categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.18120
| null | null |
http://arxiv.org/pdf/2403.18120v1
|
2024-03-26T22:01:13Z
|
2024-03-26T22:01:13Z
|
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with
Autoformalization
|
Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However, they still make unjustified logical and computational errors in their reasoning steps and answers. In this paper, we leverage the fact that if the training corpus of LLMs contained sufficiently many examples of formal mathematics (e.g. in Isabelle, a formal theorem proving environment), they can be prompted to translate i.e. autoformalize informal mathematical statements into formal Isabelle code -- which can be verified automatically for internal consistency. This provides a mechanism to automatically reject solutions whose formalized versions are inconsistent within themselves or with the formalized problem statement. We evaluate our method on GSM8K, MATH and MultiArith datasets and demonstrate that our approach provides a consistently better heuristic than vanilla majority voting -- the previously best method to identify correct answers, by more than 12% on GSM8K. In our experiments it improves results consistently across all datasets and LLM model sizes. The code can be found at https://github.com/jinpz/dtv.
|
[
"['Jin Peng Zhou' 'Charles Staats' 'Wenda Li' 'Christian Szegedy'\n 'Kilian Q. Weinberger' 'Yuhuai Wu']"
] |
null | null |
2403.18127
| null | null |
http://arxiv.org/pdf/2403.18127v1
|
2024-03-26T22:15:47Z
|
2024-03-26T22:15:47Z
|
A Correction of Pseudo Log-Likelihood Method
|
Pseudo log-likelihood is a type of maximum likelihood estimation (MLE) method used in various fields including contextual bandits, influence maximization of social networks, and causal bandits. However, in previous literature citep{li2017provably, zhang2022online, xiong2022combinatorial, feng2023combinatorial1, feng2023combinatorial2}, the log-likelihood function may not be bounded, which may result in the algorithm they proposed not well-defined. In this paper, we give a counterexample that the maximum pseudo log-likelihood estimation fails and then provide a solution to correct the algorithms in citep{li2017provably, zhang2022online, xiong2022combinatorial, feng2023combinatorial1, feng2023combinatorial2}.
|
[
"['Shi Feng' 'Nuoya Xiong' 'Zhijie Zhang' 'Wei Chen']"
] |
null | null |
2403.18128
| null | null |
http://arxiv.org/pdf/2403.18128v1
|
2024-03-26T22:17:01Z
|
2024-03-26T22:17:01Z
|
HealthGAT: Node Classifications in Electronic Health Records using Graph
Attention Networks
|
While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly limit the performance or even applicability of downstream tasks using EHRs. To address this challenge, we present HealthGAT, a novel graph attention network framework that utilizes a hierarchical approach to generate embeddings from EHR, surpassing traditional graph-based methods. Our model iteratively refines the embeddings for medical codes, resulting in improved EHR data analysis. We also introduce customized EHR-centric auxiliary pre-training tasks to leverage the rich medical knowledge embedded within the data. This approach provides a comprehensive analysis of complex medical relationships and offers significant advancement over standard data representation techniques. HealthGAT has demonstrated its effectiveness in various healthcare scenarios through comprehensive evaluations against established methodologies. Specifically, our model shows outstanding performance in node classification and downstream tasks such as predicting readmissions and diagnosis classifications. Our code is available at https://github.com/healthylaife/HealthGAT
|
[
"['Fahmida Liza Piya' 'Mehak Gupta' 'Rahmatollah Beheshti']"
] |
null | null |
2403.18132
| null | null |
http://arxiv.org/pdf/2403.18132v1
|
2024-03-26T22:26:39Z
|
2024-03-26T22:26:39Z
|
Recommendation of data-free class-incremental learning algorithms by
simulating future data
|
Class-incremental learning deals with sequential data streams composed of batches of classes. Various algorithms have been proposed to address the challenging case where samples from past classes cannot be stored. However, selecting an appropriate algorithm for a user-defined setting is an open problem, as the relative performance of these algorithms depends on the incremental settings. To solve this problem, we introduce an algorithm recommendation method that simulates the future data stream. Given an initial set of classes, it leverages generative models to simulate future classes from the same visual domain. We evaluate recent algorithms on the simulated stream and recommend the one which performs best in the user-defined incremental setting. We illustrate the effectiveness of our method on three large datasets using six algorithms and six incremental settings. Our method outperforms competitive baselines, and performance is close to that of an oracle choosing the best algorithm in each setting. This work contributes to facilitate the practical deployment of incremental learning.
|
[
"['Eva Feillet' 'Adrian Popescu' 'Céline Hudelot']"
] |
null | null |
2403.18133
| null | null |
http://arxiv.org/pdf/2403.18133v1
|
2024-03-26T22:28:43Z
|
2024-03-26T22:28:43Z
|
AE SemRL: Learning Semantic Association Rules with Autoencoders
|
Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data, for example, time series data from a large number of sensors in a smart environment, is a computationally intensive task. In this study, we propose an Autoencoder-based approach to learn and extract association rules from time series data (AE SemRL). Moreover, we argue that in the presence of semantic information related to time series data sources, semantics can facilitate learning generalizable and explainable association rules. Despite enriching time series data with additional semantic features, AE SemRL makes learning association rules from high-dimensional data feasible. Our experiments show that semantic association rules can be extracted from a latent representation created by an Autoencoder and this method has in the order of hundreds of times faster execution time than state-of-the-art ARM approaches in many scenarios. We believe that this study advances a new way of extracting associations from representations and has the potential to inspire more research in this field.
|
[
"['Erkan Karabulut' 'Victoria Degeler' 'Paul Groth']"
] |
null | null |
2403.18136
| null | null |
http://arxiv.org/pdf/2403.18136v1
|
2024-03-26T22:41:41Z
|
2024-03-26T22:41:41Z
|
Securing GNNs: Explanation-Based Identification of Backdoored Training
Graphs
|
Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks that can compromise their performance and ethical application. The detection of these attacks is crucial for maintaining the reliability and security of GNN classification tasks, but effective detection techniques are lacking. Following an initial investigation, we observed that while graph-level explanations can offer limited insights, their effectiveness in detecting backdoor triggers is inconsistent and incomplete. To bridge this gap, we extract and transform secondary outputs of GNN explanation mechanisms, designing seven novel metrics that more effectively detect backdoor attacks. Additionally, we develop an adaptive attack to rigorously evaluate our approach. We test our method on multiple benchmark datasets and examine its efficacy against various attack models. Our results show that our method can achieve high detection performance, marking a significant advancement in safeguarding GNNs against backdoor attacks.
|
[
"['Jane Downer' 'Ren Wang' 'Binghui Wang']"
] |
null | null |
2403.18142
| null | null |
http://arxiv.org/pdf/2403.18142v1
|
2024-03-26T23:03:06Z
|
2024-03-26T23:03:06Z
|
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded
Graph Neural Networks
|
As a variant of Graph Neural Networks (GNNs), Unfolded GNNs offer enhanced interpretability and flexibility over traditional designs. Nevertheless, they still suffer from scalability challenges when it comes to the training cost. Although many methods have been proposed to address the scalability issues, they mostly focus on per-iteration efficiency, without worst-case convergence guarantees. Moreover, those methods typically add components to or modify the original model, thus possibly breaking the interpretability of Unfolded GNNs. In this paper, we propose HERTA: a High-Efficiency and Rigorous Training Algorithm for Unfolded GNNs that accelerates the whole training process, achieving a nearly-linear time worst-case training guarantee. Crucially, HERTA converges to the optimum of the original model, thus preserving the interpretability of Unfolded GNNs. Additionally, as a byproduct of HERTA, we propose a new spectral sparsification method applicable to normalized and regularized graph Laplacians that ensures tighter bounds for our algorithm than existing spectral sparsifiers do. Experiments on real-world datasets verify the superiority of HERTA as well as its adaptability to various loss functions and optimizers.
|
[
"['Yongyi Yang' 'Jiaming Yang' 'Wei Hu' 'Michał Dereziński']"
] |
null | null |
2403.18147
| null | null |
http://arxiv.org/pdf/2403.18147v1
|
2024-03-26T23:14:15Z
|
2024-03-26T23:14:15Z
|
Divide, Conquer, Combine Bayesian Decision Tree Sampling
|
Decision trees are commonly used predictive models due to their flexibility and interpretability. This paper is directed at quantifying the uncertainty of decision tree predictions by employing a Bayesian inference approach. This is challenging because these approaches need to explore both the tree structure space and the space of decision parameters associated with each tree structure. This has been handled by using Markov Chain Monte Carlo (MCMC) methods, where a Markov Chain is constructed to provide samples from the desired Bayesian estimate. Importantly, the structure and the decision parameters are tightly coupled; small changes in the tree structure can demand vastly different decision parameters to provide accurate predictions. A challenge for existing MCMC approaches is proposing joint changes in both the tree structure and the decision parameters that result in efficient sampling. This paper takes a different approach, where each distinct tree structure is associated with a unique set of decision parameters. The proposed approach, entitled DCC-Tree, is inspired by the work in Zhou et al. [23] for probabilistic programs and Cochrane et al. [4] for Hamiltonian Monte Carlo (HMC) based sampling for decision trees. Results show that DCC-Tree performs comparably to other HMC-based methods and better than existing Bayesian tree methods while improving on consistency and reducing the per-proposal complexity.
|
[
"['Jodie A. Cochrane' 'Adrian Wills' 'Sarah J. Johnson']"
] |
null | null |
2403.18159
| null | null |
http://arxiv.org/pdf/2403.18159v2
|
2024-03-28T08:22:31Z
|
2024-03-26T23:51:44Z
|
Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal
Propagation Analysis for Large Language Models
|
Large generative models such as large language models (LLMs) and diffusion models have revolutionized the fields of NLP and computer vision respectively. However, their slow inference, high computation and memory requirement makes it challenging to deploy them on edge devices. In this study, we propose a light-weight quantization aware fine tuning technique using knowledge distillation (KD-QAT) to improve the performance of 4-bit weight quantized LLMs using commonly available datasets to realize a popular language use case, on device chat applications. To improve this paradigm of finetuning, as main contributions, we provide insights into stability of KD-QAT by empirically studying the gradient propagation during training to better understand the vulnerabilities of KD-QAT based approaches to low-bit quantization errors. Based on our insights, we propose ov-freeze, a simple technique to stabilize the KD-QAT process. Finally, we experiment with the popular 7B LLaMAv2-Chat model at 4-bit quantization level and demonstrate that ov-freeze results in near floating point precision performance, i.e., less than 0.7% loss of accuracy on Commonsense Reasoning benchmarks.
|
[
"['Kartikeya Bhardwaj' 'Nilesh Prasad Pandey' 'Sweta Priyadarshi'\n 'Kyunggeun Lee' 'Jun Ma' 'Harris Teague']"
] |
null | null |
2403.18176
| null | null |
http://arxiv.org/pdf/2403.18176v1
|
2024-03-27T01:05:45Z
|
2024-03-27T01:05:45Z
|
Mistake, Manipulation and Margin Guarantees in Online Strategic
Classification
|
We consider an online strategic classification problem where each arriving agent can manipulate their true feature vector to obtain a positive predicted label, while incurring a cost that depends on the amount of manipulation. The learner seeks to predict the agent's true label given access to only the manipulated features. After the learner releases their prediction, the agent's true label is revealed. Previous algorithms such as the strategic perceptron guarantee finitely many mistakes under a margin assumption on agents' true feature vectors. However, these are not guaranteed to encourage agents to be truthful. Promoting truthfulness is intimately linked to obtaining adequate margin on the predictions, thus we provide two new algorithms aimed at recovering the maximum margin classifier in the presence of strategic agent behavior. We prove convergence, finite mistake and finite manipulation guarantees for a variety of agent cost structures. We also provide generalized versions of the strategic perceptron with mistake guarantees for different costs. Our numerical study on real and synthetic data demonstrates that the new algorithms outperform previous ones in terms of margin, number of manipulation and number of mistakes.
|
[
"['Lingqing Shen' 'Nam Ho-Nguyen' 'Khanh-Hung Giang-Tran'\n 'Fatma Kılınç-Karzan']"
] |
null | null |
2403.18181
| null | null |
http://arxiv.org/pdf/2403.18181v1
|
2024-03-27T01:18:00Z
|
2024-03-27T01:18:00Z
|
Compression of the Koopman matrix for nonlinear physical models via
hierarchical clustering
|
Machine learning methods allow the prediction of nonlinear dynamical systems from data alone. The Koopman operator is one of them, which enables us to employ linear analysis for nonlinear dynamical systems. The linear characteristics of the Koopman operator are hopeful to understand the nonlinear dynamics and perform rapid predictions. The extended dynamic mode decomposition (EDMD) is one of the methods to approximate the Koopman operator as a finite-dimensional matrix. In this work, we propose a method to compress the Koopman matrix using hierarchical clustering. Numerical demonstrations for the cart-pole model and comparisons with the conventional singular value decomposition (SVD) are shown; the results indicate that the hierarchical clustering performs better than the naive SVD compressions.
|
[
"['Tomoya Nishikata' 'Jun Ohkubo']"
] |
null | null |
2403.18192
| null | null |
http://arxiv.org/pdf/2403.18192v1
|
2024-03-27T02:00:18Z
|
2024-03-27T02:00:18Z
|
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced
Samples
|
Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-batches with optimizers, where each sample is randomly selected with equal probability when constructing mini-batches. However, the intrinsic class imbalance in multi-label data may bias the model towards majority labels, since samples relevant to minority labels may be underrepresented in each mini-batch. Meanwhile, during the training process, we observe that instances associated with minority labels tend to induce greater losses. Existing heuristic batch selection methods, such as priority selection of samples with high contribution to the objective function, i.e., samples with high loss, have been proven to accelerate convergence while reducing the loss and test error in single-label data. However, batch selection methods have not yet been applied and validated in multi-label data. In this study, we introduce a simple yet effective adaptive batch selection algorithm tailored to multi-label deep learning models. It adaptively selects each batch by prioritizing hard samples related to minority labels. A variant of our method also takes informative label correlations into consideration. Comprehensive experiments combining five multi-label deep learning models on thirteen benchmark datasets show that our method converges faster and performs better than random batch selection.
|
[
"['Ao Zhou' 'Bin Liu' 'Jin Wang' 'Grigorios Tsoumakas']"
] |
null | null |
2403.18196
| null | null |
http://arxiv.org/pdf/2403.18196v1
|
2024-03-27T02:13:20Z
|
2024-03-27T02:13:20Z
|
Looking Beyond What You See: An Empirical Analysis on Subgroup
Intersectional Fairness for Multi-label Chest X-ray Classification Using
Social Determinants of Racial Health Inequities
|
There has been significant progress in implementing deep learning models in disease diagnosis using chest X- rays. Despite these advancements, inherent biases in these models can lead to disparities in prediction accuracy across protected groups. In this study, we propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups in high-dimensional chest X- ray multi-label classification. Transcending traditional protected attributes, we consider complex interactions within social determinants, enabling a more granular benchmark and evaluation of fairness. We present a simple and robust method that involves retraining the last classification layer of pre-trained models using a balanced dataset across groups. Additionally, we account for fairness constraints and integrate class-balanced fine-tuning for multi-label settings. The evaluation of our method on the MIMIC-CXR dataset demonstrates that our framework achieves an optimal tradeoff between accuracy and fairness compared to baseline methods.
