categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.15837
| null | null |
http://arxiv.org/pdf/2403.15837v2
|
2024-03-27T08:54:06Z
|
2024-03-23T13:24:31Z
|
Centered Masking for Language-Image Pre-Training
|
We introduce Gaussian masking for Language-Image Pre-Training (GLIP) a novel, straightforward, and effective technique for masking image patches during pre-training of a vision-language model. GLIP builds on Fast Language-Image Pre-Training (FLIP), which randomly masks image patches while training a CLIP model. GLIP replaces random masking with centered masking, that uses a Gaussian distribution and is inspired by the importance of image patches at the center of the image. GLIP retains the same computational savings as FLIP, while improving performance across a range of downstream datasets and tasks, as demonstrated by our experimental results. We show the benefits of GLIP to be easy to obtain, requiring no delicate tuning of the Gaussian, and also applicable to data sets containing images without an obvious center focus.
|
[
"['Mingliang Liang' 'Martha Larson']"
] |
null | null |
2403.15839
| null | null |
http://arxiv.org/pdf/2403.15839v1
|
2024-03-23T13:28:37Z
|
2024-03-23T13:28:37Z
|
TablePuppet: A Generic Framework for Relational Federated Learning
|
Current federated learning (FL) approaches view decentralized training data as a single table, divided among participants either horizontally (by rows) or vertically (by columns). However, these approaches are inadequate for handling distributed relational tables across databases. This scenario requires intricate SQL operations like joins and unions to obtain the training data, which is either costly or restricted by privacy concerns. This raises the question: can we directly run FL on distributed relational tables? In this paper, we formalize this problem as relational federated learning (RFL). We propose TablePuppet, a generic framework for RFL that decomposes the learning process into two steps: (1) learning over join (LoJ) followed by (2) learning over union (LoU). In a nutshell, LoJ pushes learning down onto the vertical tables being joined, and LoU further pushes learning down onto the horizontal partitions of each vertical table. TablePuppet incorporates computation/communication optimizations to deal with the duplicate tuples introduced by joins, as well as differential privacy (DP) to protect against both feature and label leakages. We demonstrate the efficiency of TablePuppet in combination with two widely-used ML training algorithms, stochastic gradient descent (SGD) and alternating direction method of multipliers (ADMM), and compare their computation/communication complexity. We evaluate the SGD/ADMM algorithms developed atop TablePuppet by training diverse ML models. Our experimental results show that TablePuppet achieves model accuracy comparable to the centralized baselines running directly atop the SQL results. Moreover, ADMM takes less communication time than SGD to converge to similar model accuracy.
|
[
"['Lijie Xu' 'Chulin Xie' 'Yiran Guo' 'Gustavo Alonso' 'Bo Li'\n 'Guoliang Li' 'Wei Wang' 'Wentao Wu' 'Ce Zhang']"
] |
null | null |
2403.15855
| null | null |
http://arxiv.org/pdf/2403.15855v2
|
2024-05-22T17:44:44Z
|
2024-03-23T14:24:36Z
|
Initialisation and Topology Effects in Decentralised Federated Learning
|
Fully decentralised federated learning enables collaborative training of individual machine learning models on distributed devices on a communication network while keeping the training data localised. This approach enhances data privacy and eliminates both the single point of failure and the necessity for central coordination. Our research highlights that the effectiveness of decentralised federated learning is significantly influenced by the network topology of connected devices. We propose a strategy for uncoordinated initialisation of the artificial neural networks, which leverages the distribution of eigenvector centralities of the nodes of the underlying communication network, leading to a radically improved training efficiency. Additionally, our study explores the scaling behaviour and choice of environmental parameters under our proposed initialisation strategy. This work paves the way for more efficient and scalable artificial neural network training in a distributed and uncoordinated environment, offering a deeper understanding of the intertwining roles of network structure and learning dynamics.
|
[
"['Arash Badie-Modiri' 'Chiara Boldrini' 'Lorenzo Valerio' 'János Kertész'\n 'Márton Karsai']"
] |
null | null |
2403.15881
| null | null |
http://arxiv.org/pdf/2403.15881v1
|
2024-03-23T16:21:22Z
|
2024-03-23T16:21:22Z
|
Fast and Unified Path Gradient Estimators for Normalizing Flows
|
Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training. However, they are often prohibitively more expensive from a computational point of view and cannot be applied to maximum likelihood training in a scalable manner, which severely hinders their widespread adoption. In this work, we overcome these crucial limitations. Specifically, we propose a fast path gradient estimator which improves computational efficiency significantly and works for all normalizing flow architectures of practical relevance. We then show that this estimator can also be applied to maximum likelihood training for which it has a regularizing effect as it can take the form of a given target energy function into account. We empirically establish its superior performance and reduced variance for several natural sciences applications.
|
[
"['Lorenz Vaitl' 'Ludwig Winkler' 'Lorenz Richter' 'Pan Kessel']"
] |
null | null |
2403.15886
| null | null |
http://arxiv.org/pdf/2403.15886v1
|
2024-03-23T16:51:52Z
|
2024-03-23T16:51:52Z
|
Leveraging Zero-Shot Prompting for Efficient Language Model Distillation
|
This paper introduces a novel approach for efficiently distilling LLMs into smaller, application-specific models, significantly reducing operational costs and manual labor. Addressing the challenge of deploying computationally intensive LLMs in specific applications or edge devices, this technique utilizes LLMs' reasoning capabilities to generate labels and natural language rationales for unlabeled data. Our approach enhances both finetuning and distillation by employing a multi-task training framework where student models mimic these rationales alongside teacher predictions. Key contributions include the employment of zero-shot prompting to elicit teacher model rationales, reducing the necessity for handcrafted few-shot examples and lowering the overall token count required, which directly translates to cost savings given the pay-per-token billing model of major tech companies' LLM APIs. Additionally, the paper investigates the impact of explanation properties on distillation efficiency, demonstrating that minimal performance loss occurs even when rationale augmentation is not applied across the entire dataset, facilitating further reductions of tokens. This research marks a step toward the efficient training of task-specific models with minimal human intervention, offering substantial cost-savings while maintaining, or even enhancing, performance.
|
[
"['Lukas Vöge' 'Vincent Gurgul' 'Stefan Lessmann']"
] |
null | null |
2403.15905
| null | null |
http://arxiv.org/pdf/2403.15905v4
|
2024-03-29T16:53:58Z
|
2024-03-23T18:19:02Z
|
Towards Low-Energy Adaptive Personalization for Resource-Constrained
Devices
|
The personalization of machine learning (ML) models to address data drift is a significant challenge in the context of Internet of Things (IoT) applications. Presently, most approaches focus on fine-tuning either the full base model or its last few layers to adapt to new data, while often neglecting energy costs. However, various types of data drift exist, and fine-tuning the full base model or the last few layers may not result in optimal performance in certain scenarios. We propose Target Block Fine-Tuning (TBFT), a low-energy adaptive personalization framework designed for resource-constrained devices. We categorize data drift and personalization into three types: input-level, feature-level, and output-level. For each type, we fine-tune different blocks of the model to achieve optimal performance with reduced energy costs. Specifically, input-, feature-, and output-level correspond to fine-tuning the front, middle, and rear blocks of the model. We evaluate TBFT on a ResNet model, three datasets, three different training sizes, and a Raspberry Pi. Compared with the $Block Avg$, where each block is fine-tuned individually and their performance improvements are averaged, TBFT exhibits an improvement in model accuracy by an average of 15.30% whilst saving 41.57% energy consumption on average compared with full fine-tuning.
|
[
"['Yushan Huang' 'Josh Millar' 'Yuxuan Long' 'Yuchen Zhao' 'Hamed Haddadi']"
] |
null | null |
2403.15908
| null | null |
http://arxiv.org/abs/2403.15908v1
|
2024-03-23T18:42:22Z
|
2024-03-23T18:42:22Z
|
Deep Gaussian Covariance Network with Trajectory Sampling for
Data-Efficient Policy Search
|
Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and acquire new samples. Moreover, the uncertainty-aware learning procedures in probabilistic approaches lead to robust policies that are less sensitive to noisy observations compared to uncertainty unaware solutions. We propose to combine trajectory sampling and deep Gaussian covariance network (DGCN) for a data-efficient solution to MBRL problems in an optimal control setting. We compare trajectory sampling with density-based approximation for uncertainty propagation using three different probabilistic world models; Gaussian processes, Bayesian neural networks, and DGCNs. We provide empirical evidence using four different well-known test environments, that our method improves the sample-efficiency over other combinations of uncertainty propagation methods and probabilistic models. During our tests, we place particular emphasis on the robustness of the learned policies with respect to noisy initial states.
|
[
"['Can Bogoclu' 'Robert Vosshall' 'Kevin Cremanns' 'Dirk Roos']"
] |
null | null |
2403.15928
| null | null |
http://arxiv.org/pdf/2403.15928v1
|
2024-03-23T20:22:30Z
|
2024-03-23T20:22:30Z
|
Safe Reinforcement Learning for Constrained Markov Decision Processes
with Stochastic Stopping Time
|
In this paper, we present an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint. Despite the necessary attention of the scientific community, considering stochastic stopping time, the problem of learning optimal policy without violating safety constraints during the learning phase is yet to be addressed. To this end, we propose an algorithm based on linear programming that does not require a process model. We show that the learned policy is safe with high confidence. We also propose a method to compute a safe baseline policy, which is central in developing algorithms that do not violate the safety constraints. Finally, we provide simulation results to show the efficacy of the proposed algorithm. Further, we demonstrate that efficient exploration can be achieved by defining a subset of the state-space called proxy set.
|
[
"['Abhijit Mazumdar' 'Rafal Wisniewski' 'Manuela L. Bujorianu']"
] |
null | null |
2403.15933
| null | null |
http://arxiv.org/pdf/2403.15933v3
|
2024-06-03T15:30:52Z
|
2024-03-23T21:16:56Z
|
Understanding Domain-Size Generalization in Markov Logic Networks
|
We study the generalization behavior of Markov Logic Networks (MLNs) across relational structures of different sizes. Multiple works have noticed that MLNs learned on a given domain generalize poorly across domains of different sizes. This behavior emerges from a lack of internal consistency within an MLN when used across different domain sizes. In this paper, we quantify this inconsistency and bound it in terms of the variance of the MLN parameters. The parameter variance also bounds the KL divergence between an MLN's marginal distributions taken from different domain sizes. We use these bounds to show that maximizing the data log-likelihood while simultaneously minimizing the parameter variance corresponds to two natural notions of generalization across domain sizes. Our theoretical results apply to Exponential Random Graphs and other Markov network based relational models. Finally, we observe that solutions known to decrease the variance of the MLN parameters, like regularization and Domain-Size Aware MLNs, increase the internal consistency of the MLNs. We empirically verify our results on four different datasets, with different methods to control parameter variance, showing that controlling parameter variance leads to better generalization.
|
[
"['Florian Chen' 'Felix Weitkämper' 'Sagar Malhotra']"
] |
null | null |
2403.15935
| null | null |
http://arxiv.org/pdf/2403.15935v1
|
2024-03-23T21:39:56Z
|
2024-03-23T21:39:56Z
|
Sample and Communication Efficient Fully Decentralized MARL Policy
Evaluation via a New Approach: Local TD update
|
In actor-critic framework for fully decentralized multi-agent reinforcement learning (MARL), one of the key components is the MARL policy evaluation (PE) problem, where a set of $N$ agents work cooperatively to evaluate the value function of the global states for a given policy through communicating with their neighbors. In MARL-PE, a critical challenge is how to lower the sample and communication complexities, which are defined as the number of training samples and communication rounds needed to converge to some $epsilon$-stationary point. To lower communication complexity in MARL-PE, a "natural'' idea is to perform multiple local TD-update steps between each consecutive rounds of communication to reduce the communication frequency. However, the validity of the local TD-update approach remains unclear due to the potential "agent-drift'' phenomenon resulting from heterogeneous rewards across agents in general. This leads to an interesting open question: Can the local TD-update approach entail low sample and communication complexities? In this paper, we make the first attempt to answer this fundamental question. We focus on the setting of MARL-PE with average reward, which is motivated by many multi-agent network optimization problems. Our theoretical and experimental results confirm that allowing multiple local TD-update steps is indeed an effective approach in lowering the sample and communication complexities of MARL-PE compared to consensus-based MARL-PE algorithms. Specifically, the local TD-update steps between two consecutive communication rounds can be as large as $mathcal{O}(1/epsilon^{1/2}log{(1/epsilon)})$ in order to converge to an $epsilon$-stationary point of MARL-PE. Moreover, we show theoretically that in order to reach the optimal sample complexity, the communication complexity of local TD-update approach is $mathcal{O}(1/epsilon^{1/2}log{(1/epsilon)})$.
|
[
"['Fnu Hairi' 'Zifan Zhang' 'Jia Liu']"
] |
null | null |
2403.15938
| null | null |
http://arxiv.org/pdf/2403.15938v1
|
2024-03-23T21:54:34Z
|
2024-03-23T21:54:34Z
|
LlamBERT: Large-scale low-cost data annotation in NLP
|
Large Language Models (LLMs), such as GPT-4 and Llama 2, show remarkable proficiency in a wide range of natural language processing (NLP) tasks. Despite their effectiveness, the high costs associated with their use pose a challenge. We present LlamBERT, a hybrid approach that leverages LLMs to annotate a small subset of large, unlabeled databases and uses the results for fine-tuning transformer encoders like BERT and RoBERTa. This strategy is evaluated on two diverse datasets: the IMDb review dataset and the UMLS Meta-Thesaurus. Our results indicate that the LlamBERT approach slightly compromises on accuracy while offering much greater cost-effectiveness.
