multi-modal retriever based on CLIP to create and store explicit memory for different categories beyond the immediate context window. During inference, RAR retrieves the top-k similar results from the memory and uses MLLMs to rank and make the final predictions. Our proposed approach not only addresses the inherent limitations in fine-grained recognition but also preserves the model's comprehensive knowledge base, significantly boosting accuracy across a range of vision-language recognition tasks. Notably, our approach demonstrates a significant improvement in performance on 5 fine-grained visual recognition benchmarks, 11 few-shot image recognition datasets, and the 2 object detection datasets under the zero-shot recognition setting.
|
[
"['Ziyu Liu' 'Zeyi Sun' 'Yuhang Zang' 'Wei Li' 'Pan Zhang' 'Xiaoyi Dong'\n 'Yuanjun Xiong' 'Dahua Lin' 'Jiaqi Wang']"
] |
null | null |
2403.13807
| null | null |
http://arxiv.org/pdf/2403.13807v1
|
2024-03-20T17:59:57Z
|
2024-03-20T17:59:57Z
|
Editing Massive Concepts in Text-to-Image Diffusion Models
|
Text-to-image diffusion models suffer from the risk of generating outdated, copyrighted, incorrect, and biased content. While previous methods have mitigated the issues on a small scale, it is essential to handle them simultaneously in larger-scale real-world scenarios. We propose a two-stage method, Editing Massive Concepts In Diffusion Models (EMCID). The first stage performs memory optimization for each individual concept with dual self-distillation from text alignment loss and diffusion noise prediction loss. The second stage conducts massive concept editing with multi-layer, closed form model editing. We further propose a comprehensive benchmark, named ImageNet Concept Editing Benchmark (ICEB), for evaluating massive concept editing for T2I models with two subtasks, free-form prompts, massive concept categories, and extensive evaluation metrics. Extensive experiments conducted on our proposed benchmark and previous benchmarks demonstrate the superior scalability of EMCID for editing up to 1,000 concepts, providing a practical approach for fast adjustment and re-deployment of T2I diffusion models in real-world applications.
|
[
"['Tianwei Xiong' 'Yue Wu' 'Enze Xie' 'Yue Wu' 'Zhenguo Li' 'Xihui Liu']"
] |
null | null |
2403.13808
| null | null |
http://arxiv.org/pdf/2403.13808v2
|
2024-04-05T18:22:02Z
|
2024-03-20T17:59:58Z
|
On Pretraining Data Diversity for Self-Supervised Learning
|
We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downstream data is minimal. Notably, even with an exceptionally large pretraining data diversity achieved through methods like web crawling or diffusion-generated data, among other ways, the distribution shift remains a challenge. Our experiments are comprehensive with seven SSL methods using large-scale datasets such as ImageNet and YFCC100M amounting to over 200 GPU days. Code and trained models will be available at https://github.com/hammoudhasan/DiversitySSL .
|
[
"['Hasan Abed Al Kader Hammoud' 'Tuhin Das' 'Fabio Pizzati' 'Philip Torr'\n 'Adel Bibi' 'Bernard Ghanem']"
] |
null | null |
2403.13812
| null | null |
http://arxiv.org/pdf/2403.13812v1
|
2024-02-09T17:20:48Z
|
2024-02-09T17:20:48Z
|
Quantitative Analysis of AI-Generated Texts in Academic Research: A
Study of AI Presence in Arxiv Submissions using AI Detection Tool
|
Many people are interested in ChatGPT since it has become a prominent AIGC model that provides high-quality responses in various contexts, such as software development and maintenance. Misuse of ChatGPT might cause significant issues, particularly in public safety and education, despite its immense potential. The majority of researchers choose to publish their work on Arxiv. The effectiveness and originality of future work depend on the ability to detect AI components in such contributions. To address this need, this study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv. For this study, a dataset was created using physics, mathematics, and computer science articles. Using the newly built dataset, the following step is to put originality.ai through its paces. The statistical analysis shows that Originality.ai is very accurate, with a rate of 98%.
|
[
"['Arslan Akram']"
] |
null | null |
2403.13815
| null | null |
http://arxiv.org/abs/2403.13815v1
|
2024-02-16T13:41:05Z
|
2024-02-16T13:41:05Z
|
Autonomous microARPES
|
Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in X-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming scanning process to cover not only a three-dimensional energy-momentum ($E, k_z, k_y$) space but also the two-dimensional surface area. Here, we implement a protocol to autonomously search both $mathbf{k}$- and real space in order to find positions of particular interest, either because of their high photoemission intensity or because of sharp spectral features. The search is based on the use of Gaussian process regression and can easily be expanded to include additional parameters or optimisation criteria. This autonomous experimental control is implemented on the SGM4 micro-focus beamline of the synchrotron radiation source ASTRID2.
|
[
"['Steinn Ymir Agustsson' 'Alfred J. H. Jones' 'Davide Curcio'\n 'Søren Ulstrup' 'Jill Miwa' 'Davide Mottin' 'Panagiotis Karras'\n 'Philip Hofmann']"
] |
null | null |
2403.13819
| null | null |
http://arxiv.org/pdf/2403.13819v1
|
2024-02-29T10:05:37Z
|
2024-02-29T10:05:37Z
|
A machine learning approach to predict university enrolment choices
through students' high school background in Italy
|
This paper explores the influence of Italian high school students' proficiency in mathematics and the Italian language on their university enrolment choices, specifically focusing on STEM (Science, Technology, Engineering, and Mathematics) courses. We distinguish between students from scientific and humanistic backgrounds in high school, providing valuable insights into their enrolment preferences. Furthermore, we investigate potential gender differences in response to similar previous educational choices and achievements. The study employs gradient boosting methodology, known for its high predicting performance and ability to capture non-linear relationships within data, and adjusts for variables related to the socio-demographic characteristics of the students and their previous educational achievements. Our analysis reveals significant differences in the enrolment choices based on previous high school achievements. The findings shed light on the complex interplay of academic proficiency, gender, and high school background in shaping students' choices regarding university education, with implications for educational policy and future research endeavours.
|
[
"['Andrea Priulla' 'Alessandro Albano' \"Nicoletta D'Angelo\"\n 'Massimo Attanasio']"
] |
null | null |
2403.13820
| null | null |
http://arxiv.org/pdf/2403.13820v1
|
2024-03-02T17:18:40Z
|
2024-03-02T17:18:40Z
|
Identity information based on human magnetocardiography signals
|
We have developed an individual identification system based on magnetocardiography (MCG) signals captured using optically pumped magnetometers (OPMs). Our system utilizes pattern recognition to analyze the signals obtained at different positions on the body, by scanning the matrices composed of MCG signals with a 2*2 window. In order to make use of the spatial information of MCG signals, we transform the signals from adjacent small areas into four channels of a dataset. We further transform the data into time-frequency matrices using wavelet transforms and employ a convolutional neural network (CNN) for classification. As a result, our system achieves an accuracy rate of 97.04% in identifying individuals. This finding indicates that the MCG signal holds potential for use in individual identification systems, offering a valuable tool for personalized healthcare management.
|
[
"['Pengju Zhang' 'Chenxi Sun' 'Jianwei Zhang' 'Hong Guo']"
] |
null | null |
2403.13821
| null | null |
http://arxiv.org/pdf/2403.13821v1
|
2024-03-04T03:09:25Z
|
2024-03-04T03:09:25Z
|
Offensive Lineup Analysis in Basketball with Clustering Players Based on
Shooting Style and Offensive Role
|
In a basketball game, scoring efficiency holds significant importance due to the numerous offensive possessions per game. Enhancing scoring efficiency necessitates effective collaboration among players with diverse playing styles. In previous studies, basketball lineups have been analyzed, but their playing style compatibility has not been quantitatively examined. The purpose of this study is to analyze more specifically the impact of playing style compatibility on scoring efficiency, focusing only on offense. This study employs two methods to capture the playing styles of players on offense: shooting style clustering using tracking data, and offensive role clustering based on annotated playtypes and advanced statistics. For the former, interpretable hand-crafted shot features and Wasserstein distances between shooting style distributions were utilized. For the latter, soft clustering was applied to playtype data for the first time. Subsequently, based on the lineup information derived from these two clusterings, machine learning models Bayesian models that predict statistics representing scoring efficiency were trained and interpreted. These approaches provide insights into which combinations of five players tend to be effective and which combinations of two players tend to produce good effects.
|
[
"['Kazuhiro Yamada' 'Keisuke Fujii']"
] |
null | null |
2403.13825
| null | null |
http://arxiv.org/pdf/2403.13825v1
|
2024-03-05T23:12:47Z
|
2024-03-05T23:12:47Z
|
Deep Generative Models for Ultra-High Granularity Particle Physics
Detector Simulation: A Voyage From Emulation to Extrapolation
|
Simulating ultra-high-granularity detector responses in Particle Physics represents a critical yet computationally demanding task. This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment, which features over 7.5M pixel channels-the highest spatial resolution detector simulation dataset ever analysed with generative models. This thesis starts off by a comprehensive and taxonomic review on generative models for simulating detector signatures. Then, it presents the Intra-Event Aware Generative Adversarial Network (IEA-GAN), a new geometry-aware generative model that introduces a relational attentive reasoning and Self-Supervised Learning to approximate an "event" in the detector. This study underscores the importance of intra-event correlation for downstream physics analyses. Building upon this, the work drifts towards a more generic approach and presents YonedaVAE, a Category Theory-inspired generative model that tackles the open problem of Out-of-Distribution (OOD) simulation. YonedaVAE introduces a learnable Yoneda embedding to capture the entirety of an event based on its sensor relationships, formulating a Category theoretical language for intra-event relational reasoning. This is complemented by introducing a Self-Supervised learnable prior for VAEs and an Adaptive Top-q sampling mechanism, enabling the model to sample point clouds with variable intra-category cardinality in a zero-shot manner. Variable Intra-event cardinality has not been approached before and is vital for simulating irregular detector geometries. Trained on an early experiment data, YonedaVAE can reach a reasonable OOD simulation precision of a later experiment with almost double luminosity. This study introduces, for the first time, the results of using deep generative models for ultra-high granularity detector simulation in Particle Physics.
|
[
"['Baran Hashemi']"
] |
null | null |
2403.13826
| null | null |
http://arxiv.org/pdf/2403.13826v1
|
2024-03-06T01:55:14Z
|
2024-03-06T01:55:14Z
|
Measuring Diversity in Co-creative Image Generation
|
Quality and diversity have been proposed as reasonable heuristics for assessing content generated by co-creative systems, but to date there has been little agreement around what constitutes the latter or how to measure it. Proposed approaches for assessing generative models in terms of diversity have limitations in that they compare the model's outputs to a ground truth that in the era of large pre-trained generative models might not be available, or entail an impractical number of computations. We propose an alternative based on entropy of neural network encodings for comparing diversity between sets of images that does not require ground-truth knowledge and is easy to compute. We also compare two pre-trained networks and show how the choice relates to the notion of diversity that we want to evaluate. We conclude with a discussion of the potential applications of these measures for ideation in interactive systems, model evaluation, and more broadly within computational creativity.