|
[
"['Dana Moukheiber' 'Saurabh Mahindre' 'Lama Moukheiber' 'Mira Moukheiber'\n 'Mingchen Gao']"
] |
null | null |
2403.18209
| null | null |
http://arxiv.org/pdf/2403.18209v1
|
2024-03-27T02:41:52Z
|
2024-03-27T02:41:52Z
|
Long and Short-Term Constraints Driven Safe Reinforcement Learning for
Autonomous Driving
|
Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but they still permit unsafe state occurrence, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to guarantee the short-term state safety that the vehicle explores, while the long-term constraint ensures the overall safety of the vehicle throughout the decision-making process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods.
|
[
"['Xuemin Hu' 'Pan Chen' 'Yijun Wen' 'Bo Tang' 'Long Chen']"
] |
null | null |
2403.18211
| null | null |
http://arxiv.org/pdf/2403.18211v1
|
2024-03-27T02:42:52Z
|
2024-03-27T02:42:52Z
|
NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual
Pretraining and Multi-level Modulation
|
Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality images, capture only a limited aspect of the complex information in fMRI signals and offer little detailed control over image creation. In contrast, this paper proposes to directly modulate the generation process of diffusion models using fMRI signals. Our approach, NeuroPictor, divides the fMRI-to-image process into three steps: i) fMRI calibrated-encoding, to tackle multi-individual pre-training for a shared latent space to minimize individual difference and enable the subsequent cross-subject training; ii) fMRI-to-image cross-subject pre-training, perceptually learning to guide diffusion model with high- and low-level conditions across different individuals; iii) fMRI-to-image single-subject refining, similar with step ii but focus on adapting to particular individual. NeuroPictor extracts high-level semantic features from fMRI signals that characterizing the visual stimulus and incrementally fine-tunes the diffusion model with a low-level manipulation network to provide precise structural instructions. By training with over 60,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity, particularly in the within-subject setting, as evidenced in benchmark datasets. Project page: https://jingyanghuo.github.io/neuropictor/.
|
[
"['Jingyang Huo' 'Yikai Wang' 'Xuelin Qian' 'Yun Wang' 'Chong Li'\n 'Jianfeng Feng' 'Yanwei Fu']"
] |
null | null |
2403.18216
| null | null |
http://arxiv.org/pdf/2403.18216v1
|
2024-03-27T02:59:04Z
|
2024-03-27T02:59:04Z
|
Minimax Optimal Fair Classification with Bounded Demographic Disparity
|
Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness. While extensive research aims to reduce disparity, the effect of using a emph{finite dataset} -- as opposed to the entire population -- remains unclear. This paper explores the statistical foundations of fair binary classification with two protected groups, focusing on controlling demographic disparity, defined as the difference in acceptance rates between the groups. Although fairness may come at the cost of accuracy even with infinite data, we show that using a finite sample incurs additional costs due to the need to estimate group-specific acceptance thresholds. We study the minimax optimal classification error while constraining demographic disparity to a user-specified threshold. To quantify the impact of fairness constraints, we introduce a novel measure called emph{fairness-aware excess risk} and derive a minimax lower bound on this measure that all classifiers must satisfy. Furthermore, we propose FairBayes-DDP+, a group-wise thresholding method with an offset that we show attains the minimax lower bound. Our lower bound proofs involve several innovations. Experiments support that FairBayes-DDP+ controls disparity at the user-specified level, while being faster and having a more favorable fairness-accuracy tradeoff than several baselines.
|
[
"['Xianli Zeng' 'Guang Cheng' 'Edgar Dobriban']"
] |
null | null |
2403.18219
| null | null |
http://arxiv.org/pdf/2403.18219v1
|
2024-03-27T03:07:18Z
|
2024-03-27T03:07:18Z
|
From Two-Dimensional to Three-Dimensional Environment with Q-Learning:
Modeling Autonomous Navigation with Reinforcement Learning and no Libraries
|
Reinforcement learning (RL) algorithms have become indispensable tools in artificial intelligence, empowering agents to acquire optimal decision-making policies through interactions with their environment and feedback mechanisms. This study explores the performance of RL agents in both two-dimensional (2D) and three-dimensional (3D) environments, aiming to research the dynamics of learning across different spatial dimensions. A key aspect of this investigation is the absence of pre-made libraries for learning, with the algorithm developed exclusively through computational mathematics. The methodological framework centers on RL principles, employing a Q-learning agent class and distinct environment classes tailored to each spatial dimension. The research aims to address the question: How do reinforcement learning agents adapt and perform in environments of varying spatial dimensions, particularly in 2D and 3D settings? Through empirical analysis, the study evaluates agents' learning trajectories and adaptation processes, revealing insights into the efficacy of RL algorithms in navigating complex, multi-dimensional spaces. Reflections on the findings prompt considerations for future research, particularly in understanding the dynamics of learning in higher-dimensional environments.
|
[
"['Ergon Cugler de Moraes Silva']"
] |
null | null |
2403.18222
| null | null |
http://arxiv.org/pdf/2403.18222v1
|
2024-03-27T03:19:36Z
|
2024-03-27T03:19:36Z
|
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned
Imitation Learning Policies
|
Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to new environment conditions remains a major challenge. Toward addressing this challenge, we propose a novel approach for uncertainty-aware deployment of pre-trained language-conditioned imitation learning agents. Specifically, we use temperature scaling to calibrate these models and exploit the calibrated model to make uncertainty-aware decisions by aggregating the local information of candidate actions. We implement our approach in simulation using three such pre-trained models, and showcase its potential to significantly enhance task completion rates. The accompanying code is accessible at the link: https://github.com/BobWu1998/uncertainty_quant_all.git
|
[
"['Bo Wu' 'Bruce D. Lee' 'Kostas Daniilidis' 'Bernadette Bucher'\n 'Nikolai Matni']"
] |
null | null |
2403.18223
| null | null |
http://arxiv.org/pdf/2403.18223v1
|
2024-03-27T03:25:45Z
|
2024-03-27T03:25:45Z
|
A Transformer-Based Framework for Payload Malware Detection and
Classification
|
As malicious cyber threats become more sophisticated in breaching computer networks, the need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as Deep Packet Inspection (DPI) have been introduced to allow IDSs analyze the content of network packets, providing more context for identifying potential threats. IDSs traditionally rely on using anomaly-based and signature-based detection techniques to detect unrecognized and suspicious activity. Deep learning techniques have shown great potential in DPI for IDSs due to their efficiency in learning intricate patterns from the packet content being transmitted through the network. In this paper, we propose a revolutionary DPI algorithm based on transformers adapted for the purpose of detecting malicious traffic with a classifier head. Transformers learn the complex content of sequence data and generalize them well to similar scenarios thanks to their self-attention mechanism. Our proposed method uses the raw payload bytes that represent the packet contents and is deployed as man-in-the-middle. The payload bytes are used to detect malicious packets and classify their types. Experimental results on the UNSW-NB15 and CIC-IOT23 datasets demonstrate that our transformer-based model is effective in distinguishing malicious from benign traffic in the test dataset, attaining an average accuracy of 79% using binary classification and 72% on the multi-classification experiment, both using solely payload bytes.
|
[
"['Kyle Stein' 'Arash Mahyari' 'Guillermo Francia III' 'Eman El-Sheikh']"
] |
null | null |
2403.18228
| null | null |
http://arxiv.org/pdf/2403.18228v1
|
2024-03-27T03:31:16Z
|
2024-03-27T03:31:16Z
|
Fourier or Wavelet bases as counterpart self-attention in spikformer for
efficient visual classification
|
Energy-efficient spikformer has been proposed by integrating the biologically plausible spiking neural network (SNN) and artificial Transformer, whereby the Spiking Self-Attention (SSA) is used to achieve both higher accuracy and lower computational cost. However, it seems that self-attention is not always necessary, especially in sparse spike-form calculation manners. In this paper, we innovatively replace vanilla SSA (using dynamic bases calculating from Query and Key) with spike-form Fourier Transform, Wavelet Transform, and their combinations (using fixed triangular or wavelets bases), based on a key hypothesis that both of them use a set of basis functions for information transformation. Hence, the Fourier-or-Wavelet-based spikformer (FWformer) is proposed and verified in visual classification tasks, including both static image and event-based video datasets. The FWformer can achieve comparable or even higher accuracies ($0.4%$-$1.5%$), higher running speed ($9%$-$51%$ for training and $19%$-$70%$ for inference), reduced theoretical energy consumption ($20%$-$25%$), and reduced GPU memory usage ($4%$-$26%$), compared to the standard spikformer. Our result indicates the continuous refinement of new Transformers, that are inspired either by biological discovery (spike-form), or information theory (Fourier or Wavelet Transform), is promising.
|
[
"['Qingyu Wang' 'Duzhen Zhang' 'Tilelin Zhang' 'Bo Xu']"
] |
null | null |
2403.18233
| null | null |
http://arxiv.org/abs/2403.18233v1
|
2024-03-27T03:39:57Z
|
2024-03-27T03:39:57Z
|
Benchmarking Image Transformers for Prostate Cancer Detection from
Ultrasound Data
|
PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) to detect cancer in small regions of interest (ROI) along a needle trace region. However, this approach suffers from weak labelling, since the ground-truth histopathology labels do not describe the properties of individual ROIs. Recently, multi-scale approaches have sought to mitigate this issue by combining the context awareness of transformers with a CNN feature extractor to detect cancer from multiple ROIs using multiple-instance learning (MIL). In this work, we present a detailed study of several image transformer architectures for both ROI-scale and multi-scale classification, and a comparison of the performance of CNNs and transformers for ultrasound-based prostate cancer classification. We also design a novel multi-objective learning strategy that combines both ROI and core predictions to further mitigate label noise. METHODS: We evaluate 3 image transformers on ROI-scale cancer classification, then use the strongest model to tune a multi-scale classifier with MIL. We train our MIL models using our novel multi-objective learning strategy and compare our results to existing baselines. RESULTS: We find that for both ROI-scale and multi-scale PCa detection, image transformer backbones lag behind their CNN counterparts. This deficit in performance is even more noticeable for larger models. When using multi-objective learning, we can improve performance of MIL, with a 77.9% AUROC, a sensitivity of 75.9%, and a specificity of 66.3%. CONCLUSION: Convolutional networks are better suited for modelling sparse datasets of prostate ultrasounds, producing more robust features than transformers in PCa detection. Multi-scale methods remain the best architecture for this task, with multi-objective learning presenting an effective way to improve performance.
|
[
"['Mohamed Harmanani' 'Paul F. R. Wilson' 'Fahimeh Fooladgar'\n 'Amoon Jamzad' 'Mahdi Gilany' 'Minh Nguyen Nhat To' 'Brian Wodlinger'\n 'Purang Abolmaesumi' 'Parvin Mousavi']"
] |
null | null |
2403.18241
| null | null |
http://arxiv.org/pdf/2403.18241v2
|
2024-07-12T07:30:00Z
|
2024-03-27T04:09:34Z
|
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion,
Reconstruction, and Generation
|
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized components, treating each element in isolation without considering spatial consistency. As a result, these approaches exhibit limited versatility in 3D data representation and shape generation, hindering their ability to generate highly diverse 3D shapes that comply with the specified constraints. In this paper, we introduce a novel spatial-aware 3D shape generation framework that leverages 2D plane representations for enhanced 3D shape modeling. To ensure spatial coherence and reduce memory usage, we incorporate a hybrid shape representation technique that directly learns a continuous signed distance field representation of the 3D shape using orthogonal 2D planes. Additionally, we meticulously enforce spatial correspondences across distinct planes using a transformer-based autoencoder structure, promoting the preservation of spatial relationships in the generated 3D shapes. This yields an algorithm that consistently outperforms state-of-the-art 3D shape generation methods on various tasks, including unconditional shape generation, multi-modal shape completion, single-view reconstruction, and text-to-shape synthesis. Our project page is available at https://weizheliu.github.io/NeuSDFusion/ .
|
[
"['Ruikai Cui' 'Weizhe Liu' 'Weixuan Sun' 'Senbo Wang' 'Taizhang Shang'\n 'Yang Li' 'Xibin Song' 'Han Yan' 'Zhennan Wu' 'Shenzhou Chen'\n 'Hongdong Li' 'Pan Ji']"
] |
null | null |
2403.18252
| null | null |
http://arxiv.org/pdf/2403.18252v2
|
2024-06-17T09:57:09Z
|
2024-03-27T04:49:23Z
|
Beyond Embeddings: The Promise of Visual Table in Visual Reasoning
|
Visual representation learning has been a cornerstone in computer vision, involving typical forms such as visual embeddings, structural symbols, and text-based representations. Despite the success of CLIP-type visual embeddings, they often lack access to world knowledge critical for visual reasoning. In this work, we propose Visual Table, a novel form of visual representation tailored for visual reasoning. Visual tables are constructed as hierarchical descriptions of visual scenes, featuring a scene description and multiple object-centric descriptions covering categories, attributes, and knowledge. Thanks to the structural and textual formats, visual tables offer unique advantages over mere visual embeddings, such as interpretability and controllable editing. Furthermore, they deliver instance-level world knowledge and detailed attributes that are essential for visual reasoning. To create visual tables, we develop a generator trained on the dataset with collected, small-scale annotations. Extensive results on 11 visual reasoning benchmarks demonstrate that the generated visual tables significantly outperform previous structural and text-based representations. Moreover, they consistently enhance state-of-the-art multimodal large language models across diverse benchmarks, showcasing their potential for advancing visual reasoning tasks. Our code is available at https://github.com/LaVi-Lab/Visual-Table.
|
[
"['Yiwu Zhong' 'Zi-Yuan Hu' 'Michael R. Lyu' 'Liwei Wang']"
] |
null | null |
2403.18266
| null | null |
http://arxiv.org/pdf/2403.18266v1
|
2024-03-27T05:38:48Z
|
2024-03-27T05:38:48Z
|
Branch-Tuning: Balancing Stability and Plasticity for Continual
Self-Supervised Learning
|
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications continually integrate new content, the high computational and resource demands of SSL necessitate continual learning rather than complete retraining. This poses a challenge in striking a balance between stability and plasticity when adapting to new information. In this paper, we employ Centered Kernel Alignment for quantitatively analyzing model stability and plasticity, revealing the critical roles of batch normalization layers for stability and convolutional layers for plasticity. Motivated by this, we propose Branch-tuning, an efficient and straightforward method that achieves a balance between stability and plasticity in continual SSL. Branch-tuning consists of branch expansion and compression, and can be easily applied to various SSL methods without the need of modifying the original methods, retaining old data or models. We validate our method through incremental experiments on various benchmark datasets, demonstrating its effectiveness and practical value in real-world scenarios. We hope our work offers new insights for future continual self-supervised learning research. The code will be made publicly available.