|
[
"['Bálint Csanády' 'Lajos Muzsai' 'Péter Vedres' 'Zoltán Nádasdy'\n 'András Lukács']"
] |
null | null |
2403.15941
| null | null |
http://arxiv.org/pdf/2403.15941v3
|
2024-07-07T19:40:31Z
|
2024-03-23T22:04:03Z
|
Explore until Confident: Efficient Exploration for Embodied Question
Answering
|
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question. In this work, we leverage the strong semantic reasoning capabilities of large vision-language models (VLMs) to efficiently explore and answer such questions. However, there are two main challenges when using VLMs in EQA: they do not have an internal memory for mapping the scene to be able to plan how to explore over time, and their confidence can be miscalibrated and can cause the robot to prematurely stop exploration or over-explore. We propose a method that first builds a semantic map of the scene based on depth information and via visual prompting of a VLM - leveraging its vast knowledge of relevant regions of the scene for exploration. Next, we use conformal prediction to calibrate the VLM's question answering confidence, allowing the robot to know when to stop exploration - leading to a more calibrated and efficient exploration strategy. To test our framework in simulation, we also contribute a new EQA dataset with diverse, realistic human-robot scenarios and scenes built upon the Habitat-Matterport 3D Research Dataset (HM3D). Both simulated and real robot experiments show our proposed approach improves the performance and efficiency over baselines that do no leverage VLM for exploration or do not calibrate its confidence. Webpage with experiment videos and code: https://explore-eqa.github.io/
|
[
"['Allen Z. Ren' 'Jaden Clark' 'Anushri Dixit' 'Masha Itkina'\n 'Anirudha Majumdar' 'Dorsa Sadigh']"
] |
null | null |
2403.15953
| null | null |
http://arxiv.org/pdf/2403.15953v1
|
2024-03-23T23:14:37Z
|
2024-03-23T23:14:37Z
|
Understanding The Effectiveness of Lossy Compression in Machine Learning
Training Sets
|
Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of floating point data for training and validation which need methods to share the data on a wide area network (WAN) or to transfer it from edge devices to data centers. Data compression can be a solution to these problems, but an in-depth understanding of how lossy compression affects model quality is needed. Prior work largely considers a single application or compression method. We designed a systematic methodology for evaluating data reduction techniques for ML/AI, and we use it to perform a very comprehensive evaluation with 17 data reduction methods on 7 ML/AI applications to show modern lossy compression methods can achieve a 50-100x compression ratio improvement for a 1% or less loss in quality. We identify critical insights that guide the future use and design of lossy compressors for ML/AI.
|
[
"['Robert Underwood' 'Jon C. Calhoun' 'Sheng Di' 'Franck Cappello']"
] |
null | null |
2403.15962
| null | null |
http://arxiv.org/pdf/2403.15962v1
|
2024-03-23T23:49:01Z
|
2024-03-23T23:49:01Z
|
Detection of Problem Gambling with Less Features Using Machine Learning
Methods
|
Analytic features in gambling study are performed based on the amount of data monitoring on user daily actions. While performing the detection of problem gambling, existing datasets provide relatively rich analytic features for building machine learning based model. However, considering the complexity and cost of collecting the analytic features in real applications, conducting precise detection with less features will tremendously reduce the cost of data collection. In this study, we propose a deep neural networks PGN4 that performs well when using limited analytic features. Through the experiment on two datasets, we discover that PGN4 only experiences a mere performance drop when cutting 102 features to 5 features. Besides, we find the commonality within the top 5 features from two datasets.
|
[
"['Yang Jiao' 'Gloria Wong-Padoongpatt' 'Mei Yang']"
] |
null | null |
2403.15974
| null | null |
http://arxiv.org/pdf/2403.15974v1
|
2024-03-24T00:46:40Z
|
2024-03-24T00:46:40Z
|
CBGT-Net: A Neuromimetic Architecture for Robust Classification of
Streaming Data
|
This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains. Unlike traditional neural network models, which either generate an output for each provided input, or an output after a fixed sequence of inputs, the CBGT-Net learns to produce an output after a sufficient criteria for evidence is achieved from a stream of observed data. For each observation, the CBGT-Net generates a vector that explicitly represents the amount of evidence the observation provides for each potential decision, accumulates the evidence over time, and generates a decision when the accumulated evidence exceeds a pre-defined threshold. We evaluate the proposed model on two image classification tasks, where models need to predict image categories based on a stream of small patches extracted from the image. We show that the CBGT-Net provides improved accuracy and robustness compared to models trained to classify from a single patch, and models leveraging an LSTM layer to classify from a fixed sequence length of patches.
|
[
"['Shreya Sharma' 'Dana Hughes' 'Katia Sycara']"
] |
null | null |
2403.15989
| null | null |
http://arxiv.org/pdf/2403.15989v2
|
2024-05-01T20:57:12Z
|
2024-03-24T02:54:46Z
|
Knowledge-guided Machine Learning: Current Trends and Future Prospects
|
This paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based models. It also provides an introduction to the current state of research in the emerging field of scientific knowledge-guided machine learning (KGML) that aims to use both scientific knowledge and data in ML frameworks to achieve better generalizability, scientific consistency, and explainability of results. We discuss different facets of KGML research in terms of the type of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML. We also discuss some of the common categories of use cases in environmental sciences where KGML methods are being developed, using illustrative examples in each category.
|
[
"['Anuj Karpatne' 'Xiaowei Jia' 'Vipin Kumar']"
] |
null | null |
2403.15999
| null | null |
http://arxiv.org/pdf/2403.15999v1
|
2024-03-24T03:57:21Z
|
2024-03-24T03:57:21Z
|
Near-Optimal differentially private low-rank trace regression with
guaranteed private initialization
|
We study differentially private (DP) estimation of a rank-$r$ matrix $M in mathbb{R}^{d_1times d_2}$ under the trace regression model with Gaussian measurement matrices. Theoretically, the sensitivity of non-private spectral initialization is precisely characterized, and the differential-privacy-constrained minimax lower bound for estimating $M$ under the Schatten-$q$ norm is established. Methodologically, the paper introduces a computationally efficient algorithm for DP-initialization with a sample size of $n geq widetilde O (r^2 (d_1vee d_2))$. Under certain regularity conditions, the DP-initialization falls within a local ball surrounding $M$. We also propose a differentially private algorithm for estimating $M$ based on Riemannian optimization (DP-RGrad), which achieves a near-optimal convergence rate with the DP-initialization and sample size of $n geq widetilde O(r (d_1 + d_2))$. Finally, the paper discusses the non-trivial gap between the minimax lower bound and the upper bound of low-rank matrix estimation under the trace regression model. It is shown that the estimator given by DP-RGrad attains the optimal convergence rate in a weaker notion of differential privacy. Our powerful technique for analyzing the sensitivity of initialization requires no eigengap condition between $r$ non-zero singular values.
|
[
"['Mengyue Zha']"
] |
null | null |
2403.16004
| null | null |
http://arxiv.org/pdf/2403.16004v1
|
2024-03-24T04:23:43Z
|
2024-03-24T04:23:43Z
|
A Federated Parameter Aggregation Method for Node Classification Tasks
with Different Graph Network Structures
|
Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sources without compromising privacy. However, in the area of graph neural networks, the nodes and network structures of graphs held by clients are different in many practical applications, and the aggregation method that directly shares model gradients cannot be directly applied to this scenario. Therefore, this work proposes a federated aggregation method FLGNN applied to various graph federation scenarios and investigates the aggregation effect of parameter sharing at each layer of the graph neural network model. The effectiveness of the federated aggregation method FLGNN is verified by experiments on real datasets. Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments. The results show that FLGNN performs good robustness, and the success rate of privacy theft is further reduced by adding differential privacy defense methods.
|
[
"['Hao Song' 'Jiacheng Yao' 'Zhengxi Li' 'Shaocong Xu' 'Shibo Jin'\n 'Jiajun Zhou' 'Chenbo Fu' 'Qi Xuan' 'Shanqing Yu']"
] |
null | null |
2403.16024
| null | null |
http://arxiv.org/pdf/2403.16024v1
|
2024-03-24T05:57:00Z
|
2024-03-24T05:57:00Z
|
A Unified Module for Accelerating STABLE-DIFFUSION: LCM-LORA
|
This paper presents a comprehensive study on the unified module for accelerating stable-diffusion processes, specifically focusing on the lcm-lora module. Stable-diffusion processes play a crucial role in various scientific and engineering domains, and their acceleration is of paramount importance for efficient computational performance. The standard iterative procedures for solving fixed-source discrete ordinates problems often exhibit slow convergence, particularly in optically thick scenarios. To address this challenge, unconditionally stable diffusion-acceleration methods have been developed, aiming to enhance the computational efficiency of transport equations and discrete ordinates problems. This study delves into the theoretical foundations and numerical results of unconditionally stable diffusion synthetic acceleration methods, providing insights into their stability and performance for model discrete ordinates problems. Furthermore, the paper explores recent advancements in diffusion model acceleration, including on device acceleration of large diffusion models via gpu aware optimizations, highlighting the potential for significantly improved inference latency. The results and analyses in this study provide important insights into stable diffusion processes and have important ramifications for the creation and application of acceleration methods specifically, the lcm-lora module in a variety of computing environments.
|
[
"['Ayush Thakur' 'Rashmi Vashisth']"
] |
null | null |
2403.16028
| null | null |
http://arxiv.org/pdf/2403.16028v1
|
2024-03-24T06:10:22Z
|
2024-03-24T06:10:22Z
|
Exploring the Impact of Dataset Bias on Dataset Distillation
|
Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that preserves essential information from the original dataset. This synthetic dataset can serve as a substitute for the original large-scale one, and help alleviate the training workload. However, current DD methods typically operate under the assumption that the dataset is unbiased, overlooking potential bias issues within the dataset itself. To fill in this blank, we systematically investigate the influence of dataset bias on DD. To the best of our knowledge, this is the first exploration in the DD domain. Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis. Then we utilize existing DD methods to generate synthetic datasets on CMNIST-DD and CCIFAR10-DD, and evaluate their performance following the standard process. Experiments demonstrate that biases present in the original dataset significantly impact the performance of the synthetic dataset in most cases, which highlights the necessity of identifying and mitigating biases in the original datasets during DD. Finally, we reformulate DD within the context of a biased dataset. Our code along with biased datasets are available at https://github.com/yaolu-zjut/Biased-DD.
|
[
"['Yao Lu' 'Jianyang Gu' 'Xuguang Chen' 'Saeed Vahidian' 'Qi Xuan']"
] |
null | null |
2403.16030
| null | null |
http://arxiv.org/pdf/2403.16030v1
|
2024-03-24T06:10:56Z
|
2024-03-24T06:10:56Z
|
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
|
Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of graphs. Graph transformer conventionally performs dense attention (or global attention) for every pair of nodes to learn node representation vectors, resulting in quadratic computational costs that are unaffordable for large-scale graph data. Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations. Facing this bottleneck, (1) we start by assigning each node a token list that is sampled by personalized PageRank (PPR) and then apply standard multi-head self-attention only on this list to compute its node representations. This PPR tokenization method decouples model training from complex graph topological information and makes heavy feature engineering offline and independent, such that mini-batch training of graph transformers is possible by loading each node's token list in batches. We further prove this PPR tokenization is viable as a graph convolution network with a fixed polynomial filter and jumping knowledge. However, only using personalized PageRank may limit information carried by a token list, which could not support different graph inductive biases for model training. To this end, (2) we rewire graphs by introducing multiple types of virtual connections through structure- and content-based super nodes that enable PPR tokenization to encode local and global contexts, long-range interaction, and heterophilous information into each node's token list, and then formalize our Virtual Connection Ranking based Graph Transformer (VCR-Graphormer).
|
[
"['Dongqi Fu' 'Zhigang Hua' 'Yan Xie' 'Jin Fang' 'Si Zhang' 'Kaan Sancak'\n 'Hao Wu' 'Andrey Malevich' 'Jingrui He' 'Bo Long']"
] |
null | null |
2403.16031
| null | null |
http://arxiv.org/pdf/2403.16031v1
|
2024-03-24T06:14:50Z
|
2024-03-24T06:14:50Z
|
Learning Directed Acyclic Graphs from Partial Orderings
|
Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistically challenging. Moreover, without additional information, the direction of edges may not be estimable from observational data. In contrast, given a complete causal ordering of the variables, the problem can be solved efficiently, even in high dimensions. In this paper, we consider the intermediate problem of learning DAGs when a partial causal ordering of variables is available. We propose a general estimation framework for leveraging the partial ordering and present efficient estimation algorithms for low- and high-dimensional problems. The advantages of the proposed framework are illustrated via numerical studies.
|
[
"['Ali Shojaie' 'Wenyu Chen']"
] |
null | null |
2403.16033
| null | null |
http://arxiv.org/pdf/2403.16033v1
|
2024-03-24T06:28:54Z
|
2024-03-24T06:28:54Z
|
Node Classification via Semantic-Structural Attention-Enhanced Graph
Convolutional Networks
|
Graph data, also known as complex network data, is omnipresent across various domains and applications. Prior graph neural network models primarily focused on extracting task-specific structural features through supervised learning objectives, but they fell short in capturing the inherent semantic and structural features of the entire graph. In this paper, we introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN), which not only models the graph structure but also extracts generalized unsupervised features to enhance vertex classification performance. The SSA-GCN's key contributions lie in three aspects: firstly, it derives semantic information through unsupervised feature extraction from a knowledge graph perspective; secondly, it obtains structural information through unsupervised feature extraction from a complex network perspective; and finally, it integrates these features through a cross-attention mechanism. By leveraging these features, we augment the graph convolutional network, thereby enhancing the model's generalization capabilities. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method. Furthermore, our approach also exhibits excellent accuracy under privacy settings, making it a robust and effective solution for graph data analysis.
|
[
"['Hongyin Zhu']"
] |
null | null |
2403.16049
| null | null |
http://arxiv.org/abs/2403.16049v2
|
2024-05-26T04:58:09Z
|
2024-03-24T07:29:12Z
|
Improving Demand Forecasting in Open Systems with Cartogram-Enhanced
Deep Learning
|
Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long-period prediction, which has not been achieved before. We apply this method to public bicycle rental-and-return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.