|
[
"['Francisco Ibarrola' 'Kazjon Grace']"
] |
null | null |
2403.13827
| null | null |
http://arxiv.org/pdf/2403.13827v1
|
2024-03-06T09:58:35Z
|
2024-03-06T09:58:35Z
|
Self-Supervised Path Planning in UAV-aided Wireless Networks based on
Active Inference
|
This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world model to understand the environment and implicitly discover the optimizer's policy. UAV equipped with the world model can make real-time autonomous decisions and engage in online planning using active inference. During planning, UAV can score different policies based on the expected surprise, allowing it to choose among alternative futures. Additionally, UAV can anticipate the outcomes of its actions using the world model and assess the expected surprise in a self-supervised manner. Our method enables quicker adaptation to new situations and better performance than traditional RL, leading to broader generalizability.
|
[
"['Ali Krayani' 'Khalid Khan' 'Lucio Marcenaro' 'Mario Marchese'\n 'Carlo Regazzoni']"
] |
null | null |
2403.13829
| null | null |
http://arxiv.org/pdf/2403.13829v1
|
2024-03-07T02:53:40Z
|
2024-03-07T02:53:40Z
|
DecompOpt: Controllable and Decomposed Diffusion Models for
Structure-based Molecular Optimization
|
Recently, 3D generative models have shown promising performances in structure-based drug design by learning to generate ligands given target binding sites. However, only modeling the target-ligand distribution can hardly fulfill one of the main goals in drug discovery -- designing novel ligands with desired properties, e.g., high binding affinity, easily synthesizable, etc. This challenge becomes particularly pronounced when the target-ligand pairs used for training do not align with these desired properties. Moreover, most existing methods aim at solving textit{de novo} design task, while many generative scenarios requiring flexible controllability, such as R-group optimization and scaffold hopping, have received little attention. In this work, we propose DecompOpt, a structure-based molecular optimization method based on a controllable and decomposed diffusion model. DecompOpt presents a new generation paradigm which combines optimization with conditional diffusion models to achieve desired properties while adhering to the molecular grammar. Additionally, DecompOpt offers a unified framework covering both textit{de novo} design and controllable generation. To achieve so, ligands are decomposed into substructures which allows fine-grained control and local optimization. Experiments show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines, and demonstrate great potential in controllable generation tasks.
|
[
"['Xiangxin Zhou' 'Xiwei Cheng' 'Yuwei Yang' 'Yu Bao' 'Liang Wang'\n 'Quanquan Gu']"
] |
null | null |
2403.13830
| null | null |
http://arxiv.org/pdf/2403.13830v1
|
2024-03-07T03:03:13Z
|
2024-03-07T03:03:13Z
|
Bridging Text and Molecule: A Survey on Multimodal Frameworks for
Molecule
|
Artificial intelligence has demonstrated immense potential in scientific research. Within molecular science, it is revolutionizing the traditional computer-aided paradigm, ushering in a new era of deep learning. With recent progress in multimodal learning and natural language processing, an emerging trend has targeted at building multimodal frameworks to jointly model molecules with textual domain knowledge. In this paper, we present the first systematic survey on multimodal frameworks for molecules research. Specifically,we begin with the development of molecular deep learning and point out the necessity to involve textual modality. Next, we focus on recent advances in text-molecule alignment methods, categorizing current models into two groups based on their architectures and listing relevant pre-training tasks. Furthermore, we delves into the utilization of large language models and prompting techniques for molecular tasks and present significant applications in drug discovery. Finally, we discuss the limitations in this field and highlight several promising directions for future research.
|
[
"['Yi Xiao' 'Xiangxin Zhou' 'Qiang Liu' 'Liang Wang']"
] |
null | null |
2403.13833
| null | null |
http://arxiv.org/abs/2403.13833v1
|
2024-03-08T01:01:24Z
|
2024-03-08T01:01:24Z
|
Linearly Constrained Weights: Reducing Activation Shift for Faster
Training of Neural Networks
|
In this paper, we first identify activation shift, a simple but remarkable phenomenon in a neural network in which the preactivation value of a neuron has non-zero mean that depends on the angle between the weight vector of the neuron and the mean of the activation vector in the previous layer. We then propose linearly constrained weights (LCW) to reduce the activation shift in both fully connected and convolutional layers. The impact of reducing the activation shift in a neural network is studied from the perspective of how the variance of variables in the network changes through layer operations in both forward and backward chains. We also discuss its relationship to the vanishing gradient problem. Experimental results show that LCW enables a deep feedforward network with sigmoid activation functions to be trained efficiently by resolving the vanishing gradient problem. Moreover, combined with batch normalization, LCW improves generalization performance of both feedforward and convolutional networks.
|
[
"['Takuro Kutsuna']"
] |
null | null |
2403.13834
| null | null |
http://arxiv.org/pdf/2403.13834v1
|
2024-03-10T00:43:36Z
|
2024-03-10T00:43:36Z
|
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and
Prospects
|
Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both academia and industry because prevailing studies on heterogeneous graphs often suffer from label sparsity. FLHG aims to tackle the performance degradation in the face of limited annotated data and there have been numerous recent studies proposing various methods and applications. In this paper, we provide a comprehensive review of existing FLHG methods, covering challenges, research progress, and future prospects. Specifically, we first formalize FLHG and categorize its methods into three types: single-heterogeneity FLHG, dual-heterogeneity FLHG, and multi-heterogeneity FLHG. Then, we analyze the research progress within each category, highlighting the most recent and representative developments. Finally, we identify and discuss promising directions for future research in FLHG. To the best of our knowledge, this paper is the first systematic and comprehensive review of FLHG.
|
[
"['Pengfei Ding' 'Yan Wang' 'Guanfeng Liu']"
] |
null | null |
2403.13835
| null | null |
http://arxiv.org/pdf/2403.13835v1
|
2024-03-11T17:45:47Z
|
2024-03-11T17:45:47Z
|
SMART: Automatically Scaling Down Language Models with Accuracy
Guarantees for Reduced Processing Fees
|
The advancement of Large Language Models (LLMs) has significantly boosted performance in natural language processing (NLP) tasks. However, the deployment of high-performance LLMs incurs substantial costs, primarily due to the increased number of parameters aimed at enhancing model performance. This has made the use of state-of-the-art LLMs more expensive for end-users. AI service providers, such as OpenAI and Anthropic, often offer multiple versions of LLMs with varying prices and performance. However, end-users still face challenges in choosing the appropriate LLM for their tasks that balance result quality with cost. We introduce SMART, Scaling Models Adaptively for Reduced Token Fees, a novel LLM framework designed to minimize the inference costs of NLP tasks while ensuring sufficient result quality. It enables users to specify an accuracy constraint in terms of the equivalence of outputs to those of the most powerful LLM. SMART then generates results that deviate from the outputs of this LLM only with a probability below a user-defined threshold. SMART employs a profiling phase that evaluates the performance of multiple LLMs to identify those that meet the user-defined accuracy level. SMART optimizes the tradeoff between profiling overheads and the anticipated cost savings resulting from profiling. Moreover, our approach significantly reduces inference costs by strategically leveraging a mix of LLMs. Our experiments on three real-world datasets show that, based on OpenAI models, SMART achieves significant cost savings, up to 25.6x in comparison to GPT-4.
|
[
"['Saehan Jo' 'Immanuel Trummer']"
] |
null | null |
2403.13836
| null | null |
http://arxiv.org/pdf/2403.13836v1
|
2024-03-12T01:16:29Z
|
2024-03-12T01:16:29Z
|
Tree-based Learning for High-Fidelity Prediction of Chaos
|
Model-free forecasting of the temporal evolution of chaotic systems is crucial but challenging. Existing solutions require hyperparameter tuning, significantly hindering their wider adoption. In this work, we introduce a tree-based approach not requiring hyperparameter tuning: TreeDOX. It uses time delay overembedding as explicit short-term memory and Extra-Trees Regressors to perform feature reduction and forecasting. We demonstrate the state-of-the-art performance of TreeDOX using the Henon map, Lorenz and Kuramoto-Sivashinsky systems, and the real-world Southern Oscillation Index.
|
[
"['Adam Giammarese' 'Kamal Rana' 'Erik M. Bollt' 'Nishant Malik']"
] |
null | null |
2403.13838
| null | null |
http://arxiv.org/pdf/2403.13838v1
|
2024-03-14T03:24:14Z
|
2024-03-14T03:24:14Z
|
Circuit Transformer: End-to-end Circuit Design by Predicting the Next
Gate
|
Language, a prominent human ability to express through sequential symbols, has been computationally mastered by recent advances of large language models (LLMs). By predicting the next word recurrently with huge neural models, LLMs have shown unprecedented capabilities in understanding and reasoning. Circuit, as the "language" of electronic design, specifies the functionality of an electronic device by cascade connections of logic gates. Then, can circuits also be mastered by a a sufficiently large "circuit model", which can conquer electronic design tasks by simply predicting the next logic gate? In this work, we take the first step to explore such possibilities. Two primary barriers impede the straightforward application of LLMs to circuits: their complex, non-sequential structure, and the intolerance of hallucination due to strict constraints (e.g., equivalence). For the first barrier, we encode a circuit as a memory-less, depth-first traversal trajectory, which allows Transformer-based neural models to better leverage its structural information, and predict the next gate on the trajectory as a circuit model. For the second barrier, we introduce an equivalence-preserving decoding process, which ensures that every token in the generated trajectory adheres to the specified equivalence constraints. Moreover, the circuit model can also be regarded as a stochastic policy to tackle optimization-oriented circuit design tasks. Experimentally, we trained a Transformer-based model of 88M parameters, named "Circuit Transformer", which demonstrates impressive performance in end-to-end logic synthesis. With Monte-Carlo tree search, Circuit Transformer significantly improves over resyn2 while retaining strict equivalence, showcasing the potential of generative AI in conquering electronic design challenges.
|
[
"['Xihan Li' 'Xing Li' 'Lei Chen' 'Xing Zhang' 'Mingxuan Yuan' 'Jun Wang']"
] |
null | null |
2403.13839
| null | null |
http://arxiv.org/pdf/2403.13839v1
|
2024-03-14T16:17:14Z
|
2024-03-14T16:17:14Z
|
depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning
Researchers
|
PyTorch texttt{2.x} introduces a compiler designed to accelerate deep learning programs. However, for machine learning researchers, adapting to the PyTorch compiler to full potential can be challenging. The compiler operates at the Python bytecode level, making it appear as an opaque box. To address this, we introduce texttt{depyf}, a tool designed to demystify the inner workings of the PyTorch compiler. texttt{depyf} decompiles bytecode generated by PyTorch back into equivalent source code, and establishes connections between in-memory code objects and their on-disk source code counterparts. This feature enables users to step through the source code line by line using debuggers, thus enhancing their understanding of the underlying processes. Notably, texttt{depyf} is non-intrusive and user-friendly, primarily relying on two convenient context managers for its core functionality. The project is href{https://github.com/thuml/depyf}{ openly available} and is recognized as a href{https://pytorch.org/ecosystem/}{PyTorch ecosystem project}.