|
[
"['Wenzhuo Liu' 'Fei Zhu' 'Cheng-Lin Liu']"
] |
null | null |
2403.18267
| null | null |
http://arxiv.org/pdf/2403.18267v1
|
2024-03-27T05:41:50Z
|
2024-03-27T05:41:50Z
|
DSF-GAN: DownStream Feedback Generative Adversarial Network
|
Utility and privacy are two crucial measurements of the quality of synthetic tabular data. While significant advancements have been made in privacy measures, generating synthetic samples with high utility remains challenging. To enhance the utility of synthetic samples, we propose a novel architecture called the DownStream Feedback Generative Adversarial Network (DSF-GAN). This approach incorporates feedback from a downstream prediction model during training to augment the generator's loss function with valuable information. Thus, DSF-GAN utilizes a downstream prediction task to enhance the utility of synthetic samples. To evaluate our method, we tested it using two popular datasets. Our experiments demonstrate improved model performance when training on synthetic samples generated by DSF-GAN, compared to those generated by the same GAN architecture without feedback. The evaluation was conducted on the same validation set comprising real samples. All code and datasets used in this research will be made openly available for ease of reproduction.
|
[
"['Oriel Perets' 'Nadav Rappoport']"
] |
null | null |
2403.18269
| null | null |
http://arxiv.org/pdf/2403.18269v1
|
2024-03-27T05:50:23Z
|
2024-03-27T05:50:23Z
|
Clustering Change Sign Detection by Fusing Mixture Complexity
|
This paper proposes an early detection method for cluster structural changes. Cluster structure refers to discrete structural characteristics, such as the number of clusters, when data are represented using finite mixture models, such as Gaussian mixture models. We focused on scenarios in which the cluster structure gradually changed over time. For finite mixture models, the concept of mixture complexity (MC) measures the continuous cluster size by considering the cluster proportion bias and overlap between clusters. In this paper, we propose MC fusion as an extension of MC to handle situations in which multiple mixture numbers are possible in a finite mixture model. By incorporating the fusion of multiple models, our approach accurately captured the cluster structure during transitional periods of gradual change. Moreover, we introduce a method for detecting changes in the cluster structure by examining the transition of MC fusion. We demonstrate the effectiveness of our method through empirical analysis using both artificial and real-world datasets.
|
[
"['Kento Urano' 'Ryo Yuki' 'Kenji Yamanishi']"
] |
null | null |
2403.18286
| null | null |
http://arxiv.org/pdf/2403.18286v1
|
2024-03-27T06:25:40Z
|
2024-03-27T06:25:40Z
|
Few-Shot Recalibration of Language Models
|
Recent work has uncovered promising ways to extract well-calibrated confidence estimates from language models (LMs), where the model's confidence score reflects how likely it is to be correct. However, while LMs may appear well-calibrated over broad distributions, this often hides significant miscalibration within narrower slices (e.g., systemic over-confidence in math can balance out systemic under-confidence in history, yielding perfect calibration in aggregate). To attain well-calibrated confidence estimates for any slice of a distribution, we propose a new framework for few-shot slice-specific recalibration. Specifically, we train a recalibration model that takes in a few unlabeled examples from any given slice and predicts a curve that remaps confidence scores to be more accurate for that slice. Our trained model can recalibrate for arbitrary new slices, without using any labeled data from that slice. This enables us to identify domain-specific confidence thresholds above which the LM's predictions can be trusted, and below which it should abstain. Experiments show that our few-shot recalibrator consistently outperforms existing calibration methods, for instance improving calibration error for PaLM2-Large on MMLU by 16%, as compared to temperature scaling.
|
[
"['Xiang Lisa Li' 'Urvashi Khandelwal' 'Kelvin Guu']"
] |
null | null |
2403.18296
| null | null |
http://arxiv.org/pdf/2403.18296v2
|
2024-05-14T11:44:44Z
|
2024-03-27T06:46:59Z
|
GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic
Communication Paradigm
|
Traditional approaches to semantic communication tasks rely on the knowledge of the signal-to-noise ratio (SNR) to mitigate channel noise. Moreover, these methods necessitate training under specific SNR conditions, entailing considerable time and computational resources. In this paper, we propose GeNet, a Graph Neural Network (GNN)-based paradigm for semantic communication aimed at combating noise, thereby facilitating Task-Oriented Communication (TOC). We propose a novel approach where we first transform the input data image into graph structures. Then we leverage a GNN-based encoder to extract semantic information from the source data. This extracted semantic information is then transmitted through the channel. At the receiver's end, a GNN-based decoder is utilized to reconstruct the relevant semantic information from the source data for TOC. Through experimental evaluation, we show GeNet's effectiveness in anti-noise TOC while decoupling the SNR dependency. We further evaluate GeNet's performance by varying the number of nodes, revealing its versatility as a new paradigm for semantic communication. Additionally, we show GeNet's robustness to geometric transformations by testing it with different rotation angles, without resorting to data augmentation.
|
[
"['Chunhang Zheng' 'Kechao Cai']"
] |
null | null |
2403.18301
| null | null |
http://arxiv.org/pdf/2403.18301v1
|
2024-03-27T06:55:23Z
|
2024-03-27T06:55:23Z
|
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
|
The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, which can effectively utilize the colossal amount of data to train models. However, before deploying these models in the real world, these must be strictly evaluated on performance measures like worst-case recall and satisfy constraints such as fairness. We find that current state-of-the-art empirical techniques offer sub-optimal performance on these practical, non-decomposable performance objectives. On the other hand, the theoretical techniques necessitate training a new model from scratch for each performance objective. To bridge the gap, we propose SelMix, a selective mixup-based inexpensive fine-tuning technique for pre-trained models, to optimize for the desired objective. The core idea of our framework is to determine a sampling distribution to perform a mixup of features between samples from particular classes such that it optimizes the given objective. We comprehensively evaluate our technique against the existing empirical and theoretically principled methods on standard benchmark datasets for imbalanced classification. We find that proposed SelMix fine-tuning significantly improves the performance for various practical non-decomposable objectives across benchmarks.
|
[
"['Shrinivas Ramasubramanian' 'Harsh Rangwani' 'Sho Takemori'\n 'Kunal Samanta' 'Yuhei Umeda' 'Venkatesh Babu Radhakrishnan']"
] |
null | null |
2403.18302
| null | null |
http://arxiv.org/pdf/2403.18302v1
|
2024-03-27T06:58:01Z
|
2024-03-27T06:58:01Z
|
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using
SDO/HMI Data and an Attention-Aided Convolutional Neural Network
|
Image super-resolution has been an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network (CNN) for solar image super-resolution. Our method, named SolarCNN, aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). The ground-truth labels used for training SolarCNN are the LOS magnetograms collected by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Solar ARs consist of strong magnetic fields in which magnetic energy can suddenly be released to produce extreme space weather events, such as solar flares, coronal mass ejections, and solar energetic particles. SOHO/MDI covers Solar Cycle 23, which is stronger with more eruptive events than Cycle 24. Enhanced SOHO/MDI magnetograms allow for better understanding and forecasting of violent events of space weather. Experimental results show that SolarCNN improves the quality of SOHO/MDI magnetograms in terms of the structural similarity index measure (SSIM), Pearson's correlation coefficient (PCC), and the peak signal-to-noise ratio (PSNR).
|
[
"['Chunhui Xu' 'Jason T. L. Wang' 'Haimin Wang' 'Haodi Jiang' 'Qin Li'\n 'Yasser Abduallah' 'Yan Xu']"
] |
null | null |
2403.18310
| null | null |
http://arxiv.org/pdf/2403.18310v1
|
2024-03-27T07:22:32Z
|
2024-03-27T07:22:32Z
|
A thermodynamically consistent physics-informed deep learning material
model for short fiber/polymer nanocomposites
|
This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies under various ambient conditions. The deep-learning model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive model. To accomplish this, a long short-term memory network is combined with a feed-forward neural network to predict internal variables required for characterizing the internal dissipation of the nanocomposite materials. In addition, another feed-forward neural network is used to indicate the free-energy function, which enables defining the thermodynamic state of the entire system. The PIDL model is initially developed for the three-dimensional case by generating synthetic data from a classical constitutive model. The model is then trained by extracting the data directly from cyclic loading-unloading experimental tests. Numerical examples show that the PIDL model can accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions.
|
[
"['Betim Bahtiri' 'Behrouz Arash' 'Sven Scheffler' 'Maximilian Jux'\n 'Raimund Rolfes']"
] |
null | null |
2403.18316
| null | null |
http://arxiv.org/pdf/2403.18316v1
|
2024-03-27T07:38:36Z
|
2024-03-27T07:38:36Z
|
Multi-Modal Contrastive Learning for Online Clinical Time-Series
Applications
|
Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities in supervised settings, we apply advanced self-supervised multi-modal contrastive learning techniques to ICU data, specifically focusing on clinical notes and time-series for clinically relevant online prediction tasks. We introduce a loss function Multi-Modal Neighborhood Contrastive Loss (MM-NCL), a soft neighborhood function, and showcase the excellent linear probe and zero-shot performance of our approach.
|
[
"['Fabian Baldenweg' 'Manuel Burger' 'Gunnar Rätsch' 'Rita Kuznetsova']"
] |
null | null |
2403.18321
| null | null |
http://arxiv.org/abs/2403.18321v1
|
2024-03-27T07:50:45Z
|
2024-03-27T07:50:45Z
|
Implementation of the Principal Component Analysis onto High-Performance
Computer Facilities for Hyperspectral Dimensionality Reduction: Results and
Comparisons
|
Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU) and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA)-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.
|
[
"['E. Martel' 'R. Lazcano' 'J. Lopez' 'D. Madroñal' 'R. Salvador'\n 'S. Lopez' 'E. Juarez' 'R. Guerra' 'C. Sanz' 'R. Sarmiento']"
] |
null | null |
2403.18322
| null | null |
http://arxiv.org/pdf/2403.18322v1
|
2024-03-27T07:52:10Z
|
2024-03-27T07:52:10Z
|
Quantum Algorithms: A New Frontier in Financial Crime Prevention
|
Financial crimes fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as powerful solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods. Furthermore, the paper illustrates how quantum computing can support enhanced financial risk management analysis. Financial institutions can improve their ability to identify and mitigate risks, leading to more robust risk management strategies by exploiting the quantum advantage. This research underscores the transformative impact of quantum algorithms on financial risk management. By embracing quantum technologies, organisations can enhance their capabilities to combat evolving threats and ensure the integrity and stability of financial systems.
|
[
"['Abraham Itzhak Weinberg' 'Alessio Faccia']"
] |
null | null |
2403.18326
| null | null |
http://arxiv.org/pdf/2403.18326v1
|
2024-03-27T08:07:07Z
|
2024-03-27T08:07:07Z
|
Privacy-Preserving Distributed Nonnegative Matrix Factorization
|
Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a decentralized manner over ad-hoc networks introduces privacy concerns due to the conventional approach of sharing raw data among network agents. To address this, we propose a privacy-preserving algorithm for fully-distributed NMF that decomposes a distributed large data matrix into left and right matrix factors while safeguarding each agent's local data privacy. It facilitates collaborative estimation of the left matrix factor among agents and enables them to estimate their respective right factors without exposing raw data. To ensure data privacy, we secure information exchanges between neighboring agents utilizing the Paillier cryptosystem, a probabilistic asymmetric algorithm for public-key cryptography that allows computations on encrypted data without decryption. Simulation results conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm in achieving privacy-preserving distributed NMF over ad-hoc networks.
|
[
"['Ehsan Lari' 'Reza Arablouei' 'Stefan Werner']"
] |
null | null |
2403.18330
| null | null |
http://arxiv.org/pdf/2403.18330v1
|
2024-03-27T08:11:25Z
|
2024-03-27T08:11:25Z
|
Tracking-Assisted Object Detection with Event Cameras
|
Event-based object detection has recently garnered attention in the computer vision community due to the exceptional properties of event cameras, such as high dynamic range and no motion blur. However, feature asynchronism and sparsity cause invisible objects due to no relative motion to the camera, posing a significant challenge in the task. Prior works have studied various memory mechanisms to preserve as many features as possible at the current time, guided by temporal clues. While these implicit-learned memories retain some short-term information, they still struggle to preserve long-term features effectively. In this paper, we consider those invisible objects as pseudo-occluded objects and aim to reveal their features. Firstly, we introduce visibility attribute of objects and contribute an auto-labeling algorithm to append additional visibility labels on an existing event camera dataset. Secondly, we exploit tracking strategies for pseudo-occluded objects to maintain their permanence and retain their bounding boxes, even when features have not been available for a very long time. These strategies can be treated as an explicit-learned memory guided by the tracking objective to record the displacements of objects across frames. Lastly, we propose a spatio-temporal feature aggregation module to enrich the latent features and a consistency loss to increase the robustness of the overall pipeline. We conduct comprehensive experiments to verify our method's effectiveness where still objects are retained but real occluded objects are discarded. The results demonstrate that (1) the additional visibility labels can assist in supervised training, and (2) our method outperforms state-of-the-art approaches with a significant improvement of 7.9% absolute mAP.
|
[
"['Ting-Kang Yen' 'Igor Morawski' 'Shusil Dangi' 'Kai He' 'Chung-Yi Lin'\n 'Jia-Fong Yeh' 'Hung-Ting Su' 'Winston Hsu']"
] |
null | null |
2403.18336
| null | null |
http://arxiv.org/pdf/2403.18336v1
|
2024-03-27T08:21:01Z
|
2024-03-27T08:21:01Z
|
A Dataset for Pharmacovigilance in German, French, and Japanese:
Annotating Adverse Drug Reactions across Languages
|
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.
|
[
"['Lisa Raithel' 'Hui-Syuan Yeh' 'Shuntaro Yada' 'Cyril Grouin'\n 'Thomas Lavergne' 'Aurélie Névéol' 'Patrick Paroubek' 'Philippe Thomas'\n 'Tomohiro Nishiyama' 'Sebastian Möller' 'Eiji Aramaki' 'Yuji Matsumoto'\n 'Roland Roller' 'Pierre Zweigenbaum']"
] |
null | null |
2403.18337
| null | null |
http://arxiv.org/abs/2403.18337v1
|
2024-03-27T08:21:41Z
|
2024-03-27T08:21:41Z
|
Macroscale fracture surface segmentation via semi-supervised learning
considering the structural similarity
|
To this date the safety assessment of materials, used for example in the nuclear power sector, commonly relies on a fracture mechanical analysis utilizing macroscopic concepts, where a global load quantity K or J is compared to the materials fracture toughness curve. Part of the experimental effort involved in these concepts is dedicated to the quantitative analysis of fracture surfaces. Within the scope of this study a methodology for the semi-supervised training of deep learning models for fracture surface segmentation on a macroscopic level was established. Therefore, three distinct and unique datasets were created to analyze the influence of structural similarity on the segmentation capability. The structural similarity differs due to the assessed materials and specimen, as well as imaging-induced variance due to fluctuations in image acquisition in different laboratories. The datasets correspond to typical isolated laboratory conditions, complex real-world circumstances, and a curated subset of the two. We implemented a weak-to-strong consistency regularization for semi-supervised learning. On the heterogeneous dataset we were able to train robust and well-generalizing models that learned feature representations from images across different domains without observing a significant drop in prediction quality. Furthermore, our approach reduced the number of labeled images required for training by a factor of 6. To demonstrate the success of our method and the benefit of our approach for the fracture mechanics assessment, we utilized the models for initial crack size measurements with the area average method. For the laboratory setting, the deep learning assisted measurements proved to have the same quality as manual measurements. For models trained on the heterogeneous dataset, very good measurement accuracies with mean deviations smaller than 1 % could be achieved...