|
[
"['Sangjoon Park' 'Yongsung Kwon' 'Hyungjoon Soh' 'Mi Jin Lee'\n 'Seung-Woo Son']"
] |
null | null |
2403.16059
| null | null |
http://arxiv.org/pdf/2403.16059v1
|
2024-03-24T08:06:34Z
|
2024-03-24T08:06:34Z
|
Manifold Regularization Classification Model Based On Improved Diffusion
Map
|
Manifold regularization model is a semi-supervised learning model that leverages the geometric structure of a dataset, comprising a small number of labeled samples and a large number of unlabeled samples, to generate classifiers. However, the original manifold norm limits the performance of models to local regions. To address this limitation, this paper proposes an approach to improve manifold regularization based on a label propagation model. We initially enhance the probability transition matrix of the diffusion map algorithm, which can be used to estimate the Neumann heat kernel, enabling it to accurately depict the label propagation process on the manifold. Using this matrix, we establish a label propagation function on the dataset to describe the distribution of labels at different time steps. Subsequently, we extend the label propagation function to the entire data manifold. We prove that the extended label propagation function converges to a stable distribution after a sufficiently long time and can be considered as a classifier. Building upon this concept, we propose a viable improvement to the manifold regularization model and validate its superiority through experiments.
|
[
"['Hongfu Guo' 'Wencheng Zou' 'Zeyu Zhang' 'Shuishan Zhang' 'Ruitong Wang'\n 'Jintao Zhang']"
] |
null | null |
2403.16075
| null | null |
http://arxiv.org/pdf/2403.16075v1
|
2024-03-24T09:33:45Z
|
2024-03-24T09:33:45Z
|
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral
Evolution History
|
Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. However, in many streaming applications, such as streaming recommender systems, online decision-makers typically engage in online learning during the decision-making process, meaning that the interaction history generated by online decision-makers includes their behavioral evolution from novice expert to experienced expert. This poses a new challenge for existing imitation learning approaches that can only utilize data from experienced experts. To address this issue, this paper proposes an inverse batched contextual bandit (IBCB) framework that can efficiently perform estimations of environment reward parameters and learned policy based on the expert's behavioral evolution history. Specifically, IBCB formulates the inverse problem into a simple quadratic programming problem by utilizing the behavioral evolution history of the batched contextual bandit with inaccessible rewards. We demonstrate that IBCB is a unified framework for both deterministic and randomized bandit policies. The experimental results indicate that IBCB outperforms several existing imitation learning algorithms on synthetic and real-world data and significantly reduces running time. Additionally, empirical analyses reveal that IBCB exhibits better out-of-distribution generalization and is highly effective in learning the bandit policy from the interaction history of novice experts.
|
[
"['Yi Xu' 'Weiran Shen' 'Xiao Zhang' 'Jun Xu']"
] |
null | null |
2403.16087
| null | null |
http://arxiv.org/pdf/2403.16087v1
|
2024-03-24T10:57:08Z
|
2024-03-24T10:57:08Z
|
LLMs as Compiler for Arabic Programming Language
|
In this paper we introduce APL (Arabic Programming Language) that uses Large language models (LLM) as semi-compiler to covert Arabic text code to python code then run the code. Designing a full pipeline from the structure of the APL text then a prompt (using prompt engineering) then running the prodcued python code using PyRunner. This project has a three parts first python library, a playground with simple interface and this research paper.
|
[
"['Serry Sibaee' 'Omar Najar' 'Lahouri Ghouti' 'Anis Koubaa']"
] |
null | null |
2403.16099
| null | null |
http://arxiv.org/pdf/2403.16099v2
|
2024-04-11T09:58:17Z
|
2024-03-24T11:29:55Z
|
A Multi-Label Dataset of French Fake News: Human and Machine Insights
|
We present a corpus of 100 documents, OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic of fake news, and compare them to the predictions of automated classifiers. We present a topic and genre analysis using Gate Cloud, indicative of the prevalence of satire-like text in the corpus. We then use the subjectivity analyzer VAGO, and a neural version of it, to clarify the link between ascriptions of the label Subjective and ascriptions of the label Fake News. The annotated dataset is available online at the following url: https://github.com/obs-info/obsinfox Keywords: Fake News, Multi-Labels, Subjectivity, Vagueness, Detail, Opinion, Exaggeration, French Press
|
[
"['Benjamin Icard' 'François Maine' 'Morgane Casanova' 'Géraud Faye'\n 'Julien Chanson' 'Guillaume Gadek' 'Ghislain Atemezing'\n 'François Bancilhon' 'Paul Égré']"
] |
null | null |
2403.16108
| null | null |
http://arxiv.org/pdf/2403.16108v2
|
2024-03-30T23:46:29Z
|
2024-03-24T11:52:39Z
|
A Transformer approach for Electricity Price Forecasting
|
This paper presents a novel approach to electricity price forecasting (EPF) using a pure Transformer model. As opposed to other alternatives, no other recurrent network is used in combination to the attention mechanism. Hence, showing that the attention layer is enough for capturing the temporal patterns. The paper also provides fair comparison of the models using the open-source EPF toolbox and provide the code to enhance reproducibility and transparency in EPF research. The results show that the Transformer model outperforms traditional methods, offering a promising solution for reliable and sustainable power system operation.
|
[
"['Oscar Llorente' 'Jose Portela']"
] |
null | null |
2403.16112
| null | null |
http://arxiv.org/pdf/2403.16112v1
|
2024-03-24T12:05:23Z
|
2024-03-24T12:05:23Z
|
Opportunities and challenges in the application of large artificial
intelligence models in radiology
|
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models. Secondly, we summarize the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology. Finally, this paper also summarizes some of the challenges of large AI models in radiology, with the aim of better promoting the rapid revolution in the field of radiography.
|
[
"['Liangrui Pan' 'Zhenyu Zhao' 'Ying Lu' 'Kewei Tang' 'Liyong Fu'\n 'Qingchun Liang' 'Shaoliang Peng']"
] |
null | null |
2403.16116
| null | null |
http://arxiv.org/pdf/2403.16116v1
|
2024-03-24T12:15:28Z
|
2024-03-24T12:15:28Z
|
Self-Supervised Multi-Frame Neural Scene Flow
|
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their success, the underlying reasons for their astonishing generalization capabilities remain unclear. Our research addresses this gap by examining the generalization capabilities of NSFP through the lens of uniform stability, revealing that its performance is inversely proportional to the number of input point clouds. This finding sheds light on NSFP's effectiveness in handling large-scale point cloud scene flow estimation tasks. Motivated by such theoretical insights, we further explore the improvement of scene flow estimation by leveraging historical point clouds across multiple frames, which inherently increases the number of point clouds. Consequently, we propose a simple and effective method for multi-frame point cloud scene flow estimation, along with a theoretical evaluation of its generalization abilities. Our analysis confirms that the proposed method maintains a limited generalization error, suggesting that adding multiple frames to the scene flow optimization process does not detract from its generalizability. Extensive experimental results on large-scale autonomous driving Waymo Open and Argoverse lidar datasets demonstrate that the proposed method achieves state-of-the-art performance.
|
[
"['Dongrui Liu' 'Daqi Liu' 'Xueqian Li' 'Sihao Lin' 'Hongwei xie'\n 'Bing Wang' 'Xiaojun Chang' 'Lei Chu']"
] |
null | null |
2403.16125
| null | null |
http://arxiv.org/pdf/2403.16125v1
|
2024-03-24T12:43:04Z
|
2024-03-24T12:43:04Z
|
A Codesign of Scheduling and Parallelization for Large Model Training in
Heterogeneous Clusters
|
Joint consideration of scheduling and adaptive parallelism offers great opportunities for improving the training efficiency of large models on heterogeneous GPU clusters. However, integrating adaptive parallelism into a cluster scheduler expands the cluster scheduling space. The new space is the product of the original scheduling space and the parallelism exploration space of adaptive parallelism (also a product of pipeline, data, and tensor parallelism). The exponentially enlarged scheduling space and ever-changing optimal parallelism plan from adaptive parallelism together result in the contradiction between low-overhead and accurate performance data acquisition for efficient cluster scheduling. This paper presents Crius, a training system for efficiently scheduling multiple large models with adaptive parallelism in a heterogeneous cluster. Crius proposes a novel scheduling granularity called Cell. It represents a job with deterministic resources and pipeline stages. The exploration space of Cell is shrunk to the product of only data and tensor parallelism, thus exposing the potential for accurate and low-overhead performance estimation. Crius then accurately estimates Cells and efficiently schedules training jobs. When a Cell is selected as a scheduling choice, its represented job runs with the optimal parallelism plan explored. Experimental results show that Crius reduces job completion time by up to 48.9% and schedules large models with up to 1.49x cluster throughput improvement.
|
[
"['Chunyu Xue' 'Weihao Cui' 'Han Zhao' 'Quan Chen' 'Shulai Zhang'\n 'Pengyu Yang' 'Jing Yang' 'Shaobo Li' 'Minyi Guo']"
] |
null | null |
2403.16130
| null | null |
http://arxiv.org/pdf/2403.16130v1
|
2024-03-24T13:01:05Z
|
2024-03-24T13:01:05Z
|
AKBR: Learning Adaptive Kernel-based Representations for Graph
Classification
|
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between graphs and cannot provide an end-to-end learning mechanism for the classifier, the proposed AKBR approach aims to define an end-to-end representation learning model to construct an adaptive kernel matrix for graphs. To this end, we commence by leveraging a novel feature-channel attention mechanism to capture the interdependencies between different substructure invariants of original graphs. The proposed AKBR model can thus effectively identify the structural importance of different substructures, and compute the R-convolution kernel between pairwise graphs associated with the more significant substructures specified by their structural attentions. Since each row of the resulting kernel matrix can be theoretically seen as the embedding vector of a sample graph, the proposed AKBR model is able to directly employ the resulting kernel matrix as the graph feature matrix and input it into the classifier for classification (i.e., the SoftMax layer), naturally providing an end-to-end learning architecture between the kernel computation as well as the classifier. Experimental results show that the proposed AKBR model outperforms existing state-of-the-art graph kernels and deep learning methods on standard graph benchmarks.
|
[
"['Feifei Qian' 'Lixin Cui' 'Yue Wang' 'Hangyuan Du' 'Lu Bai'\n 'Edwin R. Hancock']"
] |
null | null |
2403.16132
| null | null |
http://arxiv.org/pdf/2403.16132v1
|
2024-03-24T13:03:27Z
|
2024-03-24T13:03:27Z
|
Runtime Monitoring and Fault Detection for Neural Network-Controlled
Systems
|
There is an emerging trend in applying deep learning methods to control complex nonlinear systems. This paper considers enhancing the runtime safety of nonlinear systems controlled by neural networks in the presence of disturbance and measurement noise. A robustly stable interval observer is designed to generate sound and precise lower and upper bounds for the neural network, nonlinear function, and system state. The obtained interval is utilised to monitor the real-time system safety and detect faults in the system outputs or actuators. An adaptive cruise control vehicular system is simulated to demonstrate effectiveness of the proposed design.
|
[
"['Jianglin Lan' 'Siyuan Zhan' 'Ron Patton' 'Xianxian Zhao']"
] |
null | null |
2403.16133
| null | null |
http://arxiv.org/pdf/2403.16133v1
|
2024-03-24T13:03:35Z
|
2024-03-24T13:03:35Z
|
SSHPool: The Separated Subgraph-based Hierarchical Pooling
|
In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. To this end, we commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs. We individually employ a local graph convolution units as the local structure to further compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. Since these subgraphs are separated by different clusters and the structural information cannot be propagated between them, the local convolution operation can significantly avoid the over-smoothing problem arising in most existing Graph Neural Networks (GNNs). By hierarchically performing the proposed procedures on the resulting coarsened graph, the proposed SSHPool can effectively extract the hierarchical global feature of the original graph structure, encapsulating rich intrinsic structural characteristics. Furthermore, we develop an end-to-end GNN framework associated with the proposed SSHPool module for graph classification. Experimental results demonstrate the superior performance of the proposed model on real-world datasets, significantly outperforming state-of-the-art GNN methods in terms of the classification accuracies.
|
[
"['Zhuo Xu' 'Lixin Cui' 'Yue Wang' 'Hangyuan Du' 'Lu Bai'\n 'Edwin R. Hancock']"
] |
null | null |
2403.16135
| null | null |
http://arxiv.org/pdf/2403.16135v1
|
2024-03-24T13:06:05Z
|
2024-03-24T13:06:05Z
|
Complementary Recommendation in E-commerce: Definition, Approaches, and
Future Directions
|
In recent years, complementary recommendation has received extensive attention in the e-commerce domain. In this paper, we comprehensively summarize and compare 34 representative studies conducted between 2009 and 2024. Firstly, we compare the data and methods used for modeling complementary relationships between products, including simple complementarity and more complex scenarios such as asymmetric complementarity, the coexistence of substitution and complementarity relationships between products, and varying degrees of complementarity between different pairs of products. Next, we classify and compare the models based on the research problems of complementary recommendation, such as diversity, personalization, and cold-start. Furthermore, we provide a comparative analysis of experimental results from different studies conducted on the same dataset, which helps identify the strengths and weaknesses of the research. Compared to previous surveys, this paper provides a more updated and comprehensive summary of the research, discusses future research directions, and contributes to the advancement of this field.
|
[
"['Linyue Li' 'Zhijuan Du']"
] |
null | null |
2403.16137
| null | null |
http://arxiv.org/pdf/2403.16137v1
|
2024-03-24T13:10:09Z
|
2024-03-24T13:10:09Z
|
A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A
Knowledge-Based Perspective
|
Graph self-supervised learning is now a go-to method for pre-training graph foundation models, including graph neural networks, graph transformers, and more recent large language model (LLM)-based graph models. There is a wide variety of knowledge patterns embedded in the structure and properties of graphs which may be used for pre-training, but we lack a systematic overview of self-supervised pre-training tasks from the perspective of graph knowledge. In this paper, we comprehensively survey and analyze the pre-training tasks of graph foundation models from a knowledge-based perspective, consisting of microscopic (nodes, links, etc) and macroscopic knowledge (clusters, global structure, etc). It covers a total of 9 knowledge categories and 25 pre-training tasks, as well as various downstream task adaptation strategies. Furthermore, an extensive list of the related papers with detailed metadata is provided at https://github.com/Newiz430/Pretext.