|
[
"['Kaichao You' 'Runsheng Bai' 'Meng Cao' 'Jianmin Wang' 'Ion Stoica'\n 'Mingsheng Long']"
] |
null | null |
2403.13841
| null | null |
http://arxiv.org/pdf/2403.13841v2
|
2024-03-23T18:27:49Z
|
2024-03-16T17:24:38Z
|
Integrating Wearable Sensor Data and Self-reported Diaries for
Personalized Affect Forecasting
|
Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies are primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These studies typically focus on objective sensory measures, often neglecting other forms of self-reported information like diaries and notes. In this paper, we propose a multimodal deep learning model for affect status forecasting. This model combines a transformer encoder with a pre-trained language model, facilitating the integrated analysis of objective metrics and self-reported diaries. To validate our model, we conduct a longitudinal study, enrolling college students and monitoring them over a year, to collect an extensive dataset including physiological, environmental, sleep, metabolic, and physical activity parameters, alongside open-ended textual diaries provided by the participants. Our results demonstrate that the proposed model achieves predictive accuracy of 82.50% for positive affect and 82.76% for negative affect, a full week in advance. The effectiveness of our model is further elevated by its explainability.
|
[
"['Zhongqi Yang' 'Yuning Wang' 'Ken S. Yamashita' 'Maryam Sabah'\n 'Elahe Khatibi' 'Iman Azimi' 'Nikil Dutt' 'Jessica L. Borelli'\n 'Amir M. Rahmani']"
] |
null | null |
2403.13842
| null | null |
http://arxiv.org/pdf/2403.13842v1
|
2024-03-17T11:48:40Z
|
2024-03-17T11:48:40Z
|
Analyzing the Variations in Emergency Department Boarding and Testing
the Transferability of Forecasting Models across COVID-19 Pandemic Waves in
Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level
socioecological risk
|
Emergency department's (ED) boarding (defined as ED waiting time greater than four hours) has been linked to poor patient outcomes and health system performance. Yet, effective forecasting models is rare before COVID-19, lacking during the peri-COVID era. Here, a hybrid convolutional neural network (CNN)-Long short-term memory (LSTM) model was applied to public-domain data sourced from Hong Kong's Hospital Authority, Department of Health, and Housing Authority. In addition, we sought to identify the phase of the COVID-19 pandemic that most significantly perturbed our complex adaptive healthcare system, thereby revealing a stable pattern of interconnectedness among its components, using deep transfer learning methodology. Our result shows that 1) the greatest proportion of days with ED boarding was found between waves four and five; 2) the best-performing model for forecasting ED boarding was observed between waves four and five, which was based on features representing time-invariant residential buildings' built environment and sociodemographic profiles and the historical time series of ED boarding and case counts, compared to during the waves when best-performing forecasting is based on time-series features alone; and 3) when the model built from the period between waves four and five was applied to data from other waves via deep transfer learning, the transferred model enhanced the performance of indigenous models.
|
[
"['Eman Leung' 'Jingjing Guan' 'Kin On Kwok' 'CT Hung' 'CC. Ching'\n 'CK. Chung' 'Hector Tsang' 'EK Yeoh' 'Albert Lee']"
] |
null | null |
2403.13843
| null | null |
http://arxiv.org/pdf/2403.13843v1
|
2024-03-17T17:45:04Z
|
2024-03-17T17:45:04Z
|
Machine Learning and Vision Transformers for Thyroid Carcinoma
Diagnosis: A review
|
The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AIbased approaches, especially those employing transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artifcial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of transformers in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research feld.
|
[
"['Yassine Habchi' 'Hamza Kheddar' 'Yassine Himeur' 'Abdelkrim Boukabou'\n 'Ammar Chouchane' 'Abdelmalik Ouamane' 'Shadi Atalla' 'Wathiq Mansoor']"
] |
null | null |
2403.13844
| null | null |
http://arxiv.org/pdf/2403.13844v1
|
2024-03-18T01:06:29Z
|
2024-03-18T01:06:29Z
|
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic
Architectures for Brain-Computer Interfaces
|
Brain-Computer interfaces (BCIs) are typically designed to be lightweight and responsive in real-time to provide users timely feedback. Classical feature engineering is computationally efficient but has low accuracy, whereas the recent neural networks (DNNs) improve accuracy but are computationally expensive and incur high latency. As a promising alternative, the low-dimensional computing (LDC) classifier based on vector symbolic architecture (VSA), achieves small model size yet higher accuracy than classical feature engineering methods. However, its accuracy still lags behind that of modern DNNs, making it challenging to process complex brain signals. To improve the accuracy of a small model, knowledge distillation is a popular method. However, maintaining a constant level of distillation between the teacher and student models may not be the best way for a growing student during its progressive learning stages. In this work, we propose a simple scheduled knowledge distillation method based on curriculum data order to enable the student to gradually build knowledge from the teacher model, controlled by an $alpha$ scheduler. Meanwhile, we employ the LDC/VSA as the student model to enhance the on-device inference efficiency for tiny BCI devices that demand low latency. The empirical results have demonstrated that our approach achieves better tradeoff between accuracy and hardware efficiency compared to other methods.
|
[
"['Yejia Liu' 'Shijin Duan' 'Xiaolin Xu' 'Shaolei Ren']"
] |
null | null |
2403.13845
| null | null |
http://arxiv.org/pdf/2403.13845v1
|
2024-03-18T02:50:42Z
|
2024-03-18T02:50:42Z
|
Learning to better see the unseen: Broad-Deep Mixed Anti-Forgetting
Framework for Incremental Zero-Shot Fault Diagnosis
|
Zero-shot fault diagnosis (ZSFD) is capable of identifying unseen faults via predicting fault attributes labeled by human experts. We first recognize the demand of ZSFD to deal with continuous changes in industrial processes, i.e., the model's ability to adapt to new fault categories and attributes while avoiding forgetting the diagnosis ability learned previously. To overcome the issue that the existing ZSFD paradigm cannot learn from evolving streams of training data in industrial scenarios, the incremental ZSFD (IZSFD) paradigm is proposed for the first time, which incorporates category increment and attribute increment for both traditional ZSFD and generalized ZSFD paradigms. To achieve IZSFD, we present a broad-deep mixed anti-forgetting framework (BDMAFF) that aims to learn from new fault categories and attributes. To tackle the issue of forgetting, BDMAFF effectively accumulates previously acquired knowledge from two perspectives: features and attribute prototypes. The feature memory is established through a deep generative model that employs anti-forgetting training strategies, ensuring the generation quality of historical categories is supervised and maintained. The diagnosis model SEEs the UNSEEN faults with the help of generated samples from the generative model. The attribute prototype memory is established through a diagnosis model inspired by the broad learning system. Unlike traditional incremental learning algorithms, BDMAFF introduces a memory-driven iterative update strategy for the diagnosis model, which allows the model to learn new faults and attributes without requiring the storage of all historical training samples. The effectiveness of the proposed method is verified by a real hydraulic system and the Tennessee-Eastman benchmark process.
|
[
"['Jiancheng Zhao' 'Jiaqi Yue' 'Chunhui Zhao']"
] |
null | null |
2403.13846
| null | null |
http://arxiv.org/pdf/2403.13846v2
|
2024-04-18T12:22:12Z
|
2024-03-18T05:18:19Z
|
A Clustering Method with Graph Maximum Decoding Information
|
The clustering method based on graph models has garnered increased attention for its widespread applicability across various knowledge domains. Its adaptability to integrate seamlessly with other relevant applications endows the graph model-based clustering analysis with the ability to robustly extract "natural associations" or "graph structures" within datasets, facilitating the modelling of relationships between data points. Despite its efficacy, the current clustering method utilizing the graph-based model overlooks the uncertainty associated with random walk access between nodes and the embedded structural information in the data. To address this gap, we present a novel Clustering method for Maximizing Decoding Information within graph-based models, named CMDI. CMDI innovatively incorporates two-dimensional structural information theory into the clustering process, consisting of two phases: graph structure extraction and graph vertex partitioning. Within CMDI, graph partitioning is reformulated as an abstract clustering problem, leveraging maximum decoding information to minimize uncertainty associated with random visits to vertices. Empirical evaluations on three real-world datasets demonstrate that CMDI outperforms classical baseline methods, exhibiting a superior decoding information ratio (DI-R). Furthermore, CMDI showcases heightened efficiency, particularly when considering prior knowledge (PK). These findings underscore the effectiveness of CMDI in enhancing decoding information quality and computational efficiency, positioning it as a valuable tool in graph-based clustering analyses.
|
[
"['Xinrun Xu' 'Manying Lv' 'Zhanbiao Lian' 'Yurong Wu' 'Jin Yan'\n 'Shan Jiang' 'Zhiming Ding']"
] |
null | null |
2403.13847
| null | null |
http://arxiv.org/pdf/2403.13847v1
|
2024-03-18T09:32:33Z
|
2024-03-18T09:32:33Z
|
Optimal Transport for Domain Adaptation through Gaussian Mixture Models
|
In this paper we explore domain adaptation through optimal transport. We propose a novel approach, where we model the data distributions through Gaussian mixture models. This strategy allows us to solve continuous optimal transport through an equivalent discrete problem. The optimal transport solution gives us a matching between source and target domain mixture components. From this matching, we can map data points between domains, or transfer the labels from the source domain components towards the target domain. We experiment with 2 domain adaptation benchmarks in fault diagnosis, showing that our methods have state-of-the-art performance.
|
[
"['Eduardo Fernandes Montesuma' 'Fred Maurice Ngolè Mboula'\n 'Antoine Souloumiac']"
] |
null | null |
2403.13848
| null | null |
http://arxiv.org/pdf/2403.13848v1
|
2024-03-18T10:44:22Z
|
2024-03-18T10:44:22Z
|
Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule
Lists
|
Differentially-private (DP) mechanisms can be embedded into the design of a machine learningalgorithm to protect the resulting model against privacy leakage, although this often comes with asignificant loss of accuracy. In this paper, we aim at improving this trade-off for rule lists modelsby establishing the smooth sensitivity of the Gini impurity and leveraging it to propose a DP greedyrule list algorithm. In particular, our theoretical analysis and experimental results demonstrate thatthe DP rule lists models integrating smooth sensitivity have higher accuracy that those using otherDP frameworks based on global sensitivity.
|
[
"['Timothée Ly' 'Julien Ferry' 'Marie-José Huguet' 'Sébastien Gambs'\n 'Ulrich Aivodji']"
] |
null | null |
2403.13849
| null | null |
http://arxiv.org/abs/2403.13849v1
|
2024-03-18T14:37:27Z
|
2024-03-18T14:37:27Z
|
Graphs Unveiled: Graph Neural Networks and Graph Generation
|
One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. This paper represents a survey, providing a comprehensive overview of Graph Neural Networks (GNNs). We discuss the applications of graph neural networks across various domains. Finally, we present an advanced field in GNNs: graph generation.