|
[
"['Johannes Rosenberger' 'Johannes Tlatlik' 'Sebastian Münstermann']"
] |
null | null |
2403.18343
| null | null |
http://arxiv.org/pdf/2403.18343v1
|
2024-03-27T08:34:39Z
|
2024-03-27T08:34:39Z
|
The Artificial Neural Twin -- Process Optimization and Continual
Learning in Distributed Process Chains
|
Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasing use of data-driven AI-methods in process models and industrial sensory often requires regular fine-tuning to accommodate distribution drifts. We propose the Artificial Neural Twin, which combines concepts from model predictive control, deep learning, and sensor networks to address these issues. Our approach introduces differentiable data fusion to estimate the state of distributed process steps and their dependence on input data. By treating the interconnected process steps as a quasi neural-network, we can backpropagate loss gradients for process optimization or model fine-tuning to process parameters or AI models respectively. The concept is demonstrated on a virtual machine park simulated in Unity, consisting of bulk material processes in plastic recycling.
|
[
"['Johannes Emmert' 'Ronald Mendez' 'Houman Mirzaalian Dastjerdi'\n 'Christopher Syben' 'Andreas Maier']"
] |
null | null |
2403.18347
| null | null |
http://arxiv.org/pdf/2403.18347v1
|
2024-03-27T08:38:56Z
|
2024-03-27T08:38:56Z
|
A Quantum Fuzzy-based Approach for Real-Time Detection of Solar Coronal
Holes
|
The detection and analysis of the solar coronal holes (CHs) is an important field of study in the domain of solar physics. Mainly, it is required for the proper prediction of the geomagnetic storms which directly or indirectly affect various space and ground-based systems. For the detection of CHs till date, the solar scientist depends on manual hand-drawn approaches. However, with the advancement of image processing technologies, some automated image segmentation methods have been used for the detection of CHs. In-spite of this, fast and accurate detection of CHs are till a major issues. Here in this work, a novel quantum computing-based fast fuzzy c-mean technique has been developed for fast detection of the CHs region. The task has been carried out in two stages, in first stage the solar image has been segmented using a quantum computing based fast fuzzy c-mean (QCFFCM) and in the later stage the CHs has been extracted out from the segmented image based on image morphological operation. In the work, quantum computing has been used to optimize the cost function of the fast fuzzy c-mean (FFCM) algorithm, where quantum approximate optimization algorithm (QAOA) has been used to optimize the quadratic part of the cost function. The proposed method has been tested for 193 AA{} SDO/AIA full-disk solar image datasets and has been compared with the existing techniques. The outcome shows the comparable performance of the proposed method with the existing one within a very lesser time.
|
[
"['Sanmoy Bandyopadhyay' 'Suman Kundu']"
] |
null | null |
2403.18351
| null | null |
http://arxiv.org/pdf/2403.18351v1
|
2024-03-27T08:42:47Z
|
2024-03-27T08:42:47Z
|
Generating Diverse Agricultural Data for Vision-Based Farming
Applications
|
We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture, such as semantic segmentation for autonomous weed control. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.
|
[
"['Mikolaj Cieslak' 'Umabharathi Govindarajan' 'Alejandro Garcia'\n 'Anuradha Chandrashekar' 'Torsten Hädrich' 'Aleksander Mendoza-Drosik'\n 'Dominik L. Michels' 'Sören Pirk' 'Chia-Chun Fu' 'Wojciech Pałubicki']"
] |
null | null |
2403.18355
| null | null |
http://arxiv.org/pdf/2403.18355v1
|
2024-03-27T08:48:16Z
|
2024-03-27T08:48:16Z
|
Supervised Multiple Kernel Learning approaches for multi-omics data
integration
|
Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining.We provide novel MKL approaches based on different kernel fusion strategies.To learn from the meta-kernel of input kernels, we adaptedunsupervised integration algorithms for supervised tasks with support vector machines.We also tested deep learning architectures for kernel fusion and classification.The results show that MKL-based models can compete with more complex, state-of-the-art, supervised multi-omics integrative approaches. Multiple kernel learning offers a natural framework for predictive models in multi-omics genomic data. Our results offer a direction for bio-data mining research and further development of methods for heterogeneous data integration.
|
[
"['Mitja Briscik' 'Gabriele Tazza' 'Marie-Agnes Dillies' 'László Vidács'\n 'Sébastien Dejean']"
] |
null | null |
2403.18364
| null | null |
http://arxiv.org/pdf/2403.18364v1
|
2024-03-27T08:57:15Z
|
2024-03-27T08:57:15Z
|
Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR
|
We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and random traffic arrival. A deep reinforcement learning (DRL) based centralized dynamic scheduler for time-frequency resources is proposed to learn how to schedule the available communication resources among the IIoT UEs. The proposed scheduler leverages an RL framework to adapt to the dynamic changes in the wireless communication system and traffic arrivals. Moreover, a graph-based reduction scheme is proposed to reduce the state and action space of the RL framework to allow fast convergence and a better learning strategy. Simulation results demonstrate the effectiveness of the proposed intelligent scheduler in guaranteeing the expressed intent of IIoT UEs compared to several traditional scheduling schemes, such as round-robin, semi-static, and heuristic approaches. The proposed scheduler also outperforms the contention-free and contention-based schemes in maximizing the number of successfully computed tasks.
|
[
"['Salwa Mostafa' 'Mateus P. Mota' 'Alvaro Valcarce' 'Mehdi Bennis']"
] |
null | null |
2403.18370
| null | null |
http://arxiv.org/pdf/2403.18370v2
|
2024-05-21T16:45:05Z
|
2024-03-27T09:06:36Z
|
Ship in Sight: Diffusion Models for Ship-Image Super Resolution
|
In recent years, remarkable advancements have been achieved in the field of image generation, primarily driven by the escalating demand for high-quality outcomes across various image generation subtasks, such as inpainting, denoising, and super resolution. A major effort is devoted to exploring the application of super-resolution techniques to enhance the quality of low-resolution images. In this context, our method explores in depth the problem of ship image super resolution, which is crucial for coastal and port surveillance. We investigate the opportunity given by the growing interest in text-to-image diffusion models, taking advantage of the prior knowledge that such foundation models have already learned. In particular, we present a diffusion-model-based architecture that leverages text conditioning during training while being class-aware, to best preserve the crucial details of the ships during the generation of the super-resoluted image. Since the specificity of this task and the scarcity availability of off-the-shelf data, we also introduce a large labeled ship dataset scraped from online ship images, mostly from ShipSpottingfootnote{url{www.shipspotting.com}} website. Our method achieves more robust results than other deep learning models previously employed for super resolution, as proven by the multiple experiments performed. Moreover, we investigate how this model can benefit downstream tasks, such as classification and object detection, thus emphasizing practical implementation in a real-world scenario. Experimental results show flexibility, reliability, and impressive performance of the proposed framework over state-of-the-art methods for different tasks. The code is available at: https://github.com/LuigiSigillo/ShipinSight .
|
[
"['Luigi Sigillo' 'Riccardo Fosco Gramaccioni' 'Alessandro Nicolosi'\n 'Danilo Comminiello']"
] |
null | null |
2403.18375
| null | null |
http://arxiv.org/pdf/2403.18375v1
|
2024-03-27T09:14:36Z
|
2024-03-27T09:14:36Z
|
Stragglers-Aware Low-Latency Synchronous Federated Learning via
Layer-Wise Model Updates
|
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and architecture, or resort to asynchronous settings; which all affect the trained model performance under tight training latency constraints. In this work, we propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion. SALF allows stragglers to synchronously convey partial gradients, having each layer of the global model be updated independently with a different contributing set of users. We provide a theoretical analysis, establishing convergence guarantees for the global model under mild assumptions on the distribution of the participating devices, revealing that SALF converges at the same asymptotic rate as FL with no timing limitations. This insight is matched with empirical observations, demonstrating the performance gains of SALF compared to alternative mechanisms mitigating the device heterogeneity gap in FL.
|
[
"['Natalie Lang' 'Alejandro Cohen' 'Nir Shlezinger']"
] |
null | null |
2403.18379
| null | null |
http://arxiv.org/pdf/2403.18379v1
|
2024-03-27T09:17:50Z
|
2024-03-27T09:17:50Z
|
IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining
Useful Life Prediction
|
Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics involved. Recently, attention-based networks, such as Transformers and Informer, have been the popular architecture in time series forecasting. Despite their effectiveness, these models with abundant parameters necessitate substantial training time to unravel temporal patterns. To tackle these challenges, we propose a simple MLP-Mixer-based architecture named 'Intra-Inter Patch Mixer' (IIP-Mixer), which is an architecture based exclusively on multi-layer perceptrons (MLPs), extracting information by mixing operations along both intra-patch and inter-patch dimensions for battery RUL prediction. The proposed IIP-Mixer comprises parallel dual-head mixer layers: the intra-patch mixing MLP, capturing local temporal patterns in the short-term period, and the inter-patch mixing MLP, capturing global temporal patterns in the long-term period. Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed. Our experiments demonstrate that IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time-series frameworks
|
[
"['Guangzai Ye' 'Li Feng' 'Jianlan Guo' 'Yuqiang Chen']"
] |
null | null |
2403.18383
| null | null |
http://arxiv.org/pdf/2403.18383v1
|
2024-03-27T09:21:07Z
|
2024-03-27T09:21:07Z
|
Generative Multi-modal Models are Good Class-Incremental Learners
|
In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models, we would explore replacing discriminative models with generative ones for CIL. However, transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated textual information into the classification of distinct categories. Additionally, it requires formulating the task of CIL within a generative framework. To this end, we propose a novel generative multi-modal model (GMM) framework for class-incremental learning. Our approach directly generates labels for images using an adapted generative model. After obtaining the detailed text, we use a text encoder to extract text features and employ feature matching to determine the most similar label as the classification prediction. In the conventional CIL settings, we achieve significantly better results in long-sequence task scenarios. Under the Few-shot CIL setting, we have improved by at least 14% accuracy over all the current state-of-the-art methods with significantly less forgetting. Our code is available at url{https://github.com/DoubleClass/GMM}.
|
[
"['Xusheng Cao' 'Haori Lu' 'Linlan Huang' 'Xialei Liu' 'Ming-Ming Cheng']"
] |
null | null |
2403.18393
| null | null |
http://arxiv.org/pdf/2403.18393v2
|
2024-04-03T06:26:05Z
|
2024-03-27T09:30:50Z
|
Tensor-based Graph Learning with Consistency and Specificity for
Multi-view Clustering
|
In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering. However, they are confronted with two limitations. Firstly, they often rely on Euclidean distance to measure similarity when constructing the adaptive neighbor graph, which proves inadequate in capturing the intrinsic structure among data points in practice. Secondly, most of these methods focus solely on consensus graph, ignoring unique information from each view. Although a few graph-based studies have considered using specific information as well, the modelling approach employed does not exclude the noise impact from the specific component. To this end, we propose a novel tensor-based multi-view graph learning framework that simultaneously considers consistency and specificity, while effectively eliminating the influence of noise. Specifically, we calculate similarity distance on the Stiefel manifold to preserve the intrinsic properties of data. By making an assumption that the learned neighbor graph of each view comprises a consistent part, a specific part, and a noise part, we formulate a new tensor-based target graph learning paradigm for noise-free graph fusion. Owing to the benefits of tensor singular value decomposition (t-SVD) in uncovering high-order correlations, this model is capable of achieving a complete understanding of the target graph. Furthermore, we derive an algorithm to address the optimization problem. Experiments on six datasets have demonstrated the superiority of our method. We have released the source code on https://github.com/lshi91/CSTGL-Code.
|
[
"['Long Shi' 'Lei Cao' 'Yunshan Ye' 'Yu Zhao' 'Badong Chen']"
] |
null | null |
2403.18397
| null | null |
http://arxiv.org/pdf/2403.18397v1
|
2024-03-27T09:35:56Z
|
2024-03-27T09:35:56Z
|
Colour and Brush Stroke Pattern Recognition in Abstract Art using
Modified Deep Convolutional Generative Adversarial Networks
|
Abstract Art is an immensely popular, discussed form of art that often has the ability to depict the emotions of an artist. Many researchers have made attempts to study abstract art in the form of edge detection, brush stroke and emotion recognition algorithms using machine and deep learning. This papers describes the study of a wide distribution of abstract paintings using Generative Adversarial Neural Networks(GAN). GANs have the ability to learn and reproduce a distribution enabling researchers and scientists to effectively explore and study the generated image space. However, the challenge lies in developing an efficient GAN architecture that overcomes common training pitfalls. This paper addresses this challenge by introducing a modified-DCGAN (mDCGAN) specifically designed for high-quality artwork generation. The approach involves a thorough exploration of the modifications made, delving into the intricate workings of DCGANs, optimisation techniques, and regularisation methods aimed at improving stability and realism in art generation enabling effective study of generated patterns. The proposed mDCGAN incorporates meticulous adjustments in layer configurations and architectural choices, offering tailored solutions to the unique demands of art generation while effectively combating issues like mode collapse and gradient vanishing. Further this paper explores the generated latent space by performing random walks to understand vector relationships between brush strokes and colours in the abstract art space and a statistical analysis of unstable outputs after a certain period of GAN training and compare its significant difference. These findings validate the effectiveness of the proposed approach, emphasising its potential to revolutionise the field of digital art generation and digital art ecosystem.
|
[
"['Srinitish Srinivasan' 'Varenya Pathak']"
] |
null | null |
2403.18402
| null | null |
http://arxiv.org/abs/2403.18402v1
|
2024-03-27T09:44:50Z
|
2024-03-27T09:44:50Z
|
On Spectrogram Analysis in a Multiple Classifier Fusion Framework for
Power Grid Classification Using Electric Network Frequency
|
The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in grid classification through a fusion of classifiers. Four traditional machine learning classifiers plus a Convolutional Neural Network (CNN), optimized using Neural Architecture Search, are developed for One-vs-All classification. This process generates numerous predictions per sample, which are then compiled and used to train a shallow multi-label neural network specifically designed to model the fusion process, ultimately leading to the conclusive class prediction for each sample. Experimental findings reveal that both validation and testing accuracy outperform those of current state-of-the-art classifiers, underlining the effectiveness and robustness of the proposed methodology.