|
[
"['Ziwen Zhao' 'Yuhua Li' 'Yixiong Zou' 'Ruixuan Li' 'Rui Zhang']"
] |
null | null |
2403.16143
| null | null |
http://arxiv.org/pdf/2403.16143v1
|
2024-03-24T13:31:31Z
|
2024-03-24T13:31:31Z
|
CFAT: Unleashing TriangularWindows for Image Super-resolution
|
Transformer-based models have revolutionized the field of image super-resolution (SR) by harnessing their inherent ability to capture complex contextual features. The overlapping rectangular shifted window technique used in transformer architecture nowadays is a common practice in super-resolution models to improve the quality and robustness of image upscaling. However, it suffers from distortion at the boundaries and has limited unique shifting modes. To overcome these weaknesses, we propose a non-overlapping triangular window technique that synchronously works with the rectangular one to mitigate boundary-level distortion and allows the model to access more unique sifting modes. In this paper, we propose a Composite Fusion Attention Transformer (CFAT) that incorporates triangular-rectangular window-based local attention with a channel-based global attention technique in image super-resolution. As a result, CFAT enables attention mechanisms to be activated on more image pixels and captures long-range, multi-scale features to improve SR performance. The extensive experimental results and ablation study demonstrate the effectiveness of CFAT in the SR domain. Our proposed model shows a significant 0.7 dB performance improvement over other state-of-the-art SR architectures.
|
[
"['Abhisek Ray' 'Gaurav Kumar' 'Maheshkumar H. Kolekar']"
] |
null | null |
2403.16144
| null | null |
http://arxiv.org/pdf/2403.16144v1
|
2024-03-24T13:32:42Z
|
2024-03-24T13:32:42Z
|
Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural
Network Approach
|
Neural networks in fluid mechanics offer an efficient approach for exploring complex flows, including multiphase and free surface flows. The recurrent neural network, particularly the Long Short-Term Memory (LSTM) model, proves attractive for learning mappings from transient inputs to dynamic outputs. This study applies LSTM to predict transient and static outputs for fluid flows under surface tension effects. Specifically, we explore two distinct droplet dynamic scenarios: droplets with diverse initial shapes impacting with solid surfaces, as well as the coalescence of two droplets following collision. Using only dimensionless numbers and geometric time series data from numerical simulations, LSTM predicts the energy budget. The marker-and-cell front-tracking methodology combined with a marker-and-cell finite-difference strategy is adopted for simulating the droplet dynamics. Using a recurrent neural network (RNN) architecture fed with time series data derived from geometrical parameters, as for example droplet diameter variation, our study shows the accuracy of our approach in predicting energy budgets, as for instance the kinetic, dissipation, and surface energy trends, across a range of Reynolds and Weber numbers in droplet dynamic problems. Finally, a two-phase sequential neural network using only geometric data, which is readily available in experimental settings, is employed to predict the energies and then use them to estimate static parameters, such as the Reynolds and Weber numbers. While our methodology has been primarily validated with simulation data, its adaptability to experimental datasets is a promising avenue for future exploration. We hope that our strategy can be useful for diverse applications, spanning from inkjet printing to combustion engines, where the prediction of energy budgets or dissipation energies is crucial.
|
[
"['Diego A. de Aguiar' 'Hugo L. França' 'Cassio M. Oishi']"
] |
null | null |
2403.16149
| null | null |
http://arxiv.org/pdf/2403.16149v2
|
2024-07-15T09:05:13Z
|
2024-03-24T13:43:43Z
|
A Survey on Consumer IoT Traffic: Security and Privacy
|
Although CIoT has improved the convenience of daily activities, it also introduces new security and privacy concerns. Network traffic analysis, a common technique employed by the security community, has been extensively utilized to investigate security and privacy concerns, and it has also been applied to CIoT. However, compared to network traffic analysis in other fields such as mobile apps and websites, CIoT presents special new characteristics, which may introduce new challenges and research opportunities. In this study, we reviewed 310 publications on traffic analysis within the CIoT security and privacy domain, covering the period from January 2018 to December 2023. Initially, we summarized the CIoT traffic analysis process, highlighting the newly identified characteristics of CIoT. Subsequently, we classified existing research according to its application objectives: device fingerprinting, user activity inference, malicious traffic detection, and measurement. Lastly, we explore emerging challenges and potential future research avenues.
|
[
"['Yan Jia' 'Yuxin Song' 'Zihou Liu' 'Qingyin Tan' 'Yang Song' 'Yu Zhang'\n 'Zheli Liu']"
] |
null | null |
2403.16153
| null | null |
http://arxiv.org/pdf/2403.16153v1
|
2024-03-24T13:44:57Z
|
2024-03-24T13:44:57Z
|
One Masked Model is All You Need for Sensor Fault Detection, Isolation
and Accommodation
|
Accurate and reliable sensor measurements are critical for ensuring the safety and longevity of complex engineering systems such as wind turbines. In this paper, we propose a novel framework for sensor fault detection, isolation, and accommodation (FDIA) using masked models and self-supervised learning. Our proposed approach is a general time series modeling approach that can be applied to any neural network (NN) model capable of sequence modeling, and captures the complex spatio-temporal relationships among different sensors. During training, the proposed masked approach creates a random mask, which acts like a fault, for one or more sensors, making the training and inference task unified: finding the faulty sensors and correcting them. We validate our proposed technique on both a public dataset and a real-world dataset from GE offshore wind turbines, and demonstrate its effectiveness in detecting, diagnosing and correcting sensor faults. The masked model not only simplifies the overall FDIA pipeline, but also outperforms existing approaches. Our proposed technique has the potential to significantly improve the accuracy and reliability of sensor measurements in complex engineering systems in real-time, and could be applied to other types of sensors and engineering systems in the future. We believe that our proposed framework can contribute to the development of more efficient and effective FDIA techniques for a wide range of applications.
|
[
"['Yiwei Fu' 'Weizhong Yan']"
] |
null | null |
2403.16163
| null | null |
http://arxiv.org/pdf/2403.16163v1
|
2024-03-24T14:08:24Z
|
2024-03-24T14:08:24Z
|
An Analytic Solution to Covariance Propagation in Neural Networks
|
Uncertainty quantification of neural networks is critical to measuring the reliability and robustness of deep learning systems. However, this often involves costly or inaccurate sampling methods and approximations. This paper presents a sample-free moment propagation technique that propagates mean vectors and covariance matrices across a network to accurately characterize the input-output distributions of neural networks. A key enabler of our technique is an analytic solution for the covariance of random variables passed through nonlinear activation functions, such as Heaviside, ReLU, and GELU. The wide applicability and merits of the proposed technique are shown in experiments analyzing the input-output distributions of trained neural networks and training Bayesian neural networks.
|
[
"['Oren Wright' 'Yorie Nakahira' 'José M. F. Moura']"
] |
null | null |
2403.16176
| null | null |
http://arxiv.org/pdf/2403.16176v1
|
2024-03-24T14:35:44Z
|
2024-03-24T14:35:44Z
|
Subspace Defense: Discarding Adversarial Perturbations by Learning a
Subspace for Clean Signals
|
Deep neural networks (DNNs) are notoriously vulnerable to adversarial attacks that place carefully crafted perturbations on normal examples to fool DNNs. To better understand such attacks, a characterization of the features carried by adversarial examples is needed. In this paper, we tackle this challenge by inspecting the subspaces of sample features through spectral analysis. We first empirically show that the features of either clean signals or adversarial perturbations are redundant and span in low-dimensional linear subspaces respectively with minimal overlap, and the classical low-dimensional subspace projection can suppress perturbation features out of the subspace of clean signals. This makes it possible for DNNs to learn a subspace where only features of clean signals exist while those of perturbations are discarded, which can facilitate the distinction of adversarial examples. To prevent the residual perturbations that is inevitable in subspace learning, we propose an independence criterion to disentangle clean signals from perturbations. Experimental results show that the proposed strategy enables the model to inherently suppress adversaries, which not only boosts model robustness but also motivates new directions of effective adversarial defense.
|
[
"['Rui Zheng' 'Yuhao Zhou' 'Zhiheng Xi' 'Tao Gui' 'Qi Zhang'\n 'Xuanjing Huang']"
] |
null | null |
2403.16190
| null | null |
http://arxiv.org/abs/2403.16190v1
|
2024-03-24T15:14:44Z
|
2024-03-24T15:14:44Z
|
Logic-based Explanations for Linear Support Vector Classifiers with
Reject Option
|
Support Vector Classifier (SVC) is a well-known Machine Learning (ML) model for linear classification problems. It can be used in conjunction with a reject option strategy to reject instances that are hard to correctly classify and delegate them to a specialist. This further increases the confidence of the model. Given this, obtaining an explanation of the cause of rejection is important to not blindly trust the obtained results. While most of the related work has developed means to give such explanations for machine learning models, to the best of our knowledge none have done so for when reject option is present. We propose a logic-based approach with formal guarantees on the correctness and minimality of explanations for linear SVCs with reject option. We evaluate our approach by comparing it to Anchors, which is a heuristic algorithm for generating explanations. Obtained results show that our proposed method gives shorter explanations with reduced time cost.
|
[
"['Francisco Mateus Rocha Filho' 'Thiago Alves Rocha'\n 'Reginaldo Pereira Fernandes Ribeiro' 'Ajalmar Rêgo da Rocha Neto']"
] |
null | null |
2403.16201
| null | null |
http://arxiv.org/pdf/2403.16201v1
|
2024-03-24T15:48:29Z
|
2024-03-24T15:48:29Z
|
From Discrete to Continuous: Deep Fair Clustering With Transferable
Representations
|
We consider the problem of deep fair clustering, which partitions data into clusters via the representations extracted by deep neural networks while hiding sensitive data attributes. To achieve fairness, existing methods present a variety of fairness-related objective functions based on the group fairness criterion. However, these works typically assume that the sensitive attributes are discrete and do not work for continuous sensitive variables, such as the proportion of the female population in an area. Besides, the potential of the representations learned from clustering tasks to improve performance on other tasks is ignored by existing works. In light of these limitations, we propose a flexible deep fair clustering method that can handle discrete and continuous sensitive attributes simultaneously. Specifically, we design an information bottleneck style objective function to learn fair and clustering-friendly representations. Furthermore, we explore for the first time the transferability of the extracted representations to other downstream tasks. Unlike existing works, we impose fairness at the representation level, which could guarantee fairness for the transferred task regardless of clustering results. To verify the effectiveness of the proposed method, we perform extensive experiments on datasets with discrete and continuous sensitive attributes, demonstrating the advantage of our method in comparison with state-of-the-art methods.
|
[
"['Xiang Zhang']"
] |
null | null |
2403.16208
| null | null |
http://arxiv.org/pdf/2403.16208v1
|
2024-03-24T16:05:57Z
|
2024-03-24T16:05:57Z
|
Convergence analysis of OT-Flow for sample generation
|
Deep generative models aim to learn the underlying distribution of data and generate new ones. Despite the diversity of generative models and their high-quality generation performance in practice, most of them lack rigorous theoretical convergence proofs. In this work, we aim to establish some convergence results for OT-Flow, one of the deep generative models. First, by reformulating the framework of OT-Flow model, we establish the $Gamma$-convergence of the formulation of OT-flow to the corresponding optimal transport (OT) problem as the regularization term parameter $alpha$ goes to infinity. Second, since the loss function will be approximated by Monte Carlo method in training, we established the convergence between the discrete loss function and the continuous one when the sample number $N$ goes to infinity as well. Meanwhile, the approximation capability of the neural network provides an upper bound for the discrete loss function of the minimizers. The proofs in both aspects provide convincing assurances for OT-Flow.
|
[
"['Yang Jing' 'Lei Li']"
] |
null | null |
2403.16212
| null | null |
http://arxiv.org/pdf/2403.16212v1
|
2024-03-24T16:11:27Z
|
2024-03-24T16:11:27Z
|
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI
Classification in Alzheimer Diagnosis
|
Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD). With advancements in deep learning, particularly in Convolutional Neural Networks (CNNs) and the Xception network architecture, we are now able to analyze and classify vast amounts of MRI data with unprecedented accuracy. The progress of this technology not only enhances our understanding of brain structural changes but also opens up new avenues for monitoring disease progression through non-invasive means and potentially allows for precise diagnosis in the early stages of the disease. This study aims to classify MRI images using deep learning models to identify different stages of Alzheimer Disease through a series of innovative data processing and model construction steps. Our experimental results show that the deep learning framework based on the Xception model achieved a 99.6% accuracy rate in the multi-class MRI image classification task, demonstrating its potential application value in assistive diagnosis. Future research will focus on expanding the dataset, improving model interpretability, and clinical validation to further promote the application of deep learning technology in the medical field, with the hope of bringing earlier diagnosis and more personalized treatment plans to Alzheimer Disease patients.
|
[
"['Shaojie Li' 'Haichen Qu' 'Xinqi Dong' 'Bo Dang' 'Hengyi Zang'\n 'Yulu Gong']"
] |
null | null |
2403.16215
| null | null |
http://arxiv.org/pdf/2403.16215v1
|
2024-03-24T16:16:41Z
|
2024-03-24T16:16:41Z
|
Systematic construction of continuous-time neural networks for linear
dynamical systems
|
Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation through a high-dimensional hyper-parameter space. In this paper, we discuss a systematic approach to constructing neural architectures for modeling a subclass of dynamical systems, namely, Linear Time-Invariant (LTI) systems. We use a variant of continuous-time neural networks in which the output of each neuron evolves continuously as a solution of a first-order or second-order Ordinary Differential Equation (ODE). Instead of deriving the network architecture and parameters from data, we propose a gradient-free algorithm to compute sparse architecture and network parameters directly from the given LTI system, leveraging its properties. We bring forth a novel neural architecture paradigm featuring horizontal hidden layers and provide insights into why employing conventional neural architectures with vertical hidden layers may not be favorable. We also provide an upper bound on the numerical errors of our neural networks. Finally, we demonstrate the high accuracy of our constructed networks on three numerical examples.