|
[
"['László Kovács' 'Ali Jlidi']"
] |
null | null |
2403.13850
| null | null |
http://arxiv.org/pdf/2403.13850v1
|
2024-03-18T14:57:47Z
|
2024-03-18T14:57:47Z
|
Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and
Parameter Diffusion Guidance
|
This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid dynamics modeling in the field of earth sciences, aiming to achieve high-precision simulation and prediction of fluid dynamics through spatio-temporal physics awareness and parameter diffusion guidance. In the upstream stage, we design a vector quantization reconstruction module with temporal evolution characteristics, ensuring balanced and resilient parameter distribution by introducing general physical constraints. In the downstream stage, a diffusion probability network involving parameters is utilized to generate high-quality future states of fluids, while enhancing the model's generalization ability by perceiving parameters in various physical setups. Extensive experiments on multiple benchmark datasets have verified the effectiveness and robustness of the ST-PAD framework, which showcase that ST-PAD outperforms current mainstream models in fluid dynamics modeling and prediction, especially in effectively capturing local representations and maintaining significant advantages in OOD generations.
|
[
"['Hao Wu' 'Fan Xu' 'Yifan Duan' 'Ziwei Niu' 'Weiyan Wang' 'Gaofeng Lu'\n 'Kun Wang' 'Yuxuan Liang' 'Yang Wang']"
] |
null | null |
2403.13851
| null | null |
http://arxiv.org/pdf/2403.13851v1
|
2024-03-18T19:30:46Z
|
2024-03-18T19:30:46Z
|
Control of Medical Digital Twins with Artificial Neural Networks
|
The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.
|
[
"['Lucas Böttcher' 'Luis L. Fonseca' 'Reinhard C. Laubenbacher']"
] |
null | null |
2403.13858
| null | null |
http://arxiv.org/pdf/2403.13858v1
|
2024-03-19T22:05:17Z
|
2024-03-19T22:05:17Z
|
A conditional latent autoregressive recurrent model for generation and
forecasting of beam dynamics in particle accelerators
|
Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder (CVAE) transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory (LSTM) network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modules by sampling and decoding the latent space representation. The model also forecasts future states (downstream locations) of charged particles from past states (upstream locations). The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
|
[
"['Mahindra Rautela' 'Alan Williams' 'Alexander Scheinker']"
] |
null | null |
2403.13861
| null | null |
http://arxiv.org/pdf/2403.13861v1
|
2024-03-20T01:12:44Z
|
2024-03-20T01:12:44Z
|
Machine Learning-based Layer-wise Detection of Overheating Anomaly in
LPBF using Photodiode Data
|
Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics and thermal history. Hence, the proposed method offers a machine learning (ML) framework to utilize photodiode sensor data for layer-wise detection of overheating anomalies. In doing so, three sets of features are extracted from the raw photodiode data: MSMM (mean, standard deviation, median, maximum), MSQ (mean, standard deviation, quartiles), and MSD (mean, standard deviation, deciles). These three datasets are used to train several ML classifiers. Cost-sensitive learning is used to handle the class imbalance between the "anomalous" layers (affected by overheating) and "nominal" layers in the benchmark dataset. To boost detection accuracy, our proposed ML framework involves utilizing the majority voting ensemble (MVE) approach. The proposed method is demonstrated using a case study including an open benchmark dataset of photodiode measurements from an LPBF specimen with deliberate overheating anomalies at some layers. The results from the case study demonstrate that the MSD features yield the best performance for all classifiers, and the MVE classifier (with a mean F1-score of 0.8654) surpasses the individual ML classifiers. Moreover, our machine learning methodology achieves superior results (9.66% improvement in mean F1-score) in detecting layer-wise overheating anomalies, surpassing the existing methods in the literature that use the same benchmark dataset.
|
[
"['Nazmul Hasan' 'Apurba Kumar Saha' 'Andrew Wessman' 'Mohammed Shafae']"
] |
null | null |
2403.13863
| null | null |
http://arxiv.org/pdf/2403.13863v1
|
2024-03-20T08:45:31Z
|
2024-03-20T08:45:31Z
|
DiffImpute: Tabular Data Imputation With Denoising Diffusion
Probabilistic Model
|
Tabular data plays a crucial role in various domains but often suffers from missing values, thereby curtailing its potential utility. Traditional imputation techniques frequently yield suboptimal results and impose substantial computational burdens, leading to inaccuracies in subsequent modeling tasks. To address these challenges, we propose DiffImpute, a novel Denoising Diffusion Probabilistic Model (DDPM). Specifically, DiffImpute is trained on complete tabular datasets, ensuring that it can produce credible imputations for missing entries without undermining the authenticity of the existing data. Innovatively, it can be applied to various settings of Missing Completely At Random (MCAR) and Missing At Random (MAR). To effectively handle the tabular features in DDPM, we tailor four tabular denoising networks, spanning MLP, ResNet, Transformer, and U-Net. We also propose Harmonization to enhance coherence between observed and imputed data by infusing the data back and denoising them multiple times during the sampling stage. To enable efficient inference while maintaining imputation performance, we propose a refined non-Markovian sampling process that works along with Harmonization. Empirical evaluations on seven diverse datasets underscore the prowess of DiffImpute. Specifically, when paired with the Transformer as the denoising network, it consistently outperforms its competitors, boasting an average ranking of 1.7 and the most minimal standard deviation. In contrast, the next best method lags with a ranking of 2.8 and a standard deviation of 0.9. The code is available at https://github.com/Dendiiiii/DiffImpute.
|
[
"['Yizhu Wen' 'Kai Yi' 'Jing Ke' 'Yiqing Shen']"
] |
null | null |
2403.13864
| null | null |
http://arxiv.org/pdf/2403.13864v1
|
2024-03-20T09:23:20Z
|
2024-03-20T09:23:20Z
|
Optimal Transport for Fairness: Archival Data Repair using Small
Research Data Sets
|
With the advent of the AI Act and other regulations, there is now an urgent need for algorithms that repair unfairness in training data. In this paper, we define fairness in terms of conditional independence between protected attributes ($S$) and features ($X$), given unprotected attributes ($U$). We address the important setting in which torrents of archival data need to be repaired, using only a small proportion of these data, which are $S|U$-labelled (the research data). We use the latter to design optimal transport (OT)-based repair plans on interpolated supports. This allows {em off-sample}, labelled, archival data to be repaired, subject to stationarity assumptions. It also significantly reduces the size of the supports of the OT plans, with correspondingly large savings in the cost of their design and of their {em sequential/} application to the off-sample data. We provide detailed experimental results with simulated and benchmark real data (the Adult data set). Our performance figures demonstrate effective repair -- in the sense of quenching conditional dependence -- of large quantities of off-sample, labelled (archival) data.
|
[
"['Abigail Langbridge' 'Anthony Quinn' 'Robert Shorten']"
] |
null | null |
2403.13865
| null | null |
http://arxiv.org/abs/2403.13865v1
|
2024-03-20T10:13:54Z
|
2024-03-20T10:13:54Z
|
Graph Neural Network for Crawling Target Nodes in Social Networks
|
Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper we adopt graph neural networks for this purpose and show they are competitive to traditional classifiers and are better for individual cases. Additionally we suggest a training sample boosting technique, which helps to diversify the training set at early stages of crawling and thus improves the predictor quality. The experimental study on three types of target set topology indicates GNN based approach has a potential in crawling task, especially in the case of distributed target nodes.
|
[
"['Kirill Lukyanov' 'Mikhail Drobyshevskiy' 'Danil Shaikhelislamov'\n 'Denis Turdakov']"
] |
null | null |
2403.13866
| null | null |
http://arxiv.org/pdf/2403.13866v1
|
2024-03-20T11:47:42Z
|
2024-03-20T11:47:42Z
|
The Bid Picture: Auction-Inspired Multi-player Generative Adversarial
Networks Training
|
This article proposes auction-inspired multi-player generative adversarial networks training, which mitigates the mode collapse problem of GANs. Mode collapse occurs when an over-fitted generator generates a limited range of samples, often concentrating on a small subset of the data distribution. Despite the restricted diversity of generated samples, the discriminator can still be deceived into distinguishing these samples as real samples from the actual distribution. In the absence of external standards, a model cannot recognize its failure during the training phase. We extend the two-player game of generative adversarial networks to the multi-player game. During the training, the values of each model are determined by the bids submitted by other players in an auction-like process.
|
[
"['Joo Yong Shim' 'Jean Seong Bjorn Choe' 'Jong-Kook Kim']"
] |
null | null |
2403.13867
| null | null |
http://arxiv.org/pdf/2403.13867v1
|
2024-03-20T12:17:49Z
|
2024-03-20T12:17:49Z
|
Capsule Neural Networks as Noise Stabilizer for Time Series Data
|
Capsule Neural Networks utilize capsules, which bind neurons into a single vector and learn position equivariant features, which makes them more robust than original Convolutional Neural Networks. CapsNets employ an affine transformation matrix and dynamic routing with coupling coefficients to learn robustly. In this paper, we investigate the effectiveness of CapsNets in analyzing highly sensitive and noisy time series sensor data. To demonstrate CapsNets robustness, we compare their performance with original CNNs on electrocardiogram data, a medical time series sensor data with complex patterns and noise. Our study provides empirical evidence that CapsNets function as noise stabilizers, as investigated by manual and adversarial attack experiments using the fast gradient sign method and three manual attacks, including offset shifting, gradual drift, and temporal lagging. In summary, CapsNets outperform CNNs in both manual and adversarial attacked data. Our findings suggest that CapsNets can be effectively applied to various sensor systems to improve their resilience to noise attacks. These results have significant implications for designing and implementing robust machine learning models in real world applications. Additionally, this study contributes to the effectiveness of CapsNet models in handling noisy data and highlights their potential for addressing the challenges of noise data in time series analysis.
|
[
"['Soyeon Kim' 'Jihyeon Seong' 'Hyunkyung Han' 'Jaesik Choi']"
] |
null | null |
2403.13868
| null | null |
http://arxiv.org/pdf/2403.13868v1
|
2024-03-20T13:39:19Z
|
2024-03-20T13:39:19Z
|
Analysing heavy-tail properties of Stochastic Gradient Descent by means
of Stochastic Recurrence Equations
|
In recent works on the theory of machine learning, it has been observed that heavy tail properties of Stochastic Gradient Descent (SGD) can be studied in the probabilistic framework of stochastic recursions. In particular, G"{u}rb"{u}zbalaban et al. (arXiv:2006.04740) considered a setup corresponding to linear regression for which iterations of SGD can be modelled by a multivariate affine stochastic recursion $X_k=A_k X_{k-1}+B_k$, for independent and identically distributed pairs $(A_k, B_k)$, where $A_k$ is a random symmetric matrix and $B_k$ is a random vector. In this work, we will answer several open questions of the quoted paper and extend their results by applying the theory of irreducible-proximal (i-p) matrices.