|
[
"['Georgios Tzolopoulos' 'Christos Korgialas' 'Constantine Kotropoulos']"
] |
null | null |
2403.18406
| null | null |
http://arxiv.org/pdf/2403.18406v1
|
2024-03-27T09:48:23Z
|
2024-03-27T09:48:23Z
|
An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering
Using a VLM
|
Stimulated by the sophisticated reasoning capabilities of recent Large Language Models (LLMs), a variety of strategies for bridging video modality have been devised. A prominent strategy involves Video Language Models (VideoLMs), which train a learnable interface with video data to connect advanced vision encoders with LLMs. Recently, an alternative strategy has surfaced, employing readily available foundation models, such as VideoLMs and LLMs, across multiple stages for modality bridging. In this study, we introduce a simple yet novel strategy where only a single Vision Language Model (VLM) is utilized. Our starting point is the plain insight that a video comprises a series of images, or frames, interwoven with temporal information. The essence of video comprehension lies in adeptly managing the temporal aspects along with the spatial details of each frame. Initially, we transform a video into a single composite image by arranging multiple frames in a grid layout. The resulting single image is termed as an image grid. This format, while maintaining the appearance of a solitary image, effectively retains temporal information within the grid structure. Therefore, the image grid approach enables direct application of a single high-performance VLM without necessitating any video-data training. Our extensive experimental analysis across ten zero-shot video question answering benchmarks, including five open-ended and five multiple-choice benchmarks, reveals that the proposed Image Grid Vision Language Model (IG-VLM) surpasses the existing methods in nine out of ten benchmarks.
|
[
"['Wonkyun Kim' 'Changin Choi' 'Wonseok Lee' 'Wonjong Rhee']"
] |
null | null |
2403.18415
| null | null |
http://arxiv.org/pdf/2403.18415v3
|
2024-05-05T21:07:34Z
|
2024-03-27T10:06:33Z
|
The Topos of Transformer Networks
|
The transformer neural network has significantly out-shined all other neural network architectures as the engine behind large language models. We provide a theoretical analysis of the expressivity of the transformer architecture through the lens of topos theory. From this viewpoint, we show that many common neural network architectures, such as the convolutional, recurrent and graph convolutional networks, can be embedded in a pretopos of piecewise-linear functions, but that the transformer necessarily lives in its topos completion. In particular, this suggests that the two network families instantiate different fragments of logic: the former are first order, whereas transformers are higher-order reasoners. Furthermore, we draw parallels with architecture search and gradient descent, integrating our analysis in the framework of cybernetic agents.
|
[
"['Mattia Jacopo Villani' 'Peter McBurney']"
] |
null | null |
2403.18423
| null | null |
http://arxiv.org/pdf/2403.18423v1
|
2024-03-27T10:24:25Z
|
2024-03-27T10:24:25Z
|
SemRoDe: Macro Adversarial Training to Learn Representations That are
Robust to Word-Level Attacks
|
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern. While current research has explored adversarial training techniques, their improvements to defend against word-level attacks have been limited. In this work, we propose a novel approach called Semantic Robust Defence (SemRoDe), a Macro Adversarial Training strategy to enhance the robustness of LMs. Drawing inspiration from recent studies in the image domain, we investigate and later confirm that in a discrete data setting such as language, adversarial samples generated via word substitutions do indeed belong to an adversarial domain exhibiting a high Wasserstein distance from the base domain. Our method learns a robust representation that bridges these two domains. We hypothesize that if samples were not projected into an adversarial domain, but instead to a domain with minimal shift, it would improve attack robustness. We align the domains by incorporating a new distance-based objective. With this, our model is able to learn more generalized representations by aligning the model's high-level output features and therefore better handling unseen adversarial samples. This method can be generalized across word embeddings, even when they share minimal overlap at both vocabulary and word-substitution levels. To evaluate the effectiveness of our approach, we conduct experiments on BERT and RoBERTa models on three datasets. The results demonstrate promising state-of-the-art robustness.
|
[
"['Brian Formento' 'Wenjie Feng' 'Chuan Sheng Foo' 'Luu Anh Tuan'\n 'See-Kiong Ng']"
] |
null | null |
2403.18425
| null | null |
http://arxiv.org/pdf/2403.18425v1
|
2024-03-27T10:26:42Z
|
2024-03-27T10:26:42Z
|
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models
|
Diffusion models have demonstrated remarkable performance in text-to-image synthesis, producing realistic and high resolution images that faithfully adhere to the corresponding text-prompts. Despite their great success, they still fall behind in sketch-to-image synthesis tasks, where in addition to text-prompts, the spatial layout of the generated images has to closely follow the outlines of certain reference sketches. Employing an MLP latent edge predictor to guide the spatial layout of the synthesized image by predicting edge maps at each denoising step has been recently proposed. Despite yielding promising results, the pixel-wise operation of the MLP does not take into account the spatial layout as a whole, and demands numerous denoising iterations to produce satisfactory images, leading to time inefficiency. To this end, we introduce U-Sketch, a framework featuring a U-Net type latent edge predictor, which is capable of efficiently capturing both local and global features, as well as spatial correlations between pixels. Moreover, we propose the addition of a sketch simplification network that offers the user the choice of preprocessing and simplifying input sketches for enhanced outputs. The experimental results, corroborated by user feedback, demonstrate that our proposed U-Net latent edge predictor leads to more realistic results, that are better aligned with the spatial outlines of the reference sketches, while drastically reducing the number of required denoising steps and, consequently, the overall execution time.
|
[
"['Ilias Mitsouras' 'Eleftherios Tsonis' 'Paraskevi Tzouveli'\n 'Athanasios Voulodimos']"
] |
null | null |
2403.18436
| null | null |
http://arxiv.org/pdf/2403.18436v1
|
2024-03-27T10:40:27Z
|
2024-03-27T10:40:27Z
|
Collaborative Active Learning in Conditional Trust Environment
|
In this paper, we investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilities without disclosing their existing data and models. Instead, the collaborators share prediction results from the new domain and newly acquired labels. This collaboration offers several advantages: (a) it addresses privacy and security concerns by eliminating the need for direct model and data disclosure; (b) it enables the use of different data sources and insights without direct data exchange; and (c) it promotes cost-effectiveness and resource efficiency through shared labeling costs. To realize these benefits, we introduce a collaborative active learning framework designed to fulfill the aforementioned objectives. We validate the effectiveness of the proposed framework through simulations. The results demonstrate that collaboration leads to higher AUC scores compared to independent efforts, highlighting the framework's ability to overcome the limitations of individual models. These findings support the use of collaborative approaches in active learning, emphasizing their potential to enhance outcomes through collective expertise and shared resources. Our work provides a foundation for further research on collaborative active learning and its practical applications in various domains where data privacy, cost efficiency, and model performance are critical considerations.
|
[
"['Zan-Kai Chong' 'Hiroyuki Ohsaki' 'Bryan Ng']"
] |
null | null |
2403.18438
| null | null |
http://arxiv.org/pdf/2403.18438v1
|
2024-03-27T10:45:16Z
|
2024-03-27T10:45:16Z
|
Global Vegetation Modeling with Pre-Trained Weather Transformers
|
Accurate vegetation models can produce further insights into the complex interaction between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecasting, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation of the atmosphere's state can be transferred to model the normalized difference vegetation index (NDVI). Our model globally estimates vegetation activity at a resolution of SI{0.25}{degree} while relying only on meteorological data. We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch. Additionally, we compare our results to other recent data-driven NDVI modeling approaches from machine learning and ecology literature. We further provide experimental evidence on how much data and training time is necessary to turn FourCastNet into an effective vegetation model. Code and models will be made available upon publication.
|
[
"['Pascal Janetzky' 'Florian Gallusser' 'Simon Hentschel' 'Andreas Hotho'\n 'Anna Krause']"
] |
null | null |
2403.18439
| null | null |
http://arxiv.org/pdf/2403.18439v1
|
2024-03-27T10:47:06Z
|
2024-03-27T10:47:06Z
|
Generalized Policy Learning for Smart Grids: FL TRPO Approach
|
The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs. Our approach reveals latent interconnections and employs personalized encoding methods to capture unique insights, understanding the relationships between features and optimal strategies, allowing our model to generalize to previously unseen data. Experimental results validate the robustness of our approach, affirming its proficiency in effectively learning policy models for smart grid challenges.
|
[
"['Yunxiang Li' 'Nicolas Mauricio Cuadrado' 'Samuel Horváth' 'Martin Takáč']"
] |
null | null |
2403.18444
| null | null |
http://arxiv.org/pdf/2403.18444v1
|
2024-03-27T11:00:53Z
|
2024-03-27T11:00:53Z
|
FRESCO: Federated Reinforcement Energy System for Cooperative
Optimization
|
The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part in making the required flexibility to achieve the vision of the next-generation grid. This work presents FRESCO, a framework that aims to ease the implementation of energy markets using a hierarchical control architecture of reinforcement learning agents trained using federated learning. The core concept we are proving is that having greedy agents subject to changing conditions from a higher level agent creates a cooperative setup that will allow for fulfilling all the individual objectives. This paper presents a general overview of the framework, the current progress, and some insights we obtained from the recent results.
|
[
"['Nicolas Mauricio Cuadrado' 'Roberto Alejandro Gutierrez' 'Martin Takáč']"
] |
null | null |
2403.18447
| null | null |
http://arxiv.org/pdf/2403.18447v1
|
2024-03-27T11:06:44Z
|
2024-03-27T11:06:44Z
|
Can Language Beat Numerical Regression? Language-Based Multimodal
Trajectory Prediction
|
Language models have demonstrated impressive ability in context understanding and generative performance. Inspired by the recent success of language foundation models, in this paper, we propose LMTraj (Language-based Multimodal Trajectory predictor), which recasts the trajectory prediction task into a sort of question-answering problem. Departing from traditional numerical regression models, which treat the trajectory coordinate sequence as continuous signals, we consider them as discrete signals like text prompts. Specially, we first transform an input space for the trajectory coordinate into the natural language space. Here, the entire time-series trajectories of pedestrians are converted into a text prompt, and scene images are described as text information through image captioning. The transformed numerical and image data are then wrapped into the question-answering template for use in a language model. Next, to guide the language model in understanding and reasoning high-level knowledge, such as scene context and social relationships between pedestrians, we introduce an auxiliary multi-task question and answering. We then train a numerical tokenizer with the prompt data. We encourage the tokenizer to separate the integer and decimal parts well, and leverage it to capture correlations between the consecutive numbers in the language model. Lastly, we train the language model using the numerical tokenizer and all of the question-answer prompts. Here, we propose a beam-search-based most-likely prediction and a temperature-based multimodal prediction to implement both deterministic and stochastic inferences. Applying our LMTraj, we show that the language-based model can be a powerful pedestrian trajectory predictor, and outperforms existing numerical-based predictor methods. Code is publicly available at https://github.com/inhwanbae/LMTrajectory .
|
[
"['Inhwan Bae' 'Junoh Lee' 'Hae-Gon Jeon']"
] |
null | null |
2403.18451
| null | null |
http://arxiv.org/pdf/2403.18451v1
|
2024-03-27T11:11:06Z
|
2024-03-27T11:11:06Z
|
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in
Resource-Constrained CPS and IoT
|
Foundation models (FMs) emerge as a promising solution to harness distributed and diverse environmental data by leveraging prior knowledge to understand the complicated temporal and spatial correlations within heterogeneous datasets. Unlike distributed learning frameworks such as federated learning, which often struggle with multimodal data, FMs can transform diverse inputs into embeddings. This process facilitates the integration of information from various modalities and the application of prior learning to new domains. However, deploying FMs in resource-constrained edge systems poses significant challenges. To this end, we introduce CoRAST, a novel learning framework that utilizes FMs for enhanced analysis of distributed, correlated heterogeneous data. Utilizing a server-based FM, CoRAST can exploit existing environment information to extract temporal, spatial, and cross-modal correlations among sensor data. This enables CoRAST to offer context-aware insights for localized client tasks through FM-powered global representation learning. Our evaluation on real-world weather dataset demonstrates CoRAST's ability to exploit correlated heterogeneous data through environmental representation learning to reduce the forecast errors by up to 50.3% compared to the baselines.
|
[
"['Yi Hu' 'Jinhang Zuo' 'Alanis Zhao' 'Bob Iannucci' 'Carlee Joe-Wong']"
] |
null | null |
2403.18452
| null | null |
http://arxiv.org/pdf/2403.18452v1
|
2024-03-27T11:11:08Z
|
2024-03-27T11:11:08Z
|
SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model
|
There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These associated tasks are defined by various factors, such as the length of input paths, data split and pre-processing methods. Interestingly, even though they commonly take sequential coordinates of observations as input and infer future paths in the same coordinates as output, designing specialized architectures for each task is still necessary. For the other task, generality issues can lead to sub-optimal performances. In this paper, we propose SingularTrajectory, a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks. The core of SingularTrajectory is to unify a variety of human dynamics representations on the associated tasks. To do this, we first build a Singular space to project all types of motion patterns from each task into one embedding space. We next propose an adaptive anchor working in the Singular space. Unlike traditional fixed anchor methods that sometimes yield unacceptable paths, our adaptive anchor enables correct anchors, which are put into a wrong location, based on a traversability map. Finally, we adopt a diffusion-based predictor to further enhance the prototype paths using a cascaded denoising process. Our unified framework ensures the generality across various benchmark settings such as input modality, and trajectory lengths. Extensive experiments on five public benchmarks demonstrate that SingularTrajectory substantially outperforms existing models, highlighting its effectiveness in estimating general dynamics of human movements. Code is publicly available at https://github.com/inhwanbae/SingularTrajectory .
|
[
"['Inhwan Bae' 'Young-Jae Park' 'Hae-Gon Jeon']"
] |
null | null |
2403.18486
| null | null |
http://arxiv.org/pdf/2403.18486v1
|
2024-03-27T11:58:45Z
|
2024-03-27T11:58:45Z
|
Synthesizing EEG Signals from Event-Related Potential Paradigms with
Conditional Diffusion Models
|
Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models. While diffusion models have previously been successfully applied to electroencephalogram (EEG) data, existing models lack flexibility w.r.t.~sampling or require alternative representations of the EEG data. To overcome these limitations, we introduce a novel approach to conditional diffusion models that utilizes classifier-free guidance to directly generate subject-, session-, and class-specific EEG data. In addition to commonly used metrics, domain-specific metrics are employed to evaluate the specificity of the generated samples. The results indicate that the proposed model can generate EEG data that resembles real data for each subject, session, and class.
|
[
"['Guido Klein' 'Pierre Guetschel' 'Gianluigi Silvestri'\n 'Michael Tangermann']"
] |
null | null |
2403.18489
| null | null |
http://arxiv.org/pdf/2403.18489v1
|
2024-03-27T12:01:51Z
|
2024-03-27T12:01:51Z
|
Impact of Employing Weather Forecast Data as Input to the Estimation of
Evapotranspiration by Deep Neural Network Models
|
Reference Evapotranspiration (ET0) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for ET0 computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute ET0 using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily ET0 estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several ET0 estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors' previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct ET0 estimation by an ANN model, and (ii) estimate SR by ANN model, and then use that estimation for ET0 computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (R2) ranging between 0.893 and 0.667, when considering forecasts up to 15 days.