|
[
"['Chinmay Datar' 'Adwait Datar' 'Felix Dietrich' 'Wil Schilders']"
] |
null | null |
2403.16218
| null | null |
http://arxiv.org/pdf/2403.16218v1
|
2024-03-24T16:18:27Z
|
2024-03-24T16:18:27Z
|
CoverUp: Coverage-Guided LLM-Based Test Generation
|
This paper presents CoverUp, a novel system that drives the generation of high-coverage Python regression tests via a combination of coverage analysis and large-language models (LLMs). CoverUp iteratively improves coverage, interleaving coverage analysis with dialogs with the LLM to focus its attention on as yet uncovered lines and branches. The resulting test suites significantly improve coverage over the current state of the art: compared to CodaMosa, a hybrid LLM / search-based software testing system, CoverUp substantially improves coverage across the board. On a per-module basis, CoverUp achieves median line coverage of 81% (vs. 62%), branch coverage of 53% (vs. 35%) and line+branch coverage of 78% (vs. 55%). We show that CoverUp's iterative, coverage-guided approach is crucial to its effectiveness, contributing to nearly half of its successes.
|
[
"['Juan Altmayer Pizzorno' 'Emery D. Berger']"
] |
null | null |
2403.16233
| null | null |
http://arxiv.org/pdf/2403.16233v1
|
2024-03-24T16:49:55Z
|
2024-03-24T16:49:55Z
|
An early warning indicator trained on stochastic disease-spreading
models with different noises
|
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modeling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise, and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreak by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.
|
[
"['Amit K. Chakraborty' 'Shan Gao' 'Reza Miry' 'Pouria Ramazi'\n 'Russell Greiner' 'Mark A. Lewis' 'Hao Wang']"
] |
null | null |
2403.16244
| null | null |
http://arxiv.org/pdf/2403.16244v1
|
2024-03-24T17:21:32Z
|
2024-03-24T17:21:32Z
|
On the Equivalency, Substitutability, and Flexibility of Synthetic Data
|
We study, from an empirical standpoint, the efficacy of synthetic data in real-world scenarios. Leveraging synthetic data for training perception models has become a key strategy embraced by the community due to its efficiency, scalability, perfect annotations, and low costs. Despite proven advantages, few studies put their stress on how to efficiently generate synthetic datasets to solve real-world problems and to what extent synthetic data can reduce the effort for real-world data collection. To answer the questions, we systematically investigate several interesting properties of synthetic data -- the equivalency of synthetic data to real-world data, the substitutability of synthetic data for real data, and the flexibility of synthetic data generators to close up domain gaps. Leveraging the M3Act synthetic data generator, we conduct experiments on DanceTrack and MOT17. Our results suggest that synthetic data not only enhances model performance but also demonstrates substitutability for real data, with 60% to 80% replacement without performance loss. In addition, our study of the impact of synthetic data distributions on downstream performance reveals the importance of flexible data generators in narrowing domain gaps for improved model adaptability.
|
[
"['Che-Jui Chang' 'Danrui Li' 'Seonghyeon Moon' 'Mubbasir Kapadia']"
] |
null | null |
2403.16246
| null | null |
http://arxiv.org/pdf/2403.16246v1
|
2024-03-24T17:33:22Z
|
2024-03-24T17:33:22Z
|
Partially Blinded Unlearning: Class Unlearning for Deep Networks a
Bayesian Perspective
|
In order to adhere to regulatory standards governing individual data privacy and safety, machine learning models must systematically eliminate information derived from specific subsets of a user's training data that can no longer be utilized. The emerging discipline of Machine Unlearning has arisen as a pivotal area of research, facilitating the process of selectively discarding information designated to specific sets or classes of data from a pre-trained model, thereby eliminating the necessity for extensive retraining from scratch. The principal aim of this study is to formulate a methodology tailored for the purposeful elimination of information linked to a specific class of data from a pre-trained classification network. This intentional removal is crafted to degrade the model's performance specifically concerning the unlearned data class while concurrently minimizing any detrimental impacts on the model's performance in other classes. To achieve this goal, we frame the class unlearning problem from a Bayesian perspective, which yields a loss function that minimizes the log-likelihood associated with the unlearned data with a stability regularization in parameter space. This stability regularization incorporates Mohalanobis distance with respect to the Fisher Information matrix and $l_2$ distance from the pre-trained model parameters. Our novel approach, termed textbf{Partially-Blinded Unlearning (PBU)}, surpasses existing state-of-the-art class unlearning methods, demonstrating superior effectiveness. Notably, PBU achieves this efficacy without requiring awareness of the entire training dataset but only to the unlearned data points, marking a distinctive feature of its performance.
|
[
"['Subhodip Panda' 'Shashwat Sourav' 'Prathosh A. P']"
] |
null | null |
2403.16247
| null | null |
http://arxiv.org/pdf/2403.16247v1
|
2024-03-24T17:39:36Z
|
2024-03-24T17:39:36Z
|
Improving Sequence-to-Sequence Models for Abstractive Text Summarization
Using Meta Heuristic Approaches
|
As human society transitions into the information age, reduction in our attention span is a contingency, and people who spend time reading lengthy news articles are decreasing rapidly and the need for succinct information is higher than ever before. Therefore, it is essential to provide a quick overview of important news by concisely summarizing the top news article and the most intuitive headline. When humans try to make summaries, they extract the essential information from the source and add useful phrases and grammatical annotations from the original extract. Humans have a unique ability to create abstractions. However, automatic summarization is a complicated problem to solve. The use of sequence-to-sequence (seq2seq) models for neural abstractive text summarization has been ascending as far as prevalence. Numerous innovative strategies have been proposed to develop the current seq2seq models further, permitting them to handle different issues like saliency, familiarity, and human lucidness and create excellent synopses. In this article, we aimed toward enhancing the present architectures and models for abstractive text summarization. The modifications have been aimed at fine-tuning hyper-parameters, attempting specific encoder-decoder combinations. We examined many experiments on an extensively used CNN/DailyMail dataset to check the effectiveness of various models.
|
[
"['Aditya Saxena' 'Ashutosh Ranjan']"
] |
null | null |
2403.16258
| null | null |
http://arxiv.org/pdf/2403.16258v1
|
2024-03-24T18:33:16Z
|
2024-03-24T18:33:16Z
|
Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated
Synthesis
|
While replacing Gaussian decoders with a conditional diffusion model enhances the perceptual quality of reconstructions in neural image compression, their lack of inductive bias for image data restricts their ability to achieve state-of-the-art perceptual levels. To address this limitation, we adopt a non-isotropic diffusion model at the decoder side. This model imposes an inductive bias aimed at distinguishing between frequency contents, thereby facilitating the generation of high-quality images. Moreover, our framework is equipped with a novel entropy model that accurately models the probability distribution of latent representation by exploiting spatio-channel correlations in latent space, while accelerating the entropy decoding step. This channel-wise entropy model leverages both local and global spatial contexts within each channel chunk. The global spatial context is built upon the Transformer, which is specifically designed for image compression tasks. The designed Transformer employs a Laplacian-shaped positional encoding, the learnable parameters of which are adaptively adjusted for each channel cluster. Our experiments demonstrate that our proposed framework yields better perceptual quality compared to cutting-edge generative-based codecs, and the proposed entropy model contributes to notable bitrate savings.
|
[
"['Atefeh Khoshkhahtinat' 'Ali Zafari' 'Piyush M. Mehta'\n 'Nasser M. Nasrabadi']"
] |
null | null |
2403.16260
| null | null |
http://arxiv.org/pdf/2403.16260v1
|
2024-03-24T18:43:04Z
|
2024-03-24T18:43:04Z
|
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
|
Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequently, the adoption of model ensembles has emerged as a prominent strategy to augment this feature representation field, capitalizing on anticipated model diversity. However, our introduction of novel qualitative and quantitative model ensemble evaluation methods, specifically Loss Basin/Barrier Visualization and the Self-Coupling Index, reveals a critical drawback in existing ensemble methods. We find that these methods incorporate weights that are affine-transformable, exhibiting limited variability and thus failing to achieve the desired diversity in feature representation. To address this limitation, we elevate the dimensions of traditional model ensembles, incorporating various factors such as different weight initializations, data holdout, etc., into distinct supervision tasks. This innovative approach, termed Multi-Comprehension (MC) Ensemble, leverages diverse training tasks to generate distinct comprehensions of the data and labels, thereby extending the feature representation field. Our experimental results demonstrate the superior performance of the MC Ensemble strategy in OOD detection compared to both the naive Deep Ensemble method and a standalone model of comparable size. This underscores the effectiveness of our proposed approach in enhancing the model's capability to detect instances outside its training distribution.
|
[
"['Chenhui Xu' 'Fuxun Yu' 'Zirui Xu' 'Nathan Inkawhich' 'Xiang Chen']"
] |
null | null |
2403.16282
| null | null |
http://arxiv.org/pdf/2403.16282v1
|
2024-03-24T20:08:16Z
|
2024-03-24T20:08:16Z
|
The Evolution of Football Betting- A Machine Learning Approach to Match
Outcome Forecasting and Bookmaker Odds Estimation
|
This paper explores the significant history of professional football and the betting industry, tracing its evolution from clandestine beginnings to a lucrative multi-million-pound enterprise. Initiated by the legalization of gambling in 1960 and complemented by advancements in football data gathering pioneered by Thorold Charles Reep, the symbiotic relationship between these sectors has propelled rapid growth and innovation. Over the past six decades, both industries have undergone radical transformations, with data collection methods evolving from rudimentary notetaking to sophisticated technologies such as high-definition cameras and Artificial Intelligence (AI)-driven analytics. Therefore, the primary aim of this study is to utilize Machine Learning (ML) algorithms to forecast premier league football match outcomes. By analyzing historical data and investigating the significance of various features, the study seeks to identify the most effective predictive models and discern key factors influencing match results. Additionally, the study aims to utilize these forecasting to inform the establishment of bookmaker odds, providing insights into the impact of different variables on match outcomes. By highlighting the potential for informed decision-making in sports forecasting and betting, this study opens up new avenues for research and practical applications in the domain of sports analytics.
|
[
"['Purnachandra Mandadapu']"
] |
null | null |
2403.16293
| null | null |
http://arxiv.org/abs/2403.16293v1
|
2024-03-24T20:56:16Z
|
2024-03-24T20:56:16Z
|
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
|
In the field of high-performance computing (HPC), there has been recent exploration into the use of deep reinforcement learning for cluster scheduling (DRL scheduling), which has demonstrated promising outcomes. However, a significant challenge arises from the lack of interpretability in deep neural networks (DNN), rendering them as black-box models to system managers. This lack of model interpretability hinders the practical deployment of DRL scheduling. In this work, we present a framework called IRL (Interpretable Reinforcement Learning) to address the issue of interpretability of DRL scheduling. The core idea is to interpret DNN (i.e., the DRL policy) as a decision tree by utilizing imitation learning. Unlike DNN, decision tree models are non-parametric and easily comprehensible to humans. To extract an effective and efficient decision tree, IRL incorporates the Dataset Aggregation (DAgger) algorithm and introduces the notion of critical state to prune the derived decision tree. Through trace-based experiments, we demonstrate that IRL is capable of converting a black-box DNN policy into an interpretable rulebased decision tree while maintaining comparable scheduling performance. Additionally, IRL can contribute to the setting of rewards in DRL scheduling.
|
[
"['Boyang Li' 'Zhiling Lan' 'Michael E. Papka']"
] |
null | null |
2403.16317
| null | null |
http://arxiv.org/pdf/2403.16317v1
|
2024-03-24T22:42:40Z
|
2024-03-24T22:42:40Z
|
Optimization on a Finer Scale: Bounded Local Subgradient Variation
Perspective
|
We initiate the study of nonsmooth optimization problems under bounded local subgradient variation, which postulates bounded difference between (sub)gradients in small local regions around points, in either average or maximum sense. The resulting class of objective functions encapsulates the classes of objective functions traditionally studied in optimization, which are defined based on either Lipschitz continuity of the objective or H"{o}lder/Lipschitz continuity of its gradient. Further, the defined class contains functions that are neither Lipschitz continuous nor have a H"{o}lder continuous gradient. When restricted to the traditional classes of optimization problems, the parameters defining the studied classes lead to more fine-grained complexity bounds, recovering traditional oracle complexity bounds in the worst case but generally leading to lower oracle complexity for functions that are not ``worst case.'' Some highlights of our results are that: (i) it is possible to obtain complexity results for both convex and nonconvex problems with the (local or global) Lipschitz constant being replaced by a constant of local subgradient variation and (ii) mean width of the subdifferential set around the optima plays a role in the complexity of nonsmooth optimization, particularly in parallel settings. A consequence of (ii) is that for any error parameter $epsilon > 0$, parallel oracle complexity of nonsmooth Lipschitz convex optimization is lower than its sequential oracle complexity by a factor $tilde{Omega}big(frac{1}{epsilon}big)$ whenever the objective function is piecewise linear with polynomially many pieces in the input size. This is particularly surprising as existing parallel complexity lower bounds are based on such classes of functions. The seeming contradiction is resolved by considering the region in which the algorithm is allowed to query the objective.
|
[
"['Jelena Diakonikolas' 'Cristóbal Guzmán']"
] |
null | null |
2403.16327
| null | null |
http://arxiv.org/pdf/2403.16327v1
|
2024-03-24T23:22:02Z
|
2024-03-24T23:22:02Z
|
Artificial Neural Microcircuits as Building Blocks: Concept and
Challenges
|
Artificial Neural Networks (ANNs) are one of the most widely employed forms of bio-inspired computation. However the current trend is for ANNs to be structurally homogeneous. Furthermore, this structural homogeneity requires the application of complex training and learning tools that produce application specific ANNs, susceptible to pitfalls such as overfitting. In this paper, an new approach is explored, inspired by the role played in biology by Neural Microcircuits, the so called ``fundamental processing elements'' of organic nervous systems. How large neural networks, particularly Spiking Neural Networks (SNNs) can be assembled using Artificial Neural Microcircuits (ANMs), intended as off-the-shelf components, is articulated; the results of initial work to produce a catalogue of such Microcircuits though the use of Novelty Search is shown; followed by efforts to expand upon this initial work, including a discussion of challenges uncovered during these efforts and explorations of methods by which they might be overcome.