|
[
"['Ewa Damek' 'Sebastian Mentemeier']"
] |
null | null |
2403.13869
| null | null |
http://arxiv.org/pdf/2403.13869v3
|
2024-04-05T15:48:03Z
|
2024-03-20T14:00:29Z
|
Accurately Predicting Probabilities of Safety-Critical Rare Events for
Intelligent Systems
|
Intelligent systems are increasingly integral to our daily lives, yet rare safety-critical events present significant latent threats to their practical deployment. Addressing this challenge hinges on accurately predicting the probability of safety-critical events occurring within a given time step from the current state, a metric we define as 'criticality'. The complexity of predicting criticality arises from the extreme data imbalance caused by rare events in high dimensional variables associated with the rare events, a challenge we refer to as the curse of rarity. Existing methods tend to be either overly conservative or prone to overlooking safety-critical events, thus struggling to achieve both high precision and recall rates, which severely limits their applicability. This study endeavors to develop a criticality prediction model that excels in both precision and recall rates for evaluating the criticality of safety-critical autonomous systems. We propose a multi-stage learning framework designed to progressively densify the dataset, mitigating the curse of rarity across stages. To validate our approach, we evaluate it in two cases: lunar lander and bipedal walker scenarios. The results demonstrate that our method surpasses traditional approaches, providing a more accurate and dependable assessment of criticality in intelligent systems.
|
[
"['Ruoxuan Bai' 'Jingxuan Yang' 'Weiduo Gong' 'Yi Zhang' 'Qiujing Lu'\n 'Shuo Feng']"
] |
null | null |
2403.13870
| null | null |
http://arxiv.org/pdf/2403.13870v1
|
2024-03-20T14:47:28Z
|
2024-03-20T14:47:28Z
|
ExMap: Leveraging Explainability Heatmaps for Unsupervised Group
Robustness to Spurious Correlations
|
Group robustness strategies aim to mitigate learned biases in deep learning models that arise from spurious correlations present in their training datasets. However, most existing methods rely on the access to the label distribution of the groups, which is time-consuming and expensive to obtain. As a result, unsupervised group robustness strategies are sought. Based on the insight that a trained model's classification strategies can be inferred accurately based on explainability heatmaps, we introduce ExMap, an unsupervised two stage mechanism designed to enhance group robustness in traditional classifiers. ExMap utilizes a clustering module to infer pseudo-labels based on a model's explainability heatmaps, which are then used during training in lieu of actual labels. Our empirical studies validate the efficacy of ExMap - We demonstrate that it bridges the performance gap with its supervised counterparts and outperforms existing partially supervised and unsupervised methods. Additionally, ExMap can be seamlessly integrated with existing group robustness learning strategies. Finally, we demonstrate its potential in tackling the emerging issue of multiple shortcut mitigationfootnote{Code available at url{https://github.com/rwchakra/exmap}}.
|
[
"['Rwiddhi Chakraborty' 'Adrian Sletten' 'Michael Kampffmeyer']"
] |
null | null |
2403.13872
| null | null |
http://arxiv.org/pdf/2403.13872v3
|
2024-07-14T15:59:14Z
|
2024-03-20T15:27:17Z
|
Spatial-Temporal Graph Representation Learning for Tactical Networks
Future State Prediction
|
Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in such environments. In this paper, we introduce the Spatial-Temporal Graph Encoder-Decoder (STGED) framework for Tactical Communication Networks that leverages both spatial and temporal features of network states to learn latent tactical behaviors effectively. STGED hierarchically utilizes graph-based attention mechanism to spatially encode a series of communication network states, leverages a recurrent neural network to temporally encode the evolution of states, and a fully-connected feed-forward network to decode the connectivity in the future state. Through extensive experiments, we demonstrate that STGED consistently outperforms baseline models by large margins across different time-steps input, achieving an accuracy of up to 99.2% for the future state prediction task of tactical communication networks.
|
[
"['Junhua Liu' 'Justin Albrethsen' 'Lincoln Goh' 'David Yau' 'Kwan Hui Lim']"
] |
null | null |
2403.13890
| null | null |
http://arxiv.org/pdf/2403.13890v2
|
2024-05-01T13:18:17Z
|
2024-03-20T18:01:57Z
|
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion
Models
|
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnancy, and for those with kidney malfunction, or other adverse reactions. With contrast uptake as key biomarker for lesion malignancy, cancer recurrence risk, and treatment response, it becomes pivotal to reduce the dependency on intravenous contrast agent administration. To this end, we propose a multi-conditional latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. To evaluate medical image synthesis, we additionally propose and validate the Fr'echet radiomics distance as an image quality measure based on biomarker variability between synthetic and real imaging data. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI and uncover the emerging potential of deep learning based contrast kinetics simulation. We publicly share our accessible codebase at https://github.com/RichardObi/ccnet and provide a user-friendly library for Fr'echet radiomics distance calculation at https://pypi.org/project/frd-score.
|
[
"['Richard Osuala' 'Daniel Lang' 'Preeti Verma' 'Smriti Joshi'\n 'Apostolia Tsirikoglou' 'Grzegorz Skorupko' 'Kaisar Kushibar'\n 'Lidia Garrucho' 'Walter H. L. Pinaya' 'Oliver Diaz' 'Julia Schnabel'\n 'Karim Lekadir']"
] |
null | null |
2403.13893
| null | null |
http://arxiv.org/pdf/2403.13893v1
|
2024-03-20T18:05:52Z
|
2024-03-20T18:05:52Z
|
Data Acquisition via Experimental Design for Decentralized Data Markets
|
Acquiring high-quality training data is essential for current machine learning models. Data markets provide a way to increase the supply of data, particularly in data-scarce domains such as healthcare, by incentivizing potential data sellers to join the market. A major challenge for a data buyer in such a market is selecting the most valuable data points from a data seller. Unlike prior work in data valuation, which assumes centralized data access, we propose a federated approach to the data selection problem that is inspired by linear experimental design. Our proposed data selection method achieves lower prediction error without requiring labeled validation data and can be optimized in a fast and federated procedure. The key insight of our work is that a method that directly estimates the benefit of acquiring data for test set prediction is particularly compatible with a decentralized market setting.
|
[
"['Charles Lu' 'Baihe Huang' 'Sai Praneeth Karimireddy'\n 'Praneeth Vepakomma' 'Michael Jordan' 'Ramesh Raskar']"
] |
null | null |
2403.13909
| null | null |
http://arxiv.org/pdf/2403.13909v1
|
2024-03-20T18:29:55Z
|
2024-03-20T18:29:55Z
|
Sequential Modeling of Complex Marine Navigation: Case Study on a
Passenger Vessel (Student Abstract)
|
The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption. This paper undertakes this challenge through a machine learning approach, leveraging a real-world dataset spanning two years of a ferry in west coast Canada. Our focus centers on the creation of a time series forecasting model given the dynamic and static states, actions, and disturbances. This model is designed to predict dynamic states based on the actions provided, subsequently serving as an evaluative tool to assess the proficiency of the ferry's operation under the captain's guidance. Additionally, it lays the foundation for future optimization algorithms, providing valuable feedback on decision-making processes. To facilitate future studies, our code is available at url{https://github.com/pagand/model_optimze_vessel/tree/AAAI}
|
[
"['Yimeng Fan' 'Pedram Agand' 'Mo Chen' 'Edward J. Park' 'Allison Kennedy'\n 'Chanwoo Bae']"
] |
null | null |
2403.13910
| null | null |
http://arxiv.org/pdf/2403.13910v1
|
2024-03-20T18:30:12Z
|
2024-03-20T18:30:12Z
|
Augmented Reality Demonstrations for Scalable Robot Imitation Learning
|
Robot Imitation Learning (IL) is a widely used method for training robots to perform manipulation tasks that involve mimicking human demonstrations to acquire skills. However, its practicality has been limited due to its requirement that users be trained in operating real robot arms to provide demonstrations. This paper presents an innovative solution: an Augmented Reality (AR)-assisted framework for demonstration collection, empowering non-roboticist users to produce demonstrations for robot IL using devices like the HoloLens 2. Our framework facilitates scalable and diverse demonstration collection for real-world tasks. We validate our approach with experiments on three classical robotics tasks: reach, push, and pick-and-place. The real robot performs each task successfully while replaying demonstrations collected via AR.
|
[
"['Yue Yang' 'Bryce Ikeda' 'Gedas Bertasius' 'Daniel Szafir']"
] |
null | null |
2403.13916
| null | null |
http://arxiv.org/pdf/2403.13916v1
|
2024-03-20T18:36:30Z
|
2024-03-20T18:36:30Z
|
Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and
Style Transfer Techniques
|
We present novel approaches involving generative adversarial networks and diffusion models in order to synthesize high quality, live and spoof fingerprint images while preserving features such as uniqueness and diversity. We generate live fingerprints from noise with a variety of methods, and we use image translation techniques to translate live fingerprint images to spoof. To generate different types of spoof images based on limited training data we incorporate style transfer techniques through a cycle autoencoder equipped with a Wasserstein metric along with Gradient Penalty (CycleWGAN-GP) in order to avoid mode collapse and instability. We find that when the spoof training data includes distinct spoof characteristics, it leads to improved live-to-spoof translation. We assess the diversity and realism of the generated live fingerprint images mainly through the Fr'echet Inception Distance (FID) and the False Acceptance Rate (FAR). Our best diffusion model achieved a FID of 15.78. The comparable WGAN-GP model achieved slightly higher FID while performing better in the uniqueness assessment due to a slightly lower FAR when matched against the training data, indicating better creativity. Moreover, we give example images showing that a DDPM model clearly can generate realistic fingerprint images.
|
[
"['W. Tang' 'D. Figueroa' 'D. Liu' 'K. Johnsson' 'A. Sopasakis']"
] |
null | null |
2403.13925
| null | null |
http://arxiv.org/pdf/2403.13925v1
|
2024-03-20T18:59:18Z
|
2024-03-20T18:59:18Z
|
Reducing Large Language Model Bias with Emphasis on 'Restricted
Industries': Automated Dataset Augmentation and Prejudice Quantification
|
Despite the growing capabilities of large language models, there exists concerns about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers and in the context of 'restricted industries' with limited data. We additionally create two new additional metrics, the mb-index and db-index, to quantify bias, considering the idea that bias occurs due to both intrinsic model architecture and dataset.
|
[
"['Devam Mondal' 'Carlo Lipizzi']"
] |
null | null |
2403.13940
| null | null |
http://arxiv.org/abs/2403.13940v1
|
2024-03-20T19:25:11Z
|
2024-03-20T19:25:11Z
|
Multi-criteria approach for selecting an explanation from the set of
counterfactuals produced by an ensemble of explainers
|
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions. They can be generated by a variety of methods that optimize different, sometimes conflicting, quality measures and produce quite different solutions. However, choosing the most appropriate explanation method and one of the generated counterfactuals is not an easy task. Instead of forcing the user to test many different explanation methods and analysing conflicting solutions, in this paper, we propose to use a multi-stage ensemble approach that will select single counterfactual based on the multiple-criteria analysis. It offers a compromise solution that scores well on several popular quality measures. This approach exploits the dominance relation and the ideal point decision aid method, which selects one counterfactual from the Pareto front. The conducted experiments demonstrated that the proposed approach generates fully actionable counterfactuals with attractive compromise values of the considered quality measures.