|
[
"['Pedro J. Vaz' 'Gabriela Schütz' 'Carlos Guerrero' 'Pedro J. S. Cardoso']"
] |
null | null |
2403.18494
| null | null |
http://arxiv.org/pdf/2403.18494v1
|
2024-03-27T12:10:30Z
|
2024-03-27T12:10:30Z
|
Learning in PINNs: Phase transition, total diffusion, and generalization
|
We investigate the learning dynamics of fully-connected neural networks through the lens of gradient signal-to-noise ratio (SNR), examining the behavior of first-order optimizers like Adam in non-convex objectives. By interpreting the drift/diffusion phases in the information bottleneck theory, focusing on gradient homogeneity, we identify a third phase termed ``total diffusion", characterized by equilibrium in the learning rates and homogeneous gradients. This phase is marked by an abrupt SNR increase, uniform residuals across the sample space and the most rapid training convergence. We propose a residual-based re-weighting scheme to accelerate this diffusion in quadratic loss functions, enhancing generalization. We also explore the information compression phenomenon, pinpointing a significant saturation-induced compression of activations at the total diffusion phase, with deeper layers experiencing negligible information loss. Supported by experimental data on physics-informed neural networks (PINNs), which underscore the importance of gradient homogeneity due to their PDE-based sample inter-dependence, our findings suggest that recognizing phase transitions could refine ML optimization strategies for improved generalization.
|
[
"['Sokratis J. Anagnostopoulos' 'Juan Diego Toscano'\n 'Nikolaos Stergiopulos' 'George Em Karniadakis']"
] |
null | null |
2403.18495
| null | null |
http://arxiv.org/pdf/2403.18495v1
|
2024-03-27T12:15:22Z
|
2024-03-27T12:15:22Z
|
Direct mineral content prediction from drill core images via transfer
learning
|
Deep subsurface exploration is important for mining, oil and gas industries, as well as in the assessment of geological units for the disposal of chemical or nuclear waste, or the viability of geothermal energy systems. Typically, detailed examinations of subsurface formations or units are performed on cuttings or core materials extracted during drilling campaigns, as well as on geophysical borehole data, which provide detailed information about the petrophysical properties of the rocks. Depending on the volume of rock samples and the analytical program, the laboratory analysis and diagnostics can be very time-consuming. This study investigates the potential of utilizing machine learning, specifically convolutional neural networks (CNN), to assess the lithology and mineral content solely from analysis of drill core images, aiming to support and expedite the subsurface geological exploration. The paper outlines a comprehensive methodology, encompassing data preprocessing, machine learning methods, and transfer learning techniques. The outcome reveals a remarkable 96.7% accuracy in the classification of drill core segments into distinct formation classes. Furthermore, a CNN model was trained for the evaluation of mineral content using a learning data set from multidimensional log analysis data (silicate, total clay, carbonate). When benchmarked against laboratory XRD measurements on samples from the cores, both the advanced multidimensional log analysis model and the neural network approach developed here provide equally good performance. This work demonstrates that deep learning and particularly transfer learning can support extracting petrophysical properties, including mineral content and formation classification, from drill core images, thus offering a road map for enhancing model performance and data set quality in image-based analysis of drill cores.
|
[
"['Romana Boiger' 'Sergey V. Churakov' 'Ignacio Ballester Llagaria'\n 'Georg Kosakowski' 'Raphael Wüst' 'Nikolaos I. Prasianakis']"
] |
null | null |
2403.18506
| null | null |
http://arxiv.org/abs/2403.18506v1
|
2024-03-27T12:35:23Z
|
2024-03-27T12:35:23Z
|
Faster Convergence for Transformer Fine-tuning with Line Search Methods
|
Recent works have shown that line search methods greatly increase performance of traditional stochastic gradient descent methods on a variety of datasets and architectures [1], [2]. In this work we succeed in extending line search methods to the novel and highly popular Transformer architecture and dataset domains in natural language processing. More specifically, we combine the Armijo line search with the Adam optimizer and extend it by subdividing the networks architecture into sensible units and perform the line search separately on these local units. Our optimization method outperforms the traditional Adam optimizer and achieves significant performance improvements for small data sets or small training budgets, while performing equal or better for other tested cases. Our work is publicly available as a python package, which provides a hyperparameter-free pytorch optimizer that is compatible with arbitrary network architectures.
|
[
"['Philip Kenneweg' 'Leonardo Galli' 'Tristan Kenneweg' 'Barbara Hammer']"
] |
null | null |
2403.18509
| null | null |
http://arxiv.org/pdf/2403.18509v2
|
2024-06-17T10:38:59Z
|
2024-03-27T12:39:16Z
|
Distributed Maximum Consensus over Noisy Links
|
We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of noisy communication links. Our approach entails redefining the maximum consensus problem as a distributed optimization problem, allowing a solution using the alternating direction method of multipliers. Unlike existing algorithms that rely on multiple sets of noise-corrupted estimates, RD-MC employs a single set, enhancing both robustness and efficiency. To further mitigate the effects of link noise and improve robustness, we apply moving averaging to the local estimates. Through extensive simulations, we demonstrate that RD-MC is significantly more robust to communication link noise compared to existing maximum-consensus algorithms.
|
[
"['Ehsan Lari' 'Reza Arablouei' 'Naveen K. D. Venkategowda' 'Stefan Werner']"
] |
null | null |
2403.18514
| null | null |
http://arxiv.org/pdf/2403.18514v1
|
2024-03-27T12:44:57Z
|
2024-03-27T12:44:57Z
|
CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection
of Pathological Pulmonary CT scans
|
Unsupervised pathology detection can be implemented by training a model on healthy data only and measuring the deviation from the training set upon inference, for example with CNN-based feature extraction and one-class classifiers, or reconstruction-score-based methods such as AEs, GANs and Diffusion models. Normalizing Flows (NF) have the ability to directly learn the probability distribution of training examples through an invertible architecture. We leverage this property in a novel 3D NF-based model named CT-3DFlow, specifically tailored for patient-level pulmonary pathology detection in chest CT data. Our model is trained unsupervised on healthy 3D pulmonary CT patches, and detects deviations from its log-likelihood distribution as anomalies. We aggregate patches-level likelihood values from a patient's CT scan to provide a patient-level 'normal'/'abnormal' prediction. Out-of-distribution detection performance is evaluated using expert annotations on a separate chest CT test dataset, outperforming other state-of-the-art methods.
|
[
"['Aissam Djahnine' 'Alexandre Popoff' 'Emilien Jupin-Delevaux'\n 'Vincent Cottin' 'Olivier Nempont' 'Loic Boussel']"
] |
null | null |
2403.18517
| null | null |
http://arxiv.org/pdf/2403.18517v3
|
2024-06-08T11:41:26Z
|
2024-03-27T12:49:14Z
|
Efficient Algorithms for Regularized Nonnegative Scale-invariant
Low-rank Approximation Models
|
Regularized nonnegative low-rank approximations such as sparse Nonnegative Matrix Factorization or sparse Nonnegative Tucker Decomposition are an important branch of dimensionality reduction models with enhanced interpretability. However, from a practical perspective, the choice of regularizers and regularization coefficients, as well as the design of efficient algorithms, is challenging because of the multifactor nature of these models and the lack of theory to back these choices. This paper aims at improving upon these issues. By studying a more general model called the Homogeneous Regularized Scale-Invariant, we prove that the scale-invariance inherent to low-rank approximation models causes an implicit regularization with both unexpected beneficial and detrimental effects. This observation allows to better understand the effect of regularization functions in low-rank approximation models, to guide the choice of the regularization hyperparameters, and to design balancing strategies to enhance the convergence speed of dedicated optimization algorithms. Some of these results were already known but restricted to specific instances of regularized low-rank approximations. We also derive a generic Majorization Minimization algorithm that handles many regularized nonnegative low-rank approximations, with convergence guarantees. We showcase our contributions on sparse Nonnegative Matrix Factorization, ridge-regularized Canonical Polyadic decomposition and sparse Nonnegative Tucker Decomposition.
|
[
"['Jeremy E. Cohen' 'Valentin Leplat']"
] |
null | null |
2403.18519
| null | null |
http://arxiv.org/abs/2403.18519v1
|
2024-03-27T12:50:27Z
|
2024-03-27T12:50:27Z
|
Improving Line Search Methods for Large Scale Neural Network Training
|
In recent studies, line search methods have shown significant improvements in the performance of traditional stochastic gradient descent techniques, eliminating the need for a specific learning rate schedule. In this paper, we identify existing issues in state-of-the-art line search methods, propose enhancements, and rigorously evaluate their effectiveness. We test these methods on larger datasets and more complex data domains than before. Specifically, we improve the Armijo line search by integrating the momentum term from ADAM in its search direction, enabling efficient large-scale training, a task that was previously prone to failure using Armijo line search methods. Our optimization approach outperforms both the previous Armijo implementation and tuned learning rate schedules for Adam. Our evaluation focuses on Transformers and CNNs in the domains of NLP and image data. Our work is publicly available as a Python package, which provides a hyperparameter free Pytorch optimizer.
|
[
"['Philip Kenneweg' 'Tristan Kenneweg' 'Barbara Hammer']"
] |
null | null |
2403.18525
| null | null |
http://arxiv.org/pdf/2403.18525v1
|
2024-03-27T12:59:44Z
|
2024-03-27T12:59:44Z
|
Language Plays a Pivotal Role in the Object-Attribute Compositional
Generalization of CLIP
|
Vision-language models, such as CLIP, have shown promising Out-of-Distribution (OoD) generalization under various types of distribution shifts. Recent studies attempted to investigate the leading cause of this capability. In this work, we follow the same path, but focus on a specific type of OoD data - images with novel compositions of attribute-object pairs - and study whether such models can successfully classify those images into composition classes. We carefully designed an authentic image test dataset called ImageNet-AO, consisting of attributes for objects that are unlikely encountered in the CLIP training sets. We found that CLIPs trained with large datasets such as OpenAI CLIP, LAION-400M, and LAION-2B show orders-of-magnitude improvement in effective compositional OoD generalization compared to both supervised models and CLIPs trained with smaller datasets, such as CC-12M and YFCC-15M. Our results provide evidence that the scale and diversity of training data and language supervision play a key role in unlocking the compositional generalization abilities of vision-language models.
|
[
"['Reza Abbasi' 'Mohammad Samiei' 'Mohammad Hossein Rohban'\n 'Mahdieh Soleymani Baghshah']"
] |
null | null |
2403.18535
| null | null |
http://arxiv.org/pdf/2403.18535v1
|
2024-03-27T13:11:34Z
|
2024-03-27T13:11:34Z
|
Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs
|
Recent studies reveal a significant theoretical link between variational autoencoders (VAEs) and rate-distortion theory, notably in utilizing VAEs to estimate the theoretical upper bound of the information rate-distortion function of images. Such estimated theoretical bounds substantially exceed the performance of existing neural image codecs (NICs). To narrow this gap, we propose a theoretical bound-guided hierarchical VAE (BG-VAE) for NIC. The proposed BG-VAE leverages the theoretical bound to guide the NIC model towards enhanced performance. We implement the BG-VAE using Hierarchical VAEs and demonstrate its effectiveness through extensive experiments. Along with advanced neural network blocks, we provide a versatile, variable-rate NIC that outperforms existing methods when considering both rate-distortion performance and computational complexity. The code is available at BG-VAE.
|
[
"['Yichi Zhang' 'Zhihao Duan' 'Yuning Huang' 'Fengqing Zhu']"
] |
null | null |
2403.18539
| null | null |
http://arxiv.org/pdf/2403.18539v2
|
2024-03-30T10:07:21Z
|
2024-03-27T13:14:29Z
|
Safe and Robust Reinforcement Learning: Principles and Practice
|
Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges related to safety and robustness. This paper aims to identify and further understand those challenges thorough the exploration of the main dimensions of the safe and robust RL landscape, encompassing algorithmic, ethical, and practical considerations. We conduct a comprehensive review of methodologies and open problems that summarizes the efforts in recent years to address the inherent risks associated with RL applications. After discussing and proposing definitions for both safe and robust RL, the paper categorizes existing research works into different algorithmic approaches that enhance the safety and robustness of RL agents. We examine techniques such as uncertainty estimation, optimisation methodologies, exploration-exploitation trade-offs, and adversarial training. Environmental factors, including sim-to-real transfer and domain adaptation, are also scrutinized to understand how RL systems can adapt to diverse and dynamic surroundings. Moreover, human involvement is an integral ingredient of the analysis, acknowledging the broad set of roles that humans can take in this context. Importantly, to aid practitioners in navigating the complexities of safe and robust RL implementation, this paper introduces a practical checklist derived from the synthesized literature. The checklist encompasses critical aspects of algorithm design, training environment considerations, and ethical guidelines. It will serve as a resource for developers and policymakers alike to ensure the responsible deployment of RL systems in many application domains.
|
[
"['Taku Yamagata' 'Raul Santos-Rodriguez']"
] |
null | null |
2403.18540
| null | null |
http://arxiv.org/pdf/2403.18540v1
|
2024-03-27T13:17:15Z
|
2024-03-27T13:17:15Z
|
skscope: Fast Sparsity-Constrained Optimization in Python
|
Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact. In the paper, the library skscope is introduced to overcome such an obstacle. With skscope, users can solve the SCO by just programming the objective function. The convenience of skscope is demonstrated through two examples in the paper, where sparse linear regression and trend filtering are addressed with just four lines of code. More importantly, skscope's efficient implementation allows state-of-the-art solvers to quickly attain the sparse solution regardless of the high dimensionality of parameter space. Numerical experiments reveal the available solvers in skscope can achieve up to 80x speedup on the competing relaxation solutions obtained via the benchmarked convex solver. skscope is published on the Python Package Index (PyPI) and Conda, and its source code is available at: https://github.com/abess-team/skscope.
|
[
"['Zezhi Wang' 'Jin Zhu' 'Peng Chen' 'Huiyang Peng' 'Xiaoke Zhang'\n 'Anran Wang' 'Yu Zheng' 'Junxian Zhu' 'Xueqin Wang']"
] |
null | null |
2403.18542
| null | null |
http://arxiv.org/pdf/2403.18542v1
|
2024-03-27T13:22:38Z
|
2024-03-27T13:22:38Z
|
Attention-aware semantic relevance predicting Chinese sentence reading
|
In recent years, several influential computational models and metrics have been proposed to predict how humans comprehend and process sentence. One particularly promising approach is contextual semantic similarity. Inspired by the attention algorithm in Transformer and human memory mechanisms, this study proposes an ``attention-aware'' approach for computing contextual semantic relevance. This new approach takes into account the different contributions of contextual parts and the expectation effect, allowing it to incorporate contextual information fully. The attention-aware approach also facilitates the simulation of existing reading models and evaluate them. The resulting ``attention-aware'' metrics of semantic relevance can more accurately predict fixation durations in Chinese reading tasks recorded in an eye-tracking corpus than those calculated by existing approaches. The study's findings further provide strong support for the presence of semantic preview benefits in Chinese naturalistic reading. Furthermore, the attention-aware metrics of semantic relevance, being memory-based, possess high interpretability from both linguistic and cognitive standpoints, making them a valuable computational tool for modeling eye-movements in reading and further gaining insight into the process of language comprehension. Our approach underscores the potential of these metrics to advance our comprehension of how humans understand and process language, ultimately leading to a better understanding of language comprehension and processing.