|
[
"['Andrew Walter' 'Shimeng Wu' 'Andy M. Tyrrell' 'Liam McDaid'\n 'Malachy McElholm' 'Nidhin Thandassery Sumithran' 'Jim Harkin'\n 'Martin A. Trefzer']"
] |
null | null |
2403.16331
| null | null |
http://arxiv.org/pdf/2403.16331v1
|
2024-03-24T23:50:15Z
|
2024-03-24T23:50:15Z
|
Modeling Analog Dynamic Range Compressors using Deep Learning and
State-space Models
|
We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applications, the design process is challenging because the compressors operate nonlinearly over long time scales. Our approach is based on the structured state space sequence model (S4), as implementing the state-space model (SSM) has proven to be efficient at learning long-range dependencies and is promising for modeling dynamic range compressors. We present in this paper a deep learning model with S4 layers to model the Teletronix LA-2A analog dynamic range compressor. The model is causal, executes efficiently in real time, and achieves roughly the same quality as previous deep-learning models but with fewer parameters.
|
[
"['Hanzhi Yin' 'Gang Cheng' 'Christian J. Steinmetz' 'Ruibin Yuan'\n 'Richard M. Stern' 'Roger B. Dannenberg']"
] |
null | null |
2403.16334
| null | null |
http://arxiv.org/pdf/2403.16334v1
|
2024-03-25T00:15:34Z
|
2024-03-25T00:15:34Z
|
Graphs Generalization under Distribution Shifts
|
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution. To address this crucial issue, out-of-distribution (OOD) generalization, which aims to achieve satisfactory generalization performance when faced with unknown distribution shifts, has made a significant process. However, the OOD method for graph-structured data currently lacks clarity and remains relatively unexplored due to two primary challenges. Firstly, distribution shifts on graphs often occur simultaneously on node attributes and graph topology. Secondly, capturing invariant information amidst diverse distribution shifts proves to be a formidable challenge. To overcome these obstacles, in this paper, we introduce a novel framework, namely Graph Learning Invariant Domain genERation (GLIDER). The goal is to (1) diversify variations across domains by modeling the potential seen or unseen variations of attribute distribution and topological structure and (2) minimize the discrepancy of the variation in a representation space where the target is to predict semantic labels. Extensive experiment results indicate that our model outperforms baseline methods on node-level OOD generalization across domains in distribution shift on node features and topological structures simultaneously.
|
[
"['Qin Tian' 'Wenjun Wang' 'Chen Zhao' 'Minglai Shao' 'Wang Zhang'\n 'Dong Li']"
] |
null | null |
2403.16335
| null | null |
http://arxiv.org/pdf/2403.16335v2
|
2024-03-26T23:29:49Z
|
2024-03-25T00:17:43Z
|
MEDDAP: Medical Dataset Enhancement via Diversified Augmentation
Pipeline
|
The effectiveness of Deep Neural Networks (DNNs) heavily relies on the abundance and accuracy of available training data. However, collecting and annotating data on a large scale is often both costly and time-intensive, particularly in medical cases where practitioners are already occupied with their duties. Moreover, ensuring that the model remains robust across various scenarios of image capture is crucial in medical domains, especially when dealing with ultrasound images that vary based on the settings of different devices and the manual operation of the transducer. To address this challenge, we introduce a novel pipeline called MEDDAP, which leverages Stable Diffusion (SD) models to augment existing small datasets by automatically generating new informative labeled samples. Pretrained checkpoints for SD are typically based on natural images, and training them for medical images requires significant GPU resources due to their heavy parameters. To overcome this challenge, we introduce USLoRA (Ultrasound Low-Rank Adaptation), a novel fine-tuning method tailored specifically for ultrasound applications. USLoRA allows for selective fine-tuning of weights within SD, requiring fewer than 0.1% of parameters compared to fully fine-tuning only the UNet portion of SD. To enhance dataset diversity, we incorporate different adjectives into the generation process prompts, thereby desensitizing the classifiers to intensity changes across different images. This approach is inspired by clinicians' decision-making processes regarding breast tumors, where tumor shape often plays a more crucial role than intensity. In conclusion, our pipeline not only outperforms classifiers trained on the original dataset but also demonstrates superior performance when encountering unseen datasets. The source code is available at https://github.com/yasamin-med/MEDDAP.
|
[
"['Yasamin Medghalchi' 'Niloufar Zakariaei' 'Arman Rahmim'\n 'Ilker Hacihaliloglu']"
] |
null | null |
2403.16336
| null | null |
http://arxiv.org/pdf/2403.16336v1
|
2024-03-25T00:21:34Z
|
2024-03-25T00:21:34Z
|
Predictive Inference in Multi-environment Scenarios
|
We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments. We investigate two types of coverage suitable for these problems, extending the jackknife and split-conformal methods to show how to obtain distribution-free coverage in such non-traditional, hierarchical data-generating scenarios. Our contributions also include extensions for settings with non-real-valued responses and a theory of consistency for predictive inference in these general problems. We demonstrate a novel resizing method to adapt to problem difficulty, which applies both to existing approaches for predictive inference with hierarchical data and the methods we develop; this reduces prediction set sizes using limited information from the test environment, a key to the methods' practical performance, which we evaluate through neurochemical sensing and species classification datasets.
|
[
"['John C. Duchi' 'Suyash Gupta' 'Kuanhao Jiang' 'Pragya Sur']"
] |
null | null |
2403.16347
| null | null |
http://arxiv.org/abs/2403.16347v1
|
2024-03-25T00:50:27Z
|
2024-03-25T00:50:27Z
|
ChatGPT Incorrectness Detection in Software Reviews
|
We conducted a survey of 135 software engineering (SE) practitioners to understand how they use Generative AI-based chatbots like ChatGPT for SE tasks. We find that they want to use ChatGPT for SE tasks like software library selection but often worry about the truthfulness of ChatGPT responses. We developed a suite of techniques and a tool called CID (ChatGPT Incorrectness Detector) to automatically test and detect the incorrectness in ChatGPT responses. CID is based on the iterative prompting to ChatGPT by asking it contextually similar but textually divergent questions (using an approach that utilizes metamorphic relationships in texts). The underlying principle in CID is that for a given question, a response that is different from other responses (across multiple incarnations of the question) is likely an incorrect response. In a benchmark study of library selection, we show that CID can detect incorrect responses from ChatGPT with an F1-score of 0.74 - 0.75.
|
[
"['Minaoar Hossain Tanzil' 'Junaed Younus Khan' 'Gias Uddin']"
] |
null | null |
2403.16354
| null | null |
http://arxiv.org/pdf/2403.16354v1
|
2024-03-25T01:12:57Z
|
2024-03-25T01:12:57Z
|
ChatDBG: An AI-Powered Debugging Assistant
|
This paper presents ChatDBG, the first AI-powered debugging assistant. ChatDBG integrates large language models (LLMs) to significantly enhance the capabilities and user-friendliness of conventional debuggers. ChatDBG lets programmers engage in a collaborative dialogue with the debugger, allowing them to pose complex questions about program state, perform root cause analysis for crashes or assertion failures, and explore open-ended queries like "why is x null?". To handle these queries, ChatDBG grants the LLM autonomy to take the wheel and drive debugging by issuing commands to navigate through stacks and inspect program state; it then reports its findings and yields back control to the programmer. Our ChatDBG prototype integrates with standard debuggers including LLDB, GDB, and WinDBG for native code and Pdb for Python. Our evaluation across a diverse set of code, including C/C++ code with known bugs and a suite of Python code including standalone scripts and Jupyter notebooks, demonstrates that ChatDBG can successfully analyze root causes, explain bugs, and generate accurate fixes for a wide range of real-world errors. For the Python programs, a single query led to an actionable bug fix 67% of the time; one additional follow-up query increased the success rate to 85%. ChatDBG has seen rapid uptake; it has already been downloaded nearly 30,000 times.
|
[
"['Kyla Levin' 'Nicolas van Kempen' 'Emery D. Berger' 'Stephen N. Freund']"
] |
null | null |
2403.16365
| null | null |
http://arxiv.org/pdf/2403.16365v1
|
2024-03-25T02:03:38Z
|
2024-03-25T02:03:38Z
|
Generating Potent Poisons and Backdoors from Scratch with Guided
Diffusion
|
Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection. As a result of this insecure curation pipeline, an adversary can poison or backdoor the resulting model by uploading malicious data to the internet and waiting for a victim to scrape and train on it. Existing approaches for creating poisons and backdoors start with randomly sampled clean data, called base samples, and then modify those samples to craft poisons. However, some base samples may be significantly more amenable to poisoning than others. As a result, we may be able to craft more potent poisons by carefully choosing the base samples. In this work, we use guided diffusion to synthesize base samples from scratch that lead to significantly more potent poisons and backdoors than previous state-of-the-art attacks. Our Guided Diffusion Poisoning (GDP) base samples can be combined with any downstream poisoning or backdoor attack to boost its effectiveness. Our implementation code is publicly available at: https://github.com/hsouri/GDP .
|
[
"['Hossein Souri' 'Arpit Bansal' 'Hamid Kazemi' 'Liam Fowl'\n 'Aniruddha Saha' 'Jonas Geiping' 'Andrew Gordon Wilson' 'Rama Chellappa'\n 'Tom Goldstein' 'Micah Goldblum']"
] |
null | null |
2403.16369
| null | null |
http://arxiv.org/pdf/2403.16369v3
|
2024-06-24T13:19:17Z
|
2024-03-25T02:17:54Z
|
Learning Action-based Representations Using Invariance
|
Robust reinforcement learning agents using high-dimensional observations must be able to identify relevant state features amidst many exogeneous distractors. A representation that captures controllability identifies these state elements by determining what affects agent control. While methods such as inverse dynamics and mutual information capture controllability for a limited number of timesteps, capturing long-horizon elements remains a challenging problem. Myopic controllability can capture the moment right before an agent crashes into a wall, but not the control-relevance of the wall while the agent is still some distance away. To address this we introduce action-bisimulation encoding, a method inspired by the bisimulation invariance pseudometric, that extends single-step controllability with a recursive invariance constraint. By doing this, action-bisimulation learns a multi-step controllability metric that smoothly discounts distant state features that are relevant for control. We demonstrate that action-bisimulation pretraining on reward-free, uniformly random data improves sample efficiency in several environments, including a photorealistic 3D simulation domain, Habitat. Additionally, we provide theoretical analysis and qualitative results demonstrating the information captured by action-bisimulation.
|
[
"['Max Rudolph' 'Caleb Chuck' 'Kevin Black' 'Misha Lvovsky' 'Scott Niekum'\n 'Amy Zhang']"
] |
null | null |
2403.16372
| null | null |
http://arxiv.org/pdf/2403.16372v1
|
2024-03-25T02:32:43Z
|
2024-03-25T02:32:43Z
|
SignSGD with Federated Voting
|
Distributed learning is commonly used for accelerating model training by harnessing the computational capabilities of multiple-edge devices. However, in practical applications, the communication delay emerges as a bottleneck due to the substantial information exchange required between workers and a central parameter server. SignSGD with majority voting (signSGD-MV) is an effective distributed learning algorithm that can significantly reduce communication costs by one-bit quantization. However, due to heterogeneous computational capabilities, it fails to converge when the mini-batch sizes differ among workers. To overcome this, we propose a novel signSGD optimizer with textit{federated voting} (signSGD-FV). The idea of federated voting is to exploit learnable weights to perform weighted majority voting. The server learns the weights assigned to the edge devices in an online fashion based on their computational capabilities. Subsequently, these weights are employed to decode the signs of the aggregated local gradients in such a way to minimize the sign decoding error probability. We provide a unified convergence rate analysis framework applicable to scenarios where the estimated weights are known to the parameter server either perfectly or imperfectly. We demonstrate that the proposed signSGD-FV algorithm has a theoretical convergence guarantee even when edge devices use heterogeneous mini-batch sizes. Experimental results show that signSGD-FV outperforms signSGD-MV, exhibiting a faster convergence rate, especially in heterogeneous mini-batch sizes.
|
[
"['Chanho Park' 'H. Vincent Poor' 'Namyoon Lee']"
] |
null | null |
2403.16374
| null | null |
http://arxiv.org/pdf/2403.16374v1
|
2024-03-25T02:38:34Z
|
2024-03-25T02:38:34Z
|
ProIn: Learning to Predict Trajectory Based on Progressive Interactions
for Autonomous Driving
|
Accurate motion prediction of pedestrians, cyclists, and other surrounding vehicles (all called agents) is very important for autonomous driving. Most existing works capture map information through an one-stage interaction with map by vector-based attention, to provide map constraints for social interaction and multi-modal differentiation. However, these methods have to encode all required map rules into the focal agent's feature, so as to retain all possible intentions' paths while at the meantime to adapt to potential social interaction. In this work, a progressive interaction network is proposed to enable the agent's feature to progressively focus on relevant maps, in order to better learn agents' feature representation capturing the relevant map constraints. The network progressively encode the complex influence of map constraints into the agent's feature through graph convolutions at the following three stages: after historical trajectory encoder, after social interaction, and after multi-modal differentiation. In addition, a weight allocation mechanism is proposed for multi-modal training, so that each mode can obtain learning opportunities from a single-mode ground truth. Experiments have validated the superiority of progressive interactions to the existing one-stage interaction, and demonstrate the effectiveness of each component. Encouraging results were obtained in the challenging benchmarks.