|
[
"['Ignacy Stępka' 'Mateusz Lango' 'Jerzy Stefanowski']"
] |
null | null |
2403.13950
| null | null |
http://arxiv.org/pdf/2403.13950v1
|
2024-03-20T19:42:11Z
|
2024-03-20T19:42:11Z
|
Evo* 2023 -- Late-Breaking Abstracts Volume
|
Volume with the Late-Breaking Abstracts submitted to the Evo* 2023 Conference, held in Brno (Czech Republic), from 12 to 14 of April. These papers present ongoing research and preliminary results investigating on the application of different approaches of Bioinspired Methods (mainly Evolutionary Computation) to different problems, most of them real world ones.
|
[
"['A. M. Mora' 'A. I. Esparcia-Alcázar']"
] |
null | null |
2403.13952
| null | null |
http://arxiv.org/pdf/2403.13952v1
|
2024-03-20T19:49:21Z
|
2024-03-20T19:49:21Z
|
Considerations in the use of ML interaction potentials for free energy
calculations
|
Machine learning potentials (MLPs) offer the potential to accurately model the energy and free energy landscapes of molecules with the precision of quantum mechanics and an efficiency similar to classical simulations. This research focuses on using equivariant graph neural networks MLPs due to their proven effectiveness in modeling equilibrium molecular trajectories. A key issue addressed is the capability of MLPs to accurately predict free energies and transition states by considering both the energy and the diversity of molecular configurations. We examined how the distribution of collective variables (CVs) in the training data affects MLP accuracy in determining the free energy surface (FES) of systems, using Metadynamics simulations for butane and alanine dipeptide (ADP). The study involved training forty-three MLPs, half based on classical molecular dynamics data and the rest on ab initio computed energies. The MLPs were trained using different distributions that aim to replicate hypothetical scenarios of sampled CVs obtained if the underlying FES of the system was unknown. Findings for butane revealed that training data coverage of key FES regions ensures model accuracy regardless of CV distribution. However, missing significant FES regions led to correct potential energy predictions but failed free energy reconstruction. For ADP, models trained on classical dynamics data were notably less accurate, while ab initio-based MLPs predicted potential energy well but faltered on free energy predictions. These results emphasize the challenge of assembling an all-encompassing training set for accurate FES prediction and highlight the importance of understanding the FES in preparing training data. The study points out the limitations of MLPs in free energy calculations, stressing the need for comprehensive data that encompasses the system's full FES for effective model training.
|
[
"['Orlando A. Mendible' 'Jonathan K. Whitmer' 'Yamil J. Colón']"
] |
null | null |
2403.14002
| null | null |
http://arxiv.org/pdf/2403.14002v1
|
2024-03-20T22:03:40Z
|
2024-03-20T22:03:40Z
|
Uncertainty Driven Active Learning for Image Segmentation in Underwater
Inspection
|
Active learning aims to select the minimum amount of data to train a model that performs similarly to a model trained with the entire dataset. We study the potential of active learning for image segmentation in underwater infrastructure inspection tasks, where large amounts of data are typically collected. The pipeline inspection images are usually semantically repetitive but with great variations in quality. We use mutual information as the acquisition function, calculated using Monte Carlo dropout. To assess the effectiveness of the framework, DenseNet and HyperSeg are trained with the CamVid dataset using active learning. In addition, HyperSeg is trained with a pipeline inspection dataset of over 50,000 images. For the pipeline dataset, HyperSeg with active learning achieved 67.5% meanIoU using 12.5% of the data, and 61.4% with the same amount of randomly selected images. This shows that using active learning for segmentation models in underwater inspection tasks can lower the cost significantly.
|
[
"['Luiza Ribeiro Marnet' 'Yury Brodskiy' 'Stella Grasshof'\n 'Andrzej Wasowski']"
] |
null | null |
2403.14003
| null | null |
http://arxiv.org/pdf/2403.14003v1
|
2024-03-20T22:05:18Z
|
2024-03-20T22:05:18Z
|
Multi-Modal Hallucination Control by Visual Information Grounding
|
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image. We investigate this phenomenon, usually referred to as "hallucination" and show that it stems from an excessive reliance on the language prior. In particular, we show that as more tokens are generated, the reliance on the visual prompt decreases, and this behavior strongly correlates with the emergence of hallucinations. To reduce hallucinations, we introduce Multi-Modal Mutual-Information Decoding (M3ID), a new sampling method for prompt amplification. M3ID amplifies the influence of the reference image over the language prior, hence favoring the generation of tokens with higher mutual information with the visual prompt. M3ID can be applied to any pre-trained autoregressive VLM at inference time without necessitating further training and with minimal computational overhead. If training is an option, we show that M3ID can be paired with Direct Preference Optimization (DPO) to improve the model's reliance on the prompt image without requiring any labels. Our empirical findings show that our algorithms maintain the fluency and linguistic capabilities of pre-trained VLMs while reducing hallucinations by mitigating visually ungrounded answers. Specifically, for the LLaVA 13B model, M3ID and M3ID+DPO reduce the percentage of hallucinated objects in captioning tasks by 25% and 28%, respectively, and improve the accuracy on VQA benchmarks such as POPE by 21% and 24%.
|
[
"['Alessandro Favero' 'Luca Zancato' 'Matthew Trager' 'Siddharth Choudhary'\n 'Pramuditha Perera' 'Alessandro Achille' 'Ashwin Swaminathan'\n 'Stefano Soatto']"
] |
null | null |
2403.14013
| null | null |
http://arxiv.org/pdf/2403.14013v1
|
2024-03-20T22:24:36Z
|
2024-03-20T22:24:36Z
|
Towards a connection between the capacitated vehicle routing problem and
the constrained centroid-based clustering
|
Efficiently solving a vehicle routing problem (VRP) in a practical runtime is a critical challenge for delivery management companies. This paper explores both a theoretical and experimental connection between the Capacitated Vehicle Routing Problem (CVRP) and the Constrained Centroid-Based Clustering (CCBC). Reducing a CVRP to a CCBC is a synonym for a transition from an exponential to a polynomial complexity using commonly known algorithms for clustering, i.e K-means. At the beginning, we conduct an exploratory analysis to highlight the existence of such a relationship between the two problems through illustrative small-size examples and simultaneously deduce some mathematically-related formulations and properties. On a second level, the paper proposes a CCBC based approach endowed with some enhancements. The proposed framework consists of three stages. At the first step, a constrained centroid-based clustering algorithm generates feasible clusters of customers. This methodology incorporates three enhancement tools to achieve near-optimal clusters, namely: a multi-start procedure for initial centroids, a customer assignment metric, and a self-adjustment mechanism for choosing the number of clusters. At the second step, a traveling salesman problem (T SP) solver is used to optimize the order of customers within each cluster. Finally, we introduce a process relying on routes cutting and relinking procedure, which calls upon solving a linear and integer programming model to further improve the obtained routes. This step is inspired by the ruin & recreate algorithm. This approach is an extension of the classical cluster-first, route-second method and provides near-optimal solutions on well-known benchmark instances in terms of solution quality and computational runtime, offering a milestone in solving VRP.
|
[
"['Abdelhakim Abdellaoui' 'Loubna Benabbou' 'Issmail El Hallaoui']"
] |
null | null |
2403.14050
| null | null |
http://arxiv.org/pdf/2403.14050v2
|
2024-03-23T14:46:59Z
|
2024-03-21T00:20:16Z
|
Extracting Emotion Phrases from Tweets using BART
|
Sentiment analysis is a natural language processing task that aims to identify and extract the emotional aspects of a text. However, many existing sentiment analysis methods primarily classify the overall polarity of a text, overlooking the specific phrases that convey sentiment. In this paper, we applied an approach to sentiment analysis based on a question-answering framework. Our approach leverages the power of Bidirectional Autoregressive Transformer (BART), a pre-trained sequence-to-sequence model, to extract a phrase from a given text that amplifies a given sentiment polarity. We create a natural language question that identifies the specific emotion to extract and then guide BART to pay attention to the relevant emotional cues in the text. We use a classifier within BART to predict the start and end positions of the answer span within the text, which helps to identify the precise boundaries of the extracted emotion phrase. Our approach offers several advantages over most sentiment analysis studies, including capturing the complete context and meaning of the text and extracting precise token spans that highlight the intended sentiment. We achieved an end loss of 87% and Jaccard score of 0.61.
|
[
"['Mahdi Rezapour']"
] |
null | null |
2403.14058
| null | null |
http://arxiv.org/pdf/2403.14058v1
|
2024-03-21T01:06:47Z
|
2024-03-21T01:06:47Z
|
Hypothesis-Driven Deep Learning for Out of Distribution Detection
|
Predictions of opaque black-box systems are frequently deployed in high-stakes applications such as healthcare. For such applications, it is crucial to assess how models handle samples beyond the domain of training data. While several metrics and tests exist to detect out-of-distribution (OoD) data from in-distribution (InD) data to a deep neural network (DNN), their performance varies significantly across datasets, models, and tasks, which limits their practical use. In this paper, we propose a hypothesis-driven approach to quantify whether a new sample is InD or OoD. Given a trained DNN and some input, we first feed the input through the DNN and compute an ensemble of OoD metrics, which we term latent responses. We then formulate the OoD detection problem as a hypothesis test between latent responses of different groups, and use permutation-based resampling to infer the significance of the observed latent responses under a null hypothesis. We adapt our method to detect an unseen sample of bacteria to a trained deep learning model, and show that it reveals interpretable differences between InD and OoD latent responses. Our work has implications for systematic novelty detection and informed decision-making from classifiers trained on a subset of labels.
|
[
"['Yasith Jayawardana' 'Azeem Ahmad' 'Balpreet S. Ahluwalia' 'Rafi Ahmad'\n 'Sampath Jayarathna' 'Dushan N. Wadduwage']"
] |
null | null |
2403.14063
| null | null |
http://arxiv.org/pdf/2403.14063v1
|
2024-03-21T01:20:32Z
|
2024-03-21T01:20:32Z
|
DiffSTOCK: Probabilistic relational Stock Market Predictions using
Diffusion Models
|
In this work, we propose an approach to generalize denoising diffusion probabilistic models for stock market predictions and portfolio management. Present works have demonstrated the efficacy of modeling interstock relations for market time-series forecasting and utilized Graph-based learning models for value prediction and portfolio management. Though convincing, these deterministic approaches still fall short of handling uncertainties i.e., due to the low signal-to-noise ratio of the financial data, it is quite challenging to learn effective deterministic models. Since the probabilistic methods have shown to effectively emulate higher uncertainties for time-series predictions. To this end, we showcase effective utilisation of Denoising Diffusion Probabilistic Models (DDPM), to develop an architecture for providing better market predictions conditioned on the historical financial indicators and inter-stock relations. Additionally, we also provide a novel deterministic architecture MaTCHS which uses Masked Relational Transformer(MRT) to exploit inter-stock relations along with historical stock features. We demonstrate that our model achieves SOTA performance for movement predication and Portfolio management.