|
[
"['Kun Sun']"
] |
null | null |
2403.18560
| null | null |
http://arxiv.org/pdf/2403.18560v1
|
2024-03-27T13:42:14Z
|
2024-03-27T13:42:14Z
|
Noise-Robust Keyword Spotting through Self-supervised Pretraining
|
Voice assistants are now widely available, and to activate them a keyword spotting (KWS) algorithm is used. Modern KWS systems are mainly trained using supervised learning methods and require a large amount of labelled data to achieve a good performance. Leveraging unlabelled data through self-supervised learning (SSL) has been shown to increase the accuracy in clean conditions. This paper explores how SSL pretraining such as Data2Vec can be used to enhance the robustness of KWS models in noisy conditions, which is under-explored. Models of three different sizes are pretrained using different pretraining approaches and then fine-tuned for KWS. These models are then tested and compared to models trained using two baseline supervised learning methods, one being standard training using clean data and the other one being multi-style training (MTR). The results show that pretraining and fine-tuning on clean data is superior to supervised learning on clean data across all testing conditions, and superior to supervised MTR for testing conditions of SNR above 5 dB. This indicates that pretraining alone can increase the model's robustness. Finally, it is found that using noisy data for pretraining models, especially with the Data2Vec-denoising approach, significantly enhances the robustness of KWS models in noisy conditions.
|
[
"['Jacob Mørk' 'Holger Severin Bovbjerg' 'Gergely Kiss' 'Zheng-Hua Tan']"
] |
null | null |
2403.18569
| null | null |
http://arxiv.org/pdf/2403.18569v1
|
2024-03-27T13:50:13Z
|
2024-03-27T13:50:13Z
|
PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop
Prediction
|
IR drop on the power delivery network (PDN) is closely related to PDN's configuration and cell current consumption. As the integrated circuit (IC) design is growing larger, dynamic IR drop simulation becomes computationally unaffordable and machine learning based IR drop prediction has been explored as a promising solution. Although CNN-based methods have been adapted to IR drop prediction task in several works, the shortcomings of overlooking PDN configuration is non-negligible. In this paper, we consider not only how to properly represent cell-PDN relation, but also how to model IR drop following its physical nature in the feature aggregation procedure. Thus, we propose a novel graph structure, PDNGraph, to unify the representations of the PDN structure and the fine-grained cell-PDN relation. We further propose a dual-branch heterogeneous network, PDNNet, incorporating two parallel GNN-CNN branches to favorably capture the above features during the learning process. Several key designs are presented to make the dynamic IR drop prediction highly effective and interpretable. We are the first work to apply graph structure to deep-learning based dynamic IR drop prediction method. Experiments show that PDNNet outperforms the state-of-the-art CNN-based methods by up to 39.3% reduction in prediction error and achieves 545x speedup compared to the commercial tool, which demonstrates the superiority of our method.
|
[
"['Yuxiang Zhao' 'Zhuomin Chai' 'Xun Jiang' 'Yibo Lin' 'Runsheng Wang'\n 'Ru Huang']"
] |
null | null |
2403.18570
| null | null |
http://arxiv.org/pdf/2403.18570v1
|
2024-03-27T13:51:26Z
|
2024-03-27T13:51:26Z
|
Physics-Informed Graph Neural Networks for Water Distribution Systems
|
Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban environments in 2050, efficient simulation and planning tools for WDS play a crucial role in reaching UN's sustainable developmental goal (SDG) 6 - "Clean water and sanitation for all". In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS. Using a recursive approach, our model only needs a few graph convolutional neural network (GCN) layers and employs an innovative algorithm based on message passing. Unlike conventional machine learning tasks, the model uses hydraulic principles to infer two additional hydraulic state features in the process of reconstructing the available ground truth feature in an unsupervised manner. To the best of our knowledge, this is the first DL approach to emulate the popular hydraulic simulator EPANET, utilizing no additional information. Like most DL models and unlike the hydraulic simulator, our model demonstrates vastly faster emulation times that do not increase drastically with the size of the WDS. Moreover, we achieve high accuracy on the ground truth and very similar results compared to the hydraulic simulator as demonstrated through experiments on five real-world WDS datasets.
|
[
"['Inaam Ashraf' 'Janine Strotherm' 'Luca Hermes' 'Barbara Hammer']"
] |
null | null |
2403.18578
| null | null |
http://arxiv.org/pdf/2403.18578v2
|
2024-04-04T22:38:02Z
|
2024-03-27T13:59:05Z
|
SteinGen: Generating Fidelitous and Diverse Graph Samples
|
Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging, especially when the number of graph observations is small. Here, we tackle the problem of graph generation from only one observed graph. The classical approach of graph generation from parametric models relies on the estimation of parameters, which can be inconsistent or expensive to compute due to intractable normalisation constants. Generative modelling based on machine learning techniques to generate high-quality graph samples avoids parameter estimation but usually requires abundant training samples. Our proposed generating procedure, SteinGen, which is phrased in the setting of graphs as realisations of exponential random graph models, combines ideas from Stein's method and MCMC by employing Markovian dynamics which are based on a Stein operator for the target model. SteinGen uses the Glauber dynamics associated with an estimated Stein operator to generate a sample, and re-estimates the Stein operator from the sample after every sampling step. We show that on a class of exponential random graph models this novel "estimation and re-estimation" generation strategy yields high distributional similarity (high fidelity) to the original data, combined with high sample diversity.
|
[
"['Gesine Reinert' 'Wenkai Xu']"
] |
null | null |
2403.18579
| null | null |
http://arxiv.org/pdf/2403.18579v1
|
2024-03-27T13:59:09Z
|
2024-03-27T13:59:09Z
|
On Optimizing Hyperparameters for Quantum Neural Networks
|
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, where we have started to experience limits in scaling conventional HPC hardware, as theorized by Moore's law. Despite heavy parallelization and optimization efforts, current state-of-the-art ML models require weeks for training, which is associated with an enormous $CO_2$ footprint. Quantum Computing, and specifically Quantum Machine Learning (QML), can offer significant theoretical speed-ups and enhanced expressive power. However, training QML models requires tuning various hyperparameters, which is a nontrivial task and suboptimal choices can highly affect the trainability and performance of the models. In this study, we identify the most impactful hyperparameters and collect data about the performance of QML models. We compare different configurations and provide researchers with performance data and concrete suggestions for hyperparameter selection.
|
[
"['Sabrina Herbst' 'Vincenzo De Maio' 'Ivona Brandic']"
] |
null | null |
2403.18582
| null | null |
http://arxiv.org/pdf/2403.18582v1
|
2024-03-27T14:03:41Z
|
2024-03-27T14:03:41Z
|
One flow to correct them all: improving simulations in high-energy
physics with a single normalising flow and a switch
|
Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.
|
[
"['Caio Cesar Daumann' 'Mauro Donega' 'Johannes Erdmann'\n 'Massimiliano Galli' 'Jan Lukas Späh' 'Davide Valsecchi']"
] |
null | null |
2403.18587
| null | null |
http://arxiv.org/pdf/2403.18587v1
|
2024-03-27T14:11:23Z
|
2024-03-27T14:11:23Z
|
The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency
Attacks in Computer Vision
|
Resource efficiency plays an important role for machine learning nowadays. The energy and decision latency are two critical aspects to ensure a sustainable and practical application. Unfortunately, the energy consumption and decision latency are not robust against adversaries. Researchers have recently demonstrated that attackers can compute and submit so-called sponge examples at inference time to increase the energy consumption and decision latency of neural networks. In computer vision, the proposed strategy crafts inputs with less activation sparsity which could otherwise be used to accelerate the computation. In this paper, we analyze the mechanism how these energy-latency attacks reduce activation sparsity. In particular, we find that input uniformity is a key enabler. A uniform image, that is, an image with mostly flat, uniformly colored surfaces, triggers more activations due to a specific interplay of convolution, batch normalization, and ReLU activation. Based on these insights, we propose two new simple, yet effective strategies for crafting sponge examples: sampling images from a probability distribution and identifying dense, yet inconspicuous inputs in natural datasets. We empirically examine our findings in a comprehensive evaluation with multiple image classification models and show that our attack achieves the same sparsity effect as prior sponge-example methods, but at a fraction of computation effort. We also show that our sponge examples transfer between different neural networks. Finally, we discuss applications of our findings for the good by improving efficiency by increasing sparsity.
|
[
"['Andreas Müller' 'Erwin Quiring']"
] |
null | null |
2403.18597
| null | null |
http://arxiv.org/pdf/2403.18597v1
|
2024-03-27T14:20:11Z
|
2024-03-27T14:20:11Z
|
Heterogeneous Peridynamic Neural Operators: Discover Biotissue
Constitutive Law and Microstructure From Digital Image Correlation
Measurements
|
Human tissues are highly organized structures with specific collagen fiber arrangements varying from point to point. The effects of such heterogeneity play an important role for tissue function, and hence it is of critical to discover and understand the distribution of such fiber orientations from experimental measurements, such as the digital image correlation data. To this end, we introduce the heterogeneous peridynamic neural operator (HeteroPNO) approach, for data-driven constitutive modeling of heterogeneous anisotropic materials. The goal is to learn both a nonlocal constitutive law together with the material microstructure, in the form of a heterogeneous fiber orientation field, from loading field-displacement field measurements. To this end, we propose a two-phase learning approach. Firstly, we learn a homogeneous constitutive law in the form of a neural network-based kernel function and a nonlocal bond force, to capture complex homogeneous material responses from data. Then, in the second phase we reinitialize the learnt bond force and the kernel function, and training them together with a fiber orientation field for each material point. Owing to the state-based peridynamic skeleton, our HeteroPNO-learned material models are objective and have the balance of linear and angular momentum guaranteed. Moreover, the effects from heterogeneity and nonlinear constitutive relationship are captured by the kernel function and the bond force respectively, enabling physical interpretability. As a result, our HeteroPNO architecture can learn a constitutive model for a biological tissue with anisotropic heterogeneous response undergoing large deformation regime. Moreover, the framework is capable to provide displacement and stress field predictions for new and unseen loading instances.
|
[
"['Siavash Jafarzadeh' 'Stewart Silling' 'Lu Zhang' 'Colton Ross'\n 'Chung-Hao Lee' 'S. M. Rakibur Rahman' 'Shuodao Wang' 'Yue Yu']"
] |
null | null |
2403.18613
| null | null |
http://arxiv.org/pdf/2403.18613v1
|
2024-03-27T14:28:44Z
|
2024-03-27T14:28:44Z
|
Scalable Lipschitz Estimation for CNNs
|
Estimating the Lipschitz constant of deep neural networks is of growing interest as it is useful for informing on generalisability and adversarial robustness. Convolutional neural networks (CNNs) in particular, underpin much of the recent success in computer vision related applications. However, although existing methods for estimating the Lipschitz constant can be tight, they have limited scalability when applied to CNNs. To tackle this, we propose a novel method to accelerate Lipschitz constant estimation for CNNs. The core idea is to divide a large convolutional block via a joint layer and width-wise partition, into a collection of smaller blocks. We prove an upper-bound on the Lipschitz constant of the larger block in terms of the Lipschitz constants of the smaller blocks. Through varying the partition factor, the resulting method can be adjusted to prioritise either accuracy or scalability and permits parallelisation. We demonstrate an enhanced scalability and comparable accuracy to existing baselines through a range of experiments.
|
[
"['Yusuf Sulehman' 'Tingting Mu']"
] |
null | null |
2403.18631
| null | null |
http://arxiv.org/abs/2403.18631v2
|
2024-06-28T22:21:45Z
|
2024-03-27T14:38:02Z
|
First Experiences with the Identification of People at Risk for Diabetes
in Argentina using Machine Learning Techniques
|
Detecting Type 2 Diabetes (T2D) and Prediabetes (PD) is a real challenge for medicine due to the absence of pathogenic symptoms and the lack of known associated risk factors. Even though some proposals for machine learning models enable the identification of people at risk, the nature of the condition makes it so that a model suitable for one population may not necessarily be suitable for another. In this article, the development and assessment of predictive models to identify people at risk for T2D and PD specifically in Argentina are discussed. First, the database was thoroughly preprocessed and three specific datasets were generated considering a compromise between the number of records and the amount of available variables. After applying 5 different classification models, the results obtained show that a very good performance was observed for two datasets with some of these models. In particular, RF, DT, and ANN demonstrated great classification power, with good values for the metrics under consideration. Given the lack of this type of tool in Argentina, this work represents the first step towards the development of more sophisticated models.
|
[
"['Enzo Rucci' 'Gonzalo Tittarelli' 'Franco Ronchetti' 'Jorge F. Elgart'\n 'Laura Lanzarini' 'Juan José Gagliardino']"
] |
null | null |
2403.18635
| null | null |
http://arxiv.org/abs/2403.18635v1
|
2024-03-27T14:40:25Z
|
2024-03-27T14:40:25Z
|
Fusion approaches for emotion recognition from speech using acoustic and
text-based features
|
In this paper, we study different approaches for classifying emotions from speech using acoustic and text-based features. We propose to obtain contextualized word embeddings with BERT to represent the information contained in speech transcriptions and show that this results in better performance than using Glove embeddings. We also propose and compare different strategies to combine the audio and text modalities, evaluating them on IEMOCAP and MSP-PODCAST datasets. We find that fusing acoustic and text-based systems is beneficial on both datasets, though only subtle differences are observed across the evaluated fusion approaches. Finally, for IEMOCAP, we show the large effect that the criteria used to define the cross-validation folds have on results. In particular, the standard way of creating folds for this dataset results in a highly optimistic estimation of performance for the text-based system, suggesting that some previous works may overestimate the advantage of incorporating transcriptions.