|
[
"['Yinke Dong' 'Haifeng Yuan' 'Hongkun Liu' 'Wei Jing' 'Fangzhen Li'\n 'Hongmin Liu' 'Bin Fan']"
] |
null | null |
2403.16377
| null | null |
http://arxiv.org/pdf/2403.16377v1
|
2024-03-25T02:47:29Z
|
2024-03-25T02:47:29Z
|
Real-time Adaptation for Condition Monitoring Signal Prediction using
Label-aware Neural Processes
|
Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer from a trade-off between representation power and agility in online settings. For instance, parametric methods that assume an underlying functional form for CM signals facilitate efficient online prediction updates. However, this simplification leads to vulnerability to model specifications and an inability to capture complex signals. On the other hand, approaches based on over-parameterized or non-parametric models can excel at explaining complex nonlinear signals, but real-time updates for such models pose a challenging task. In this paper, we propose a neural process-based approach that addresses this trade-off. It encodes available observations within a CM signal into a representation space and then reconstructs the signal's history and evolution for prediction. Once trained, the model can encode an arbitrary number of observations without requiring retraining, enabling on-the-spot real-time predictions along with quantified uncertainty and can be readily updated as more online data is gathered. Furthermore, our model is designed to incorporate qualitative information (i.e., labels) from individual units. This integration not only enhances individualized predictions for each unit but also enables joint inference for both signals and their associated labels. Numerical studies on both synthetic and real-world data in reliability engineering highlight the advantageous features of our model in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.
|
[
"['Seokhyun Chung' 'Raed Al Kontar']"
] |
null | null |
2403.16391
| null | null |
http://arxiv.org/pdf/2403.16391v1
|
2024-03-25T03:13:56Z
|
2024-03-25T03:13:56Z
|
Physics-informed RL for Maximal Safety Probability Estimation
|
Accurate risk quantification and reachability analysis are crucial for safe control and learning, but sampling from rare events, risky states, or long-term trajectories can be prohibitively costly. Motivated by this, we study how to estimate the long-term safety probability of maximally safe actions without sufficient coverage of samples from risky states and long-term trajectories. The use of maximal safety probability in control and learning is expected to avoid conservative behaviors due to over-approximation of risk. Here, we first show that long-term safety probability, which is multiplicative in time, can be converted into additive costs and be solved using standard reinforcement learning methods. We then derive this probability as solutions of partial differential equations (PDEs) and propose Physics-Informed Reinforcement Learning (PIRL) algorithm. The proposed method can learn using sparse rewards because the physics constraints help propagate risk information through neighbors. This suggests that, for the purpose of extracting more information for efficient learning, physics constraints can serve as an alternative to reward shaping. The proposed method can also estimate long-term risk using short-term samples and deduce the risk of unsampled states. This feature is in stark contrast with the unconstrained deep RL that demands sufficient data coverage. These merits of the proposed method are demonstrated in numerical simulation.
|
[
"['Hikaru Hoshino' 'Yorie Nakahira']"
] |
null | null |
2403.16393
| null | null |
http://arxiv.org/pdf/2403.16393v1
|
2024-03-25T03:17:27Z
|
2024-03-25T03:17:27Z
|
Concurrent Linguistic Error Detection (CLED) for Large Language Models
|
The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thus, efficient error detection for LLMs is an important issue. In many settings, the LLM is considered as a black box with no access to the internal nodes; this prevents the use of many error detection schemes that need access to the model's internal nodes. An interesting observation is that the output of LLMs in error-free operation should be valid and normal text. Therefore, when the text is not valid or differs significantly from normal text, it is likely that there is an error. Based on this observation we propose to perform Concurrent Linguistic Error Detection (CLED); this scheme extracts some linguistic features of the text generated by the LLM and feeds them to a concurrent classifier that detects errors. Since the proposed error detection mechanism only relies on the outputs of the model, then it can be used on LLMs in which there is no access to the internal nodes. The proposed CLED scheme has been evaluated on the T5 model when used for news summarization and on the OPUS-MT model when used for translation. In both cases, the same set of linguistic features has been used for error detection to illustrate the applicability of the proposed scheme beyond a specific case. The results show that CLED can detect most of the errors at a low overhead penalty. The use of the concurrent classifier also enables a trade-off between error detection effectiveness and its associated overhead, so providing flexibility to a designer.
|
[
"['Jinhua Zhu' 'Javier Conde' 'Zhen Gao' 'Pedro Reviriego' 'Shanshan Liu'\n 'Fabrizio Lombardi']"
] |
null | null |
2403.16398
| null | null |
http://arxiv.org/pdf/2403.16398v1
|
2024-03-25T03:26:01Z
|
2024-03-25T03:26:01Z
|
Rethinking the Representation in Federated Unsupervised Learning with
Non-IID Data
|
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However, the performance of existing FUSL methods suffers from insufficient representations, i.e., (1) representation collapse entanglement among local and global models, and (2) inconsistent representation spaces among local models. The former indicates that representation collapse in local model will subsequently impact the global model and other local models. The latter means that clients model data representation with inconsistent parameters due to the deficiency of supervision signals. In this work, we propose FedU2 which enhances generating uniform and unified representation in FUSL with non-IID data. Specifically, FedU2 consists of flexible uniform regularizer (FUR) and efficient unified aggregator (EUA). FUR in each client avoids representation collapse via dispersing samples uniformly, and EUA in server promotes unified representation by constraining consistent client model updating. To extensively validate the performance of FedU2, we conduct both cross-device and cross-silo evaluation experiments on two benchmark datasets, i.e., CIFAR10 and CIFAR100.
|
[
"['Xinting Liao' 'Weiming Liu' 'Chaochao Chen' 'Pengyang Zhou'\n 'Fengyuan Yu' 'Huabin Zhu' 'Binhui Yao' 'Tao Wang' 'Xiaolin Zheng'\n 'Yanchao Tan']"
] |
null | null |
2403.16405
| null | null |
http://arxiv.org/pdf/2403.16405v1
|
2024-03-25T03:44:36Z
|
2024-03-25T03:44:36Z
|
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low
Curvature Models
|
The integration of an ensemble of deep learning models has been extensively explored to enhance defense against adversarial attacks. The diversity among sub-models increases the attack cost required to deceive the majority of the ensemble, thereby improving the adversarial robustness. While existing approaches mainly center on increasing diversity in feature representations or dispersion of first-order gradients with respect to input, the limited correlation between these diversity metrics and adversarial robustness constrains the performance of ensemble adversarial defense. In this work, we aim to enhance ensemble diversity by reducing attack transferability. We identify second-order gradients, which depict the loss curvature, as a key factor in adversarial robustness. Computing the Hessian matrix involved in second-order gradients is computationally expensive. To address this, we approximate the Hessian-vector product using differential approximation. Given that low curvature provides better robustness, our ensemble model was designed to consider the influence of curvature among different sub-models. We introduce a novel regularizer to train multiple more-diverse low-curvature network models. Extensive experiments across various datasets demonstrate that our ensemble model exhibits superior robustness against a range of attacks, underscoring the effectiveness of our approach.
|
[
"['Kaikang Zhao' 'Xi Chen' 'Wei Huang' 'Liuxin Ding' 'Xianglong Kong'\n 'Fan Zhang']"
] |
null | null |
2403.16418
| null | null |
http://arxiv.org/abs/2403.16418v2
|
2024-04-29T13:00:21Z
|
2024-03-25T04:43:47Z
|
An Incremental MaxSAT-based Model to Learn Interpretable and Balanced
Classification Rules
|
The increasing advancements in the field of machine learning have led to the development of numerous applications that effectively address a wide range of problems with accurate predictions. However, in certain cases, accuracy alone may not be sufficient. Many real-world problems also demand explanations and interpretability behind the predictions. One of the most popular interpretable models that are classification rules. This work aims to propose an incremental model for learning interpretable and balanced rules based on MaxSAT, called IMLIB. This new model was based on two other approaches, one based on SAT and the other on MaxSAT. The one based on SAT limits the size of each generated rule, making it possible to balance them. We suggest that such a set of rules seem more natural to be understood compared to a mixture of large and small rules. The approach based on MaxSAT, called IMLI, presents a technique to increase performance that involves learning a set of rules by incrementally applying the model in a dataset. Finally, IMLIB and IMLI are compared using diverse databases. IMLIB obtained results comparable to IMLI in terms of accuracy, generating more balanced rules with smaller sizes.
|
[
"['Antônio Carlos Souza Ferreira Júnior' 'Thiago Alves Rocha']"
] |
null | null |
2403.16439
| null | null |
http://arxiv.org/pdf/2403.16439v1
|
2024-03-25T05:58:33Z
|
2024-03-25T05:58:33Z
|
Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
|
High-definition (HD) maps have played an integral role in the development of modern autonomous vehicle (AV) stacks, albeit with high associated labeling and maintenance costs. As a result, many recent works have proposed methods for estimating HD maps online from sensor data, enabling AVs to operate outside of previously-mapped regions. However, current online map estimation approaches are developed in isolation of their downstream tasks, complicating their integration in AV stacks. In particular, they do not produce uncertainty or confidence estimates. In this work, we extend multiple state-of-the-art online map estimation methods to additionally estimate uncertainty and show how this enables more tightly integrating online mapping with trajectory forecasting. In doing so, we find that incorporating uncertainty yields up to 50% faster training convergence and up to 15% better prediction performance on the real-world nuScenes driving dataset.
|
[
"['Xunjiang Gu' 'Guanyu Song' 'Igor Gilitschenski' 'Marco Pavone'\n 'Boris Ivanovic']"
] |
null | null |
2403.16442
| null | null |
http://arxiv.org/pdf/2403.16442v1
|
2024-03-25T06:05:50Z
|
2024-03-25T06:05:50Z
|
If CLIP Could Talk: Understanding Vision-Language Model Representations
Through Their Preferred Concept Descriptions
|
Recent works often assume that Vision-Language Model (VLM) representations are based on visual attributes like shape. However, it is unclear to what extent VLMs prioritize this information to represent concepts. We propose Extract and Explore (EX2), a novel approach to characterize important textual features for VLMs. EX2 uses reinforcement learning to align a large language model with VLM preferences and generates descriptions that incorporate the important features for the VLM. Then, we inspect the descriptions to identify the features that contribute to VLM representations. We find that spurious descriptions have a major role in VLM representations despite providing no helpful information, e.g., Click to enlarge photo of CONCEPT. More importantly, among informative descriptions, VLMs rely significantly on non-visual attributes like habitat to represent visual concepts. Also, our analysis reveals that different VLMs prioritize different attributes in their representations. Overall, we show that VLMs do not simply match images to scene descriptions and that non-visual or even spurious descriptions significantly influence their representations.
|
[
"['Reza Esfandiarpoor' 'Cristina Menghini' 'Stephen H. Bach']"
] |
null | null |
2403.16451
| null | null |
http://arxiv.org/pdf/2403.16451v4
|
2024-03-28T11:36:06Z
|
2024-03-25T06:30:54Z
|
DeepMachining: Online Prediction of Machining Errors of Lathe Machines
|
We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states. Then, we fine-tune the pretrained model to adapt to specific machining tasks. We demonstrate that DeepMachining achieves high prediction accuracy for multiple tasks that involve different workpieces and cutting tools. To the best of our knowledge, this work is one of the first factory experiments using pre-trained deep-learning models to predict machining errors of lathe machines.
|
[
"['Xiang-Li Lu' 'Hwai-Jung Hsu' 'Che-Wei Chou' 'H. T. Kung' 'Chen-Hsin Lee'\n 'Sheng-Mao Cheng']"
] |
null | null |
2403.16459
| null | null |
http://arxiv.org/pdf/2403.16459v2
|
2024-04-09T03:06:10Z
|
2024-03-25T06:42:02Z
|
On the rates of convergence for learning with convolutional neural
networks
|
We study approximation and learning capacities of convolutional neural networks (CNNs) with one-side zero-padding and multiple channels. Our first result proves a new approximation bound for CNNs with certain constraint on the weights. Our second result gives new analysis on the covering number of feed-forward neural networks with CNNs as special cases. The analysis carefully takes into account the size of the weights and hence gives better bounds than the existing literature in some situations. Using these two results, we are able to derive rates of convergence for estimators based on CNNs in many learning problems. In particular, we establish minimax optimal convergence rates of the least squares based on CNNs for learning smooth functions in the nonparametric regression setting. For binary classification, we derive convergence rates for CNN classifiers with hinge loss and logistic loss. It is also shown that the obtained rates for classification are minimax optimal in some common settings.
|
[
"['Yunfei Yang' 'Han Feng' 'Ding-Xuan Zhou']"
] |
null | null |
2403.16460
| null | null |
http://arxiv.org/pdf/2403.16460v2
|
2024-03-29T08:46:16Z
|
2024-03-25T06:43:28Z
|
FedAC: An Adaptive Clustered Federated Learning Framework for
Heterogeneous Data
|
Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for cluster-wise model training. However, current CFL methods struggle due to inadequate integration of global and intra-cluster knowledge and the absence of an efficient online model similarity metric, while treating the cluster count as a fixed hyperparameter limits flexibility and robustness. In this paper, we propose an adaptive CFL framework, named FedAC, which (1) efficiently integrates global knowledge into intra-cluster learning by decoupling neural networks and utilizing distinct aggregation methods for each submodule, significantly enhancing performance; (2) includes a costeffective online model similarity metric based on dimensionality reduction; (3) incorporates a cluster number fine-tuning module for improved adaptability and scalability in complex, heterogeneous environments. Extensive experiments show that FedAC achieves superior empirical performance, increasing the test accuracy by around 1.82% and 12.67% on CIFAR-10 and CIFAR-100 datasets, respectively, under different non-IID settings compared to SOTA methods.
|
[
"['Yuxin Zhang' 'Haoyu Chen' 'Zheng Lin' 'Zhe Chen' 'Jin Zhao']"
] |
null | null |
2403.16464
| null | null |
http://arxiv.org/pdf/2403.16464v1
|
2024-03-25T06:46:27Z
|
2024-03-25T06:46:27Z
|
Training Generative Adversarial Network-Based Vocoder with Limited Data
Using Augmentation-Conditional Discriminator
|
A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics. However, this data-driven model requires a large amount of training data incurring high data-collection costs. This fact motivates us to train a GAN-based vocoder on limited data. A promising solution is to augment the training data to avoid overfitting. However, a standard discriminator is unconditional and insensitive to distributional changes caused by data augmentation. Thus, augmented speech (which can be extraordinary) may be considered real speech. To address this issue, we propose an augmentation-conditional discriminator (AugCondD) that receives the augmentation state as input in addition to speech, thereby assessing the input speech according to the augmentation state, without inhibiting the learning of the original non-augmented distribution. Experimental results indicate that AugCondD improves speech quality under limited data conditions while achieving comparable speech quality under sufficient data conditions. Audio samples are available at https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/augcondd/.