|
[
"['Divyanshu Daiya' 'Monika Yadav' 'Harshit Singh Rao']"
] |
null | null |
2403.14067
| null | null |
http://arxiv.org/pdf/2403.14067v2
|
2024-07-11T05:22:42Z
|
2024-03-21T01:30:24Z
|
Automatic Outlier Rectification via Optimal Transport
|
In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically use a two-stage procedure: first, outliers are detected and removed, and then estimation is performed on the cleaned data. However, this approach does not inform outlier removal with the estimation task, leaving room for improvement. To address this limitation, we propose an automatic outlier rectification mechanism that integrates rectification and estimation within a joint optimization framework. We take the first step to utilize the optimal transport distance with a concave cost function to construct a rectification set in the space of probability distributions. Then, we select the best distribution within the rectification set to perform the estimation task. Notably, the concave cost function we introduced in this paper is the key to making our estimator effectively identify the outlier during the optimization process. We demonstrate the effectiveness of our approach over conventional approaches in simulations and empirical analyses for mean estimation, least absolute regression, and the fitting of option implied volatility surfaces.
|
[
"['Jose Blanchet' 'Jiajin Li' 'Markus Pelger' 'Greg Zanotti']"
] |
null | null |
2403.14069
| null | null |
http://arxiv.org/pdf/2403.14069v1
|
2024-03-21T01:35:03Z
|
2024-03-21T01:35:03Z
|
Sampling Audit Evidence Using a Naive Bayes Classifier
|
Taiwan's auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetric around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.
|
[
"['Guang-Yih Sheu' 'Nai-Ru Liu']"
] |
null | null |
2403.14074
| null | null |
http://arxiv.org/pdf/2403.14074v1
|
2024-03-21T01:52:07Z
|
2024-03-21T01:52:07Z
|
M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain
Multi-Hop Dense Sentence Retrieval
|
In recent research, contrastive learning has proven to be a highly effective method for representation learning and is widely used for dense retrieval. However, we identify that relying solely on contrastive learning can lead to suboptimal retrieval performance. On the other hand, despite many retrieval datasets supporting various learning objectives beyond contrastive learning, combining them efficiently in multi-task learning scenarios can be challenging. In this paper, we introduce M3, an advanced recursive Multi-hop dense sentence retrieval system built upon a novel Multi-task Mixed-objective approach for dense text representation learning, addressing the aforementioned challenges. Our approach yields state-of-the-art performance on a large-scale open-domain fact verification benchmark dataset, FEVER. Code and data are available at: https://github.com/TonyBY/M3
|
[
"['Yang Bai' 'Anthony Colas' 'Christan Grant' 'Daisy Zhe Wang']"
] |
null | null |
2403.14076
| null | null |
http://arxiv.org/pdf/2403.14076v1
|
2024-03-21T01:54:00Z
|
2024-03-21T01:54:00Z
|
Improving $Λ$ Signal Extraction with Domain Adaptation via
Normalizing Flows
|
The present study presents a novel application for normalizing flows for domain adaptation. The study investigates the ability of flow based neural networks to improve signal extraction of $Lambda$ Hyperons at CLAS12. Normalizing Flows can help model complex probability density functions that describe physics processes, enabling uses such as event generation. $Lambda$ signal extraction has been improved through the use of classifier networks, but differences in simulation and data domains limit classifier performance; this study utilizes the flows for domain adaptation between Monte Carlo simulation and data. We were successful in training a flow network to transform between the latent physics space and a normal distribution. We also found that applying the flows lessened the dependence of the figure of merit on the cut on the classifier output, meaning that there was a broader range where the cut results in a similar figure of merit.
|
[
"['Rowan Kelleher' 'Matthew McEneaney' 'Anselm Vossen']"
] |
null | null |
2403.14083
| null | null |
http://arxiv.org/pdf/2403.14083v1
|
2024-03-21T02:26:30Z
|
2024-03-21T02:26:30Z
|
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network
Architectures for Superior Speech Emotion Recognition
|
Speech Emotion Recognition (SER) is crucial for enabling computers to understand the emotions conveyed in human communication. With recent advancements in Deep Learning (DL), the performance of SER models has significantly improved. However, designing an optimal DL architecture requires specialised knowledge and experimental assessments. Fortunately, Neural Architecture Search (NAS) provides a potential solution for automatically determining the best DL model. The Differentiable Architecture Search (DARTS) is a particularly efficient method for discovering optimal models. This study presents emoDARTS, a DARTS-optimised joint CNN and Sequential Neural Network (SeqNN: LSTM, RNN) architecture that enhances SER performance. The literature supports the selection of CNN and LSTM coupling to improve performance. While DARTS has previously been used to choose CNN and LSTM operations independently, our technique adds a novel mechanism for selecting CNN and SeqNN operations in conjunction using DARTS. Unlike earlier work, we do not impose limits on the layer order of the CNN. Instead, we let DARTS choose the best layer order inside the DARTS cell. We demonstrate that emoDARTS outperforms conventionally designed CNN-LSTM models and surpasses the best-reported SER results achieved through DARTS on CNN-LSTM by evaluating our approach on the IEMOCAP, MSP-IMPROV, and MSP-Podcast datasets.
|
[
"['Thejan Rajapakshe' 'Rajib Rana' 'Sara Khalifa' 'Berrak Sisman'\n 'Bjorn W. Schuller' 'Carlos Busso']"
] |
null | null |
2403.14084
| null | null |
http://arxiv.org/pdf/2403.14084v2
|
2024-06-20T07:50:42Z
|
2024-03-21T02:30:56Z
|
Learning-based Multi-continuum Model for Multiscale Flow Problems
|
Multiscale problems can usually be approximated through numerical homogenization by an equation with some effective parameters that can capture the macroscopic behavior of the original system on the coarse grid to speed up the simulation. However, this approach usually assumes scale separation and that the heterogeneity of the solution can be approximated by the solution average in each coarse block. For complex multiscale problems, the computed single effective properties/continuum might be inadequate. In this paper, we propose a novel learning-based multi-continuum model to enrich the homogenized equation and improve the accuracy of the single continuum model for multiscale problems with some given data. Without loss of generalization, we consider a two-continuum case. The first flow equation keeps the information of the original homogenized equation with an additional interaction term. The second continuum is newly introduced, and the effective permeability in the second flow equation is determined by a neural network. The interaction term between the two continua aligns with that used in the Dual-porosity model but with a learnable coefficient determined by another neural network. The new model with neural network terms is then optimized using trusted data. We discuss both direct back-propagation and the adjoint method for the PDE-constraint optimization problem. Our proposed learning-based multi-continuum model can resolve multiple interacted media within each coarse grid block and describe the mass transfer among them, and it has been demonstrated to significantly improve the simulation results through numerical experiments involving both linear and nonlinear flow equations.
|
[
"['Fan Wang' 'Yating Wang' 'Wing Tat Leung' 'Zongben Xu']"
] |
null | null |
2403.14088
| null | null |
http://arxiv.org/pdf/2403.14088v1
|
2024-03-21T02:44:08Z
|
2024-03-21T02:44:08Z
|
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
|
The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes. Traditional physics-based computational methods, such as molecular dynamics (MD) simulations, suffer from rare event sampling and long equilibration time problems, hindering their applications in general protein systems. Recently, deep generative modeling techniques, especially diffusion models, have been employed to generate novel protein conformations. However, existing score-based diffusion methods cannot properly incorporate important physical prior knowledge to guide the generation process, causing large deviations in the sampled protein conformations from the equilibrium distribution. In this paper, to overcome these limitations, we propose a force-guided SE(3) diffusion model, ConfDiff, for protein conformation generation. By incorporating a force-guided network with a mixture of data-based score models, ConfDiff can can generate protein conformations with rich diversity while preserving high fidelity. Experiments on a variety of protein conformation prediction tasks, including 12 fast-folding proteins and the Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our method surpasses the state-of-the-art method.
|
[
"['Yan Wang' 'Lihao Wang' 'Yuning Shen' 'Yiqun Wang' 'Huizhuo Yuan'\n 'Yue Wu' 'Quanquan Gu']"
] |
null | null |
2403.14092
| null | null |
http://arxiv.org/pdf/2403.14092v2
|
2024-03-25T17:49:07Z
|
2024-03-21T02:59:56Z
|
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
|
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.
|
[
"['Soumyendu Sarkar' 'Avisek Naug' 'Ricardo Luna' 'Antonio Guillen'\n 'Vineet Gundecha' 'Sahand Ghorbanpour' 'Sajad Mousavi' 'Dejan Markovikj'\n 'Ashwin Ramesh Babu']"
] |
null | null |
2403.14101
| null | null |
http://arxiv.org/pdf/2403.14101v1
|
2024-03-21T03:24:01Z
|
2024-03-21T03:24:01Z
|
Text-Enhanced Data-free Approach for Federated Class-Incremental
Learning
|
Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue, involving the dynamic addition of new classes in the context of federated learning. In this field, Data-Free Knowledge Transfer (DFKT) plays a crucial role in addressing catastrophic forgetting and data privacy problems. However, prior approaches lack the crucial synergy between DFKT and the model training phases, causing DFKT to encounter difficulties in generating high-quality data from a non-anchored latent space of the old task model. In this paper, we introduce LANDER (Label Text Centered Data-Free Knowledge Transfer) to address this issue by utilizing label text embeddings (LTE) produced by pretrained language models. Specifically, during the model training phase, our approach treats LTE as anchor points and constrains the feature embeddings of corresponding training samples around them, enriching the surrounding area with more meaningful information. In the DFKT phase, by using these LTE anchors, LANDER can synthesize more meaningful samples, thereby effectively addressing the forgetting problem. Additionally, instead of tightly constraining embeddings toward the anchor, the Bounding Loss is introduced to encourage sample embeddings to remain flexible within a defined radius. This approach preserves the natural differences in sample embeddings and mitigates the embedding overlap caused by heterogeneous federated settings. Extensive experiments conducted on CIFAR100, Tiny-ImageNet, and ImageNet demonstrate that LANDER significantly outperforms previous methods and achieves state-of-the-art performance in FCIL. The code is available at https://github.com/tmtuan1307/lander.