|
[
"['Leonardo Pepino' 'Pablo Riera' 'Luciana Ferrer' 'Agustin Gravano']"
] |
null | null |
2403.18637
| null | null |
http://arxiv.org/pdf/2403.18637v1
|
2024-03-27T14:42:08Z
|
2024-03-27T14:42:08Z
|
Transformers-based architectures for stroke segmentation: A review
|
Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes. The emergence of deep learning methodologies has transformed the landscape of medical image analysis. Recently, Transformers, initially designed for natural language processing, have exhibited remarkable capabilities in various computer vision applications, including medical image analysis. This comprehensive review aims to provide an in-depth exploration of the cutting-edge Transformer-based architectures applied in the context of stroke segmentation. It commences with an exploration of stroke pathology, imaging modalities, and the challenges associated with accurate diagnosis and segmentation. Subsequently, the review delves into the fundamental ideas of Transformers, offering detailed insights into their architectural intricacies and the underlying mechanisms that empower them to effectively capture complex spatial information within medical images. The existing literature is systematically categorized and analyzed, discussing various approaches that leverage Transformers for stroke segmentation. A critical assessment is provided, highlighting the strengths and limitations of these methods, including considerations of performance and computational efficiency. Additionally, this review explores potential avenues for future research and development
|
[
"['Yalda Zafari-Ghadim' 'Essam A. Rashed' 'Mohamed Mabrok']"
] |
null | null |
2403.18639
| null | null |
http://arxiv.org/pdf/2403.18639v1
|
2024-02-05T13:54:11Z
|
2024-02-05T13:54:11Z
|
Dependency Aware Incident Linking in Large Cloud Systems
|
Despite significant reliability efforts, large-scale cloud services inevitably experience production incidents that can significantly impact service availability and customer's satisfaction. Worse, in many cases one incident can lead to multiple downstream failures due to cascading effects that creates several related incidents across different dependent services. Often time On-call Engineers (OCEs) examine these incidents in silos that lead to significant amount of manual toil and increase the overall time-to-mitigate incidents. Therefore, developing efficient incident linking models is of paramount importance for grouping related incidents into clusters so as to quickly resolve major outages and reduce on-call fatigue. Existing incident linking methods mostly leverages textual and contextual information of incidents (e.g., title, description, severity, impacted components), thus failing to leverage the inter-dependencies between services. In this paper, we propose the dependency-aware incident linking (DiLink) framework which leverages both textual and service dependency graph information to improve the accuracy and coverage of incident links not only coming from same service, but also from different services and workloads. Furthermore, we propose a novel method to align the embeddings of multi-modal (i.e., textual and graphical) data using Orthogonal Procrustes. Extensive experimental results on real-world incidents from 5 workloads of Microsoft demonstrate that our alignment method has an F1-score of 0.96 (14% gain over current state-of-the-art methods). We are also in the process of deploying this solution across 610 services from these 5 workloads for continuously supporting OCEs improving incident management and reducing manual toil.
|
[
"['Supriyo Ghosh' 'Karish Grover' 'Jimmy Wong' 'Chetan Bansal'\n 'Rakesh Namineni' 'Mohit Verma' 'Saravan Rajmohan']"
] |
null | null |
2403.18664
| null | null |
http://arxiv.org/pdf/2403.18664v1
|
2024-03-27T15:08:00Z
|
2024-03-27T15:08:00Z
|
Neural Network-Based Piecewise Survival Models
|
In this paper, a family of neural network-based survival models is presented. The models are specified based on piecewise definitions of the hazard function and the density function on a partitioning of the time; both constant and linear piecewise definitions are presented, resulting in a family of four models. The models can be seen as an extension of the commonly used discrete-time and piecewise exponential models and thereby add flexibility to this set of standard models. Using a simulated dataset the models are shown to perform well compared to the highly expressive, state-of-the-art energy-based model, while only requiring a fraction of the computation time.
|
[
"['Olov Holmer' 'Erik Frisk' 'Mattias Krysander']"
] |
null | null |
2403.18668
| null | null |
http://arxiv.org/pdf/2403.18668v1
|
2024-03-27T15:11:07Z
|
2024-03-27T15:11:07Z
|
Aiming for Relevance
|
Vital signs are crucial in intensive care units (ICUs). They are used to track the patient's state and to identify clinically significant changes. Predicting vital sign trajectories is valuable for early detection of adverse events. However, conventional machine learning metrics like RMSE often fail to capture the true clinical relevance of such predictions. We introduce novel vital sign prediction performance metrics that align with clinical contexts, focusing on deviations from clinical norms, overall trends, and trend deviations. These metrics are derived from empirical utility curves obtained in a previous study through interviews with ICU clinicians. We validate the metrics' usefulness using simulated and real clinical datasets (MIMIC and eICU). Furthermore, we employ these metrics as loss functions for neural networks, resulting in models that excel in predicting clinically significant events. This research paves the way for clinically relevant machine learning model evaluation and optimization, promising to improve ICU patient care. 10 pages, 9 figures.
|
[
"['Bar Eini Porat' 'Danny Eytan' 'Uri Shalit']"
] |
null | null |
2403.18671
| null | null |
http://arxiv.org/pdf/2403.18671v1
|
2024-03-27T15:15:14Z
|
2024-03-27T15:15:14Z
|
Fact Checking Beyond Training Set
|
Evaluating the veracity of everyday claims is time consuming and in some cases requires domain expertise. We empirically demonstrate that the commonly used fact checking pipeline, known as the retriever-reader, suffers from performance deterioration when it is trained on the labeled data from one domain and used in another domain. Afterwards, we delve into each component of the pipeline and propose novel algorithms to address this problem. We propose an adversarial algorithm to make the retriever component robust against distribution shift. Our core idea is to initially train a bi-encoder on the labeled source data, and then, to adversarially train two separate document and claim encoders using unlabeled target data. We then focus on the reader component and propose to train it such that it is insensitive towards the order of claims and evidence documents. Our empirical evaluations support the hypothesis that such a reader shows a higher robustness against distribution shift. To our knowledge, there is no publicly available multi-topic fact checking dataset. Thus, we propose a simple automatic method to re-purpose two well-known fact checking datasets. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models, including recent domain adaptation models that use GPT4 for generating synthetic data.
|
[
"['Payam Karisani' 'Heng Ji']"
] |
null | null |
2403.18680
| null | null |
http://arxiv.org/pdf/2403.18680v2
|
2024-06-06T13:58:20Z
|
2024-03-27T15:22:16Z
|
Non-Linear Inference Time Intervention: Improving LLM Truthfulness
|
In this work, we explore LLM's internal representation space to identify attention heads that contain the most truthful and accurate information. We further developed the Inference Time Intervention (ITI) framework, which lets bias LLM without the need for fine-tuning. The improvement manifests in introducing a non-linear multi-token probing and multi-token intervention: Non-Linear ITI (NL-ITI), which significantly enhances performance on evaluation benchmarks. NL-ITI is tested on diverse multiple-choice datasets, including TruthfulQA, on which we report over 16% relative MC1 (accuracy of model pointing to the correct answer) improvement with respect to the baseline ITI results. Moreover, we achieved a 10% relative improvement over the recently released Truth Forest (TrFf) method that also focused on ITI improvement.
|
[
"['Jakub Hoscilowicz' 'Adam Wiacek' 'Jan Chojnacki' 'Adam Cieslak'\n 'Leszek Michon' 'Vitalii Urbanevych' 'Artur Janicki']"
] |
null | null |
2403.18681
| null | null |
http://arxiv.org/pdf/2403.18681v1
|
2024-03-27T15:24:54Z
|
2024-03-27T15:24:54Z
|
TransFusion: Contrastive Learning with Transformers
|
This paper proposes a novel framework, TransFusion, designed to make the process of contrastive learning more analytical and explainable. TransFusion consists of attention blocks whose softmax being replaced by ReLU, and its final block's weighted-sum operation is truncated to leave the adjacency matrix as the output. The model is trained by minimizing the Jensen-Shannon Divergence between its output and the target affinity matrix, which indicates whether each pair of samples belongs to the same or different classes. The main contribution of TransFusion lies in defining a theoretical limit for answering two fundamental questions in the field: the maximum level of data augmentation and the minimum batch size required for effective contrastive learning. Furthermore, experimental results indicate that TransFusion successfully extracts features that isolate clusters from complex real-world data, leading to improved classification accuracy in downstream tasks.
|
[
"['Huanran Li' 'Daniel Pimentel-Alarcón']"
] |
null | null |
2403.18685
| null | null |
http://arxiv.org/pdf/2403.18685v1
|
2024-03-27T15:29:08Z
|
2024-03-27T15:29:08Z
|
Representatividad Muestral en la Incertidumbre Simétrica Multivariada
para la Selección de Atributos
|
In this work, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features. Experiments show how the number of attributes, their cardinalities, and the sample size affect the MSU. In this thesis, through observation of results, it is proposed an heuristic condition that preserves good quality in the MSU under different combinations of these three factors, providing a new useful criterion to help drive the process of dimension reduction. -- En el presente trabajo hemos analizado el comportamiento de una versi'on multivariada de la incertidumbre sim'etrica a trav'es de t'ecnicas de simulaci'on estad'isticas sobre varias combinaciones de atributos informativos y no-informativos generados de forma aleatoria. Los experimentos muestran como el n'umero de atributos, sus cardinalidades y el tama~no muestral afectan al MSU como medida. En esta tesis, mediante la observaci'on de resultados hemos propuesto una condici'on que preserva una buena calidad en el MSU bajo diferentes combinaciones de los tres factores mencionados, lo cual provee un nuevo y valioso criterio para llevar a cabo el proceso de reducci'on de dimensionalidad.
|
[
"['Gustavo Sosa-Cabrera']"
] |
null | null |
2403.18687
| null | null |
http://arxiv.org/pdf/2403.18687v1
|
2024-03-27T15:34:27Z
|
2024-03-27T15:34:27Z
|
InceptionTime vs. Wavelet -- A comparison for time series classification
|
Neural networks were used to classify infrasound data. Two different approaches were compared. One based on the direct classification of time series data, using a custom implementation of the InceptionTime network. For the other approach, we generated 2D images of the wavelet transformation of the signals, which were subsequently classified using a ResNet implementation. Choosing appropriate hyperparameter settings, both achieve a classification accuracy of above 90 %, with the direct approach reaching 95.2 %.
|
[
"['Daniel Klenkert' 'Daniel Schaeffer' 'Julian Stauch']"
] |
null | null |
2403.18699
| null | null |
http://arxiv.org/pdf/2403.18699v1
|
2024-03-27T15:48:16Z
|
2024-03-27T15:48:16Z
|
Contrastive Learning with Orthonormal Anchors (CLOA)
|
This study focuses on addressing the instability issues prevalent in contrastive learning, specifically examining the InfoNCE loss function and its derivatives. We reveal a critical observation that these loss functions exhibit a restrictive behavior, leading to a convergence phenomenon where embeddings tend to merge into a singular point. This "over-fusion" effect detrimentally affects classification accuracy in subsequent supervised-learning tasks. Through theoretical analysis, we demonstrate that embeddings, when equalized or confined to a rank-1 linear subspace, represent a local minimum for InfoNCE. In response to this challenge, our research introduces an innovative strategy that leverages the same or fewer labeled data than typically used in the fine-tuning phase. The loss we proposed, Orthonormal Anchor Regression Loss, is designed to disentangle embedding clusters, significantly enhancing the distinctiveness of each embedding while simultaneously ensuring their aggregation into dense, well-defined clusters. Our method demonstrates remarkable improvements with just a fraction of the conventional label requirements, as evidenced by our results on CIFAR10 and CIFAR100 datasets.
|
[
"['Huanran Li' 'Daniel Pimentel-Alarcón']"
] |
null | null |
2403.18703
| null | null |
http://arxiv.org/pdf/2403.18703v2
|
2024-03-28T09:44:06Z
|
2024-03-27T15:52:54Z
|
FPGA-Based Neural Thrust Controller for UAVs
|
The advent of unmanned aerial vehicles (UAVs) has improved a variety of fields by providing a versatile, cost-effective and accessible platform for implementing state-of-the-art algorithms. To accomplish a broader range of tasks, there is a growing need for enhanced on-board computing to cope with increasing complexity and dynamic environmental conditions. Recent advances have seen the application of Deep Neural Networks (DNNs), particularly in combination with Reinforcement Learning (RL), to improve the adaptability and performance of UAVs, especially in unknown environments. However, the computational requirements of DNNs pose a challenge to the limited computing resources available on many UAVs. This work explores the use of Field Programmable Gate Arrays (FPGAs) as a viable solution to this challenge, offering flexibility, high performance, energy and time efficiency. We propose a novel hardware board equipped with an Artix-7 FPGA for a popular open-source micro-UAV platform. We successfully validate its functionality by implementing an RL-based low-level controller using real-world experiments.
|
[
"['Sharif Azem' 'David Scheunert' 'Mengguang Li' 'Jonas Gehrunger'\n 'Kai Cui' 'Christian Hochberger' 'Heinz Koeppl']"
] |
null | null |
2403.18705
| null | null |
http://arxiv.org/pdf/2403.18705v2
|
2024-06-05T12:51:08Z
|
2024-03-27T15:54:55Z
|
Conditional Wasserstein Distances with Applications in Bayesian OT Flow
Matching
|
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation. While this approach also controls the distance between the posterior measures in the case of the Kullback--Leibler divergence, this is in general not hold true for the Wasserstein distance. In this paper, we introduce a conditional Wasserstein distance via a set of restricted couplings that equals the expected Wasserstein distance of the posteriors. Interestingly, the dual formulation of the conditional Wasserstein-1 flow resembles losses in the conditional Wasserstein GAN literature in a quite natural way. We derive theoretical properties of the conditional Wasserstein distance, characterize the corresponding geodesics and velocity fields as well as the flow ODEs. Subsequently, we propose to approximate the velocity fields by relaxing the conditional Wasserstein distance. Based on this, we propose an extension of OT Flow Matching for solving Bayesian inverse problems and demonstrate its numerical advantages on an inverse problem and class-conditional image generation.
|
[
"['Jannis Chemseddine' 'Paul Hagemann' 'Gabriele Steidl' 'Christian Wald']"
] |
null | null |
2403.18710
| null | null |
http://arxiv.org/pdf/2403.18710v2
|
2024-04-01T06:51:56Z
|
2024-03-27T15:57:42Z
|
Energy-Guided Data Sampling for Traffic Prediction with Mini Training
Datasets
|
Recent endeavors aimed at forecasting future traffic flow states through deep learning encounter various challenges and yield diverse outcomes. A notable obstacle arises from the substantial data requirements of deep learning models, a resource often scarce in traffic flow systems. Despite the abundance of domain knowledge concerning traffic flow dynamics, prevailing deep learning methodologies frequently fail to fully exploit it. To address these issues, we propose an innovative solution that merges Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) architecture to enhance the prediction of traffic flow dynamics. A key revelation of our research is the feasibility of sampling training data for large traffic systems from simulations conducted on smaller traffic systems. This insight suggests the potential for referencing a macroscopic-level distribution to inform the sampling of microscopic data. Such sampling is facilitated by the observed scale invariance in the normalized energy distribution of the statistical mechanics model, thereby streamlining the data generation process for large-scale traffic systems. Our simulations demonstrate promising agreement between predicted and actual traffic flow dynamics, underscoring the efficacy of our proposed approach.
|
[
"['Zhaohui Yang' 'Kshitij Jerath']"
] |
null | null |
2403.18717
| null | null |
http://arxiv.org/pdf/2403.18717v2
|
2024-07-12T14:13:41Z
|
2024-03-27T16:06:37Z
|
Semi-Supervised Learning for Deep Causal Generative Models
|
Developing models that are capable of answering questions of the form "How would x change if y had been z?'" is fundamental to advancing medical image analysis. Training causal generative models that address such counterfactual questions, though, currently requires that all relevant variables have been observed and that the corresponding labels are available in the training data. However, clinical data may not have complete records for all patients and state of the art causal generative models are unable to take full advantage of this. We thus develop, for the first time, a semi-supervised deep causal generative model that exploits the causal relationships between variables to maximise the use of all available data. We explore this in the setting where each sample is either fully labelled or fully unlabelled, as well as the more clinically realistic case of having different labels missing for each sample. We leverage techniques from causal inference to infer missing values and subsequently generate realistic counterfactuals, even for samples with incomplete labels.
|
[
"['Yasin Ibrahim' 'Hermione Warr' 'Konstantinos Kamnitsas']"
] |
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