|
[
"['Takuhiro Kaneko' 'Hirokazu Kameoka' 'Kou Tanaka']"
] |
null | null |
2403.16469
| null | null |
http://arxiv.org/pdf/2403.16469v1
|
2024-03-25T06:50:25Z
|
2024-03-25T06:50:25Z
|
Learning from Reduced Labels for Long-Tailed Data
|
Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming. Unfortunately, despite being a common approach to mitigate labeling costs, existing weakly supervised learning methods struggle to adequately preserve supervised information for tail samples, resulting in a decline in accuracy for the tail classes. To alleviate this problem, we introduce a novel weakly supervised labeling setting called Reduced Label. The proposed labeling setting not only avoids the decline of supervised information for the tail samples, but also decreases the labeling costs associated with long-tailed data. Additionally, we propose an straightforward and highly efficient unbiased framework with strong theoretical guarantees to learn from these Reduced Labels. Extensive experiments conducted on benchmark datasets including ImageNet validate the effectiveness of our approach, surpassing the performance of state-of-the-art weakly supervised methods.
|
[
"['Meng Wei' 'Zhongnian Li' 'Yong Zhou' 'Xinzheng Xu']"
] |
null | null |
2403.16482
| null | null |
http://arxiv.org/pdf/2403.16482v1
|
2024-03-25T07:08:01Z
|
2024-03-25T07:08:01Z
|
Determined Multi-Label Learning via Similarity-Based Prompt
|
In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each training instance is time- and labor-consuming for real-world applications. To alleviate this problem, a novel labeling setting termed textit{Determined Multi-Label Learning} (DMLL) is proposed, aiming to effectively alleviate the labeling cost inherent in multi-label tasks. In this novel labeling setting, each training instance is associated with a textit{determined label} (either "Yes" or "No"), which indicates whether the training instance contains the provided class label. The provided class label is randomly and uniformly selected from the whole candidate labels set. Besides, each training instance only need to be determined once, which significantly reduce the annotation cost of the labeling task for multi-label datasets. In this paper, we theoretically derive an risk-consistent estimator to learn a multi-label classifier from these determined-labeled training data. Additionally, we introduce a similarity-based prompt learning method for the first time, which minimizes the risk-consistent loss of large-scale pre-trained models to learn a supplemental prompt with richer semantic information. Extensive experimental validation underscores the efficacy of our approach, demonstrating superior performance compared to existing state-of-the-art methods.
|
[
"['Meng Wei' 'Zhongnian Li' 'Peng Ying' 'Yong Zhou' 'Xinzheng Xu']"
] |
null | null |
2403.16495
| null | null |
http://arxiv.org/abs/2403.16495v1
|
2024-03-25T07:23:23Z
|
2024-03-25T07:23:23Z
|
LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural
Network for Traffic Flow Forecasting
|
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models. However, due to structural limitations, existing STGNNs can only utilize short-range traffic flow data; therefore, the models cannot adequately learn the complex trends and periodic features in traffic flow. Besides, it is challenging to extract the key temporal information from the long historical traffic series and obtain a compact representation. To solve the above problems, we propose a novel LSTTN (Long-Short Term Transformer-based Network) framework comprehensively considering the long- and short-term features in historical traffic flow. First, we employ a masked subseries Transformer to infer the content of masked subseries from a small portion of unmasked subseries and their temporal context in a pretraining manner, forcing the model to efficiently learn compressed and contextual subseries temporal representations from long historical series. Then, based on the learned representations, long-term trend is extracted by using stacked 1D dilated convolution layers, and periodic features are extracted by dynamic graph convolution layers. For the difficulties in making time-step level prediction, LSTTN adopts a short-term trend extractor to learn fine-grained short-term temporal features. Finally, LSTTN fuses the long-term trend, periodic features and short-term features to obtain the prediction results. Experiments on four real-world datasets show that in 60-minute-ahead long-term forecasting, the LSTTN model achieves a minimum improvement of 5.63% and a maximum improvement of 16.78% over baseline models. The source code is available at https://github.com/GeoX-Lab/LSTTN.
|
[
"['Qinyao Luo' 'Silu He' 'Xing Han' 'Yuhan Wang' 'Haifeng Li']"
] |
null | null |
2403.16497
| null | null |
http://arxiv.org/pdf/2403.16497v2
|
2024-07-15T07:24:36Z
|
2024-03-25T07:29:18Z
|
PathoTune: Adapting Visual Foundation Model to Pathological Specialists
|
As natural image understanding moves towards the pretrain-finetune era, research in pathology imaging is concurrently evolving. Despite the predominant focus on pretraining pathological foundation models, how to adapt foundation models to downstream tasks is little explored. For downstream adaptation, we propose the existence of two domain gaps, i.e., the Foundation-Task Gap and the Task-Instance Gap. To mitigate these gaps, we introduce PathoTune, a framework designed to efficiently adapt pathological or even visual foundation models to pathology-specific tasks via multi-modal prompt tuning. The proposed framework leverages Task-specific Visual Prompts and Task-specific Textual Prompts to identify task-relevant features, along with Instance-specific Visual Prompts for encoding single pathological image features. Results across multiple datasets at both patch-level and WSI-level demonstrate its superior performance over single-modality prompt tuning approaches. Significantly, PathoTune facilitates the direct adaptation of natural visual foundation models to pathological tasks, drastically outperforming pathological foundation models with simple linear probing. The code is available at https://github.com/openmedlab/PathoDuet.
|
[
"['Jiaxuan Lu' 'Fang Yan' 'Xiaofan Zhang' 'Yue Gao' 'Shaoting Zhang']"
] |
null | null |
2403.16509
| null | null |
http://arxiv.org/pdf/2403.16509v1
|
2024-03-25T07:48:34Z
|
2024-03-25T07:48:34Z
|
Human Understanding AI Paper Challenge 2024 -- Dataset Design
|
In 2024, we will hold a research paper competition (the third Human Understanding AI Paper Challenge) for the research and development of artificial intelligence technologies to understand human daily life. This document introduces the datasets that will be provided to participants in the competition, and summarizes the issues to consider in data processing and learning model development.
|
[
"['Se Won Oh' 'Hyuntae Jeong' 'Jeong Mook Lim' 'Seungeun Chung'\n 'Kyoung Ju Noh']"
] |
null | null |
2403.16523
| null | null |
http://arxiv.org/pdf/2403.16523v1
|
2024-03-25T08:06:08Z
|
2024-03-25T08:06:08Z
|
Causal Discovery from Poisson Branching Structural Causal Model Using
High-Order Cumulant with Path Analysis
|
Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientific and industrial scenarios. One of the most common characteristics of count data is the inherent branching structure described by a binomial thinning operator and an independent Poisson distribution that captures both branching and noise. For instance, in a population count scenario, mortality and immigration contribute to the count, where survival follows a Bernoulli distribution, and immigration follows a Poisson distribution. However, causal discovery from such data is challenging due to the non-identifiability issue: a single causal pair is Markov equivalent, i.e., $Xrightarrow Y$ and $Yrightarrow X$ are distributed equivalent. Fortunately, in this work, we found that the causal order from $X$ to its child $Y$ is identifiable if $X$ is a root vertex and has at least two directed paths to $Y$, or the ancestor of $X$ with the most directed path to $X$ has a directed path to $Y$ without passing $X$. Specifically, we propose a Poisson Branching Structure Causal Model (PB-SCM) and perform a path analysis on PB-SCM using high-order cumulants. Theoretical results establish the connection between the path and cumulant and demonstrate that the path information can be obtained from the cumulant. With the path information, causal order is identifiable under some graphical conditions. A practical algorithm for learning causal structure under PB-SCM is proposed and the experiments demonstrate and verify the effectiveness of the proposed method.
|
[
"['Jie Qiao' 'Yu Xiang' 'Zhengming Chen' 'Ruichu Cai' 'Zhifeng Hao']"
] |
null | null |
2403.16542
| null | null |
http://arxiv.org/pdf/2403.16542v1
|
2024-03-25T08:35:19Z
|
2024-03-25T08:35:19Z
|
Differentially Private Online Federated Learning with Correlated Noise
|
We propose a novel differentially private algorithm for online federated learning that employs temporally correlated noise to improve the utility while ensuring the privacy of the continuously released models. To address challenges stemming from DP noise and local updates with streaming noniid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an $(epsilon, delta)$-DP budget, we establish a dynamic regret bound over the entire time horizon that quantifies the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments validate the efficacy of the proposed algorithm.
|
[
"['Jiaojiao Zhang' 'Linglingzhi Zhu' 'Mikael Johansson']"
] |
null | null |
2403.16557
| null | null |
http://arxiv.org/pdf/2403.16557v1
|
2024-03-25T09:16:59Z
|
2024-03-25T09:16:59Z
|
Accelerating Federated Learning by Selecting Beneficial Herd of Local
Gradients
|
Federated Learning (FL) is a distributed machine learning framework in communication network systems. However, the systems' Non-Independent and Identically Distributed (Non-IID) data negatively affect the convergence efficiency of the global model, since only a subset of these data samples are beneficial for model convergence. In pursuit of this subset, a reliable approach involves determining a measure of validity to rank the samples within the dataset. In this paper, We propose the BHerd strategy which selects a beneficial herd of local gradients to accelerate the convergence of the FL model. Specifically, we map the distribution of the local dataset to the local gradients and use the Herding strategy to obtain a permutation of the set of gradients, where the more advanced gradients in the permutation are closer to the average of the set of gradients. These top portion of the gradients will be selected and sent to the server for global aggregation. We conduct experiments on different datasets, models and scenarios by building a prototype system, and experimental results demonstrate that our BHerd strategy is effective in selecting beneficial local gradients to mitigate the effects brought by the Non-IID dataset, thus accelerating model convergence.
|
[
"['Ping Luo' 'Xiaoge Deng' 'Ziqing Wen' 'Tao Sun' 'Dongsheng Li']"
] |
null | null |
2403.16561
| null | null |
http://arxiv.org/pdf/2403.16561v1
|
2024-03-25T09:24:05Z
|
2024-03-25T09:24:05Z
|
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
|
Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches. To tackle this issue, we propose FedFixer, where the personalized model is introduced to cooperate with the global model to effectively select clean client-specific samples. In the dual models, updating the personalized model solely at a local level can lead to overfitting on noisy data due to limited samples, consequently affecting both the local and global models' performance. To mitigate overfitting, we address this concern from two perspectives. Firstly, we employ a confidence regularizer to alleviate the impact of unconfident predictions caused by label noise. Secondly, a distance regularizer is implemented to constrain the disparity between the personalized and global models. We validate the effectiveness of FedFixer through extensive experiments on benchmark datasets. The results demonstrate that FedFixer can perform well in filtering noisy label samples on different clients, especially in highly heterogeneous label noise scenarios.
|
[
"['Xinyuan Ji' 'Zhaowei Zhu' 'Wei Xi' 'Olga Gadyatskaya' 'Zilong Song'\n 'Yong Cai' 'Yang Liu']"
] |
null | null |
2403.16569
| null | null |
http://arxiv.org/pdf/2403.16569v1
|
2024-03-25T09:36:10Z
|
2024-03-25T09:36:10Z
|
Revealing Vulnerabilities of Neural Networks in Parameter Learning and
Defense Against Explanation-Aware Backdoors
|
Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks. Nonetheless, these techniques could potentially generate misleading explanations. Blinding attacks can drastically alter a machine learning algorithm's prediction and explanation, providing misleading information by adding visually unnoticeable artifacts into the input, while maintaining the model's accuracy. It poses a serious challenge in ensuring the reliability of XAI methods. To ensure the reliability of XAI methods poses a real challenge, we leverage statistical analysis to highlight the changes in CNN weights within a CNN following blinding attacks. We introduce a method specifically designed to limit the effectiveness of such attacks during the evaluation phase, avoiding the need for extra training. The method we suggest defences against most modern explanation-aware adversarial attacks, achieving an approximate decrease of ~99% in the Attack Success Rate (ASR) and a ~91% reduction in the Mean Square Error (MSE) between the original explanation and the defended (post-attack) explanation across three unique types of attacks.
|
[
"['Md Abdul Kadir' 'GowthamKrishna Addluri' 'Daniel Sonntag']"
] |
null | null |
2403.16571
| null | null |
http://arxiv.org/pdf/2403.16571v1
|
2024-03-25T09:36:51Z
|
2024-03-25T09:36:51Z
|
NSINA: A News Corpus for Sinhala
|
The introduction of large language models (LLMs) has advanced natural language processing (NLP), but their effectiveness is largely dependent on pre-training resources. This is especially evident in low-resource languages, such as Sinhala, which face two primary challenges: the lack of substantial training data and limited benchmarking datasets. In response, this study introduces NSINA, a comprehensive news corpus of over 500,000 articles from popular Sinhala news websites, along with three NLP tasks: news media identification, news category prediction, and news headline generation. The release of NSINA aims to provide a solution to challenges in adapting LLMs to Sinhala, offering valuable resources and benchmarks for improving NLP in the Sinhala language. NSINA is the largest news corpus for Sinhala, available up to date.
|
[
"['Hansi Hettiarachchi' 'Damith Premasiri' 'Lasitha Uyangodage'\n 'Tharindu Ranasinghe']"
] |
null | null |
2403.16576
| null | null |
http://arxiv.org/pdf/2403.16576v2
|
2024-06-26T03:06:42Z
|
2024-03-25T09:41:49Z
|
Antigen-Specific Antibody Design via Direct Energy-based Preference
Optimization
|
Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature. In this paper, we tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences, considering both rationality and functionality. Leveraging a pre-trained conditional diffusion model that jointly models sequences and structures of antibodies with equivariant neural networks, we propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens. Our method involves fine-tuning the pre-trained diffusion model using a residue-level decomposed energy preference. Additionally, we employ gradient surgery to address conflicts between various types of energy, such as attraction