|
[
"['Minh-Tuan Tran' 'Trung Le' 'Xuan-May Le' 'Mehrtash Harandi' 'Dinh Phung']"
] |
null | null |
2403.14102
| null | null |
http://arxiv.org/pdf/2403.14102v1
|
2024-03-21T03:25:49Z
|
2024-03-21T03:25:49Z
|
DouRN: Improving DouZero by Residual Neural Networks
|
Deep reinforcement learning has made significant progress in games with imperfect information, but its performance in the card game Doudizhu (Chinese Poker/Fight the Landlord) remains unsatisfactory. Doudizhu is different from conventional games as it involves three players and combines elements of cooperation and confrontation, resulting in a large state and action space. In 2021, a Doudizhu program called DouZerocite{zha2021douzero} surpassed previous models without prior knowledge by utilizing traditional Monte Carlo methods and multilayer perceptrons. Building on this work, our study incorporates residual networks into the model, explores different architectural designs, and conducts multi-role testing. Our findings demonstrate that this model significantly improves the winning rate within the same training time. Additionally, we introduce a call scoring system to assist the agent in deciding whether to become a landlord. With these enhancements, our model consistently outperforms the existing version of DouZero and even experienced human players. footnote{The source code is available at url{https://github.com/Yingchaol/Douzero_Resnet.git.}
|
[
"['Yiquan Chen' 'Yingchao Lyu' 'Di Zhang']"
] |
null | null |
2403.14110
| null | null |
http://arxiv.org/pdf/2403.14110v1
|
2024-03-21T03:42:39Z
|
2024-03-21T03:42:39Z
|
Heuristic Algorithm-based Action Masking Reinforcement Learning
(HAAM-RL) with Ensemble Inference Method
|
This paper presents a novel reinforcement learning (RL) approach called HAAM-RL (Heuristic Algorithm-based Action Masking Reinforcement Learning) for optimizing the color batching re-sequencing problem in automobile painting processes. The existing heuristic algorithms have limitations in adequately reflecting real-world constraints and accurately predicting logistics performance. Our methodology incorporates several key techniques including a tailored Markov Decision Process (MDP) formulation, reward setting including Potential-Based Reward Shaping, action masking using heuristic algorithms (HAAM-RL), and an ensemble inference method that combines multiple RL models. The RL agent is trained and evaluated using FlexSim, a commercial 3D simulation software, integrated with our RL MLOps platform BakingSoDA. Experimental results across 30 scenarios demonstrate that HAAM-RL with an ensemble inference method achieves a 16.25% performance improvement over the conventional heuristic algorithm, with stable and consistent results. The proposed approach exhibits superior performance and generalization capability, indicating its effectiveness in optimizing complex manufacturing processes. The study also discusses future research directions, including alternative state representations, incorporating model-based RL methods, and integrating additional real-world constraints.
|
[
"['Kyuwon Choi' 'Cheolkyun Rho' 'Taeyoun Kim' 'Daewoo Choi']"
] |
null | null |
2403.14111
| null | null |
http://arxiv.org/pdf/2403.14111v1
|
2024-03-21T03:47:26Z
|
2024-03-21T03:47:26Z
|
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic
Encryption
|
Transfer learning is a de facto standard method for efficiently training machine learning models for data-scarce problems by adding and fine-tuning new classification layers to a model pre-trained on large datasets. Although numerous previous studies proposed to use homomorphic encryption to resolve the data privacy issue in transfer learning in the machine learning as a service setting, most of them only focused on encrypted inference. In this study, we present HETAL, an efficient Homomorphic Encryption based Transfer Learning algorithm, that protects the client's privacy in training tasks by encrypting the client data using the CKKS homomorphic encryption scheme. HETAL is the first practical scheme that strictly provides encrypted training, adopting validation-based early stopping and achieving the accuracy of nonencrypted training. We propose an efficient encrypted matrix multiplication algorithm, which is 1.8 to 323 times faster than prior methods, and a highly precise softmax approximation algorithm with increased coverage. The experimental results for five well-known benchmark datasets show total training times of 567-3442 seconds, which is less than an hour.
|
[
"['Seewoo Lee' 'Garam Lee' 'Jung Woo Kim' 'Junbum Shin' 'Mun-Kyu Lee']"
] |
null | null |
2403.14119
| null | null |
http://arxiv.org/pdf/2403.14119v3
|
2024-03-31T13:36:54Z
|
2024-03-21T04:08:29Z
|
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via
Text Feature Dispersion
|
In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. A prime exemplification is the recently proposed test-time prompt tuning for large-scale vision-language models such as CLIP. Unfortunately, these prompts have been mainly developed to improve accuracy, overlooking the importance of calibration, which is a crucial aspect for quantifying prediction uncertainty. However, traditional calibration methods rely on substantial amounts of labeled data, making them impractical for test-time scenarios. To this end, this paper explores calibration during test-time prompt tuning by leveraging the inherent properties of CLIP. Through a series of observations, we find that the prompt choice significantly affects the calibration in CLIP, where the prompts leading to higher text feature dispersion result in better-calibrated predictions. Introducing the Average Text Feature Dispersion (ATFD), we establish its relationship with calibration error and present a novel method, Calibrated Test-time Prompt Tuning (C-TPT), for optimizing prompts during test-time with enhanced calibration. Through extensive experiments on different CLIP architectures and datasets, we show that C-TPT can effectively improve the calibration of test-time prompt tuning without needing labeled data. The code is publicly accessible at https://github.com/hee-suk-yoon/C-TPT.
|
[
"['Hee Suk Yoon' 'Eunseop Yoon' 'Joshua Tian Jin Tee'\n 'Mark Hasegawa-Johnson' 'Yingzhen Li' 'Chang D. Yoo']"
] |
null | null |
2403.14120
| null | null |
http://arxiv.org/pdf/2403.14120v1
|
2024-03-21T04:15:56Z
|
2024-03-21T04:15:56Z
|
Advancing IIoT with Over-the-Air Federated Learning: The Role of
Iterative Magnitude Pruning
|
The industrial Internet of Things (IIoT) under Industry 4.0 heralds an era of interconnected smart devices where data-driven insights and machine learning (ML) fuse to revolutionize manufacturing. A noteworthy development in IIoT is the integration of federated learning (FL), which addresses data privacy and security among devices. FL enables edge sensors, also known as peripheral intelligence units (PIUs) to learn and adapt using their data locally, without explicit sharing of confidential data, to facilitate a collaborative yet confidential learning process. However, the lower memory footprint and computational power of PIUs inherently require deep neural network (DNN) models that have a very compact size. Model compression techniques such as pruning can be used to reduce the size of DNN models by removing unnecessary connections that have little impact on the model's performance, thus making the models more suitable for the limited resources of PIUs. Targeting the notion of compact yet robust DNN models, we propose the integration of iterative magnitude pruning (IMP) of the DNN model being trained in an over-the-air FL (OTA-FL) environment for IIoT. We provide a tutorial overview and also present a case study of the effectiveness of IMP in OTA-FL for an IIoT environment. Finally, we present future directions for enhancing and optimizing these deep compression techniques further, aiming to push the boundaries of IIoT capabilities in acquiring compact yet robust and high-performing DNN models.
|
[
"['Fazal Muhammad Ali Khan' 'Hatem Abou-Zeid' 'Aryan Kaushik'\n 'Syed Ali Hassan']"
] |
null | null |
2403.14123
| null | null |
http://arxiv.org/pdf/2403.14123v1
|
2024-03-21T04:31:59Z
|
2024-03-21T04:31:59Z
|
AI and Memory Wall
|
The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memory bandwidth. Over the past 20 years, peak server hardware FLOPS has been scaling at 3.0x/2yrs, outpacing the growth of DRAM and interconnect bandwidth, which have only scaled at 1.6 and 1.4 times every 2 years, respectively. This disparity has made memory, rather than compute, the primary bottleneck in AI applications, particularly in serving. Here, we analyze encoder and decoder Transformer models and show how memory bandwidth can become the dominant bottleneck for decoder models. We argue for a redesign in model architecture, training, and deployment strategies to overcome this memory limitation.
|
[
"['Amir Gholami' 'Zhewei Yao' 'Sehoon Kim' 'Coleman Hooper'\n 'Michael W. Mahoney' 'Kurt Keutzer']"
] |
null | null |
2403.14125
| null | null |
http://arxiv.org/pdf/2403.14125v1
|
2024-03-21T04:42:04Z
|
2024-03-21T04:42:04Z
|
Learning causal graphs using variable grouping according to ancestral
relationship
|
Several causal discovery algorithms have been proposed. However, when the sample size is small relative to the number of variables, the accuracy of estimating causal graphs using existing methods decreases. And some methods are not feasible when the sample size is smaller than the number of variables. To circumvent these problems, some researchers proposed causal structure learning algorithms using divide-and-conquer approaches. For learning the entire causal graph, the approaches first split variables into several subsets according to the conditional independence relationships among the variables, then apply a conventional causal discovery algorithm to each subset and merge the estimated results. Since the divide-and-conquer approach reduces the number of variables to which a causal structure learning algorithm is applied, it is expected to improve the estimation accuracy of causal graphs, especially when the sample size is small relative to the number of variables and the model is sparse. However, existing methods are either computationally expensive or do not provide sufficient accuracy when the sample size is small. This paper proposes a new algorithm for grouping variables based the ancestral relationships among the variables, under the LiNGAM assumption, where the causal relationships are linear, and the mutually independent noise are distributed as continuous non-Gaussian distributions. We call the proposed algorithm CAG. The time complexity of the ancestor finding in CAG is shown to be cubic to the number of variables. Extensive computer experiments confirm that the proposed method outperforms the original DirectLiNGAM without grouping variables and other divide-and-conquer approaches not only in estimation accuracy but also in computation time when the sample size is small relative to the number of variables and the model is sparse.
|
[
"['Ming Cai' 'Hisayuki Hara']"
] |
null | null |
2403.14137
| null | null |
http://arxiv.org/pdf/2403.14137v2
|
2024-03-24T07:58:01Z
|
2024-03-21T05:13:12Z
|
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion
and Inter-Class Separability in Image Classification
|
To address the issues of MixUp and its variants (e.g., Manifold MixUp) in image classification tasks-namely, their neglect of mixing within the same class (intra-class mixup) and their inadequacy in enhancing intra-class cohesion through their mixing operations-we propose a novel mixup method named SynerMix-Intra and, building upon this, introduce a synergistic mixup solution named SynerMix. SynerMix-Intra specifically targets intra-class mixup to bolster intra-class cohesion, a feature not addressed by current mixup methods. For each mini-batch, it leverages feature representations of unaugmented original images from each class to generate a synthesized feature representation through random linear interpolation. All synthesized representations are then fed into the classification and loss layers to calculate an average classification loss that significantly enhances intra-class cohesion. Furthermore, SynerMix combines SynerMix-Intra with an existing mixup approach (e.g., MixUp, Manifold MixUp), which primarily focuses on inter-class mixup and has the benefit of enhancing inter-class separability. In doing so, it integrates both inter- and intra-class mixup in a balanced way while concurrently improving intra-class cohesion and inter-class separability. Experimental results on six datasets show that SynerMix achieves a 0.1% to 3.43% higher accuracy than the best of either MixUp or SynerMix-Intra alone, averaging a 1.16% gain. It also surpasses the top-performer of either Manifold MixUp or SynerMix-Intra by 0.12% to 5.16%, with an average gain of 1.11%. Given that SynerMix is model-agnostic, it holds significant potential for application in other domains where mixup methods have shown promise, such as speech and text classification. Our code is publicly available at: https://github.com/wxitxy/synermix.git.
|
[
"['Ye Xu' 'Ya Gao' 'Xiaorong Qiu' 'Yang Chen' 'Ying Ji']"
] |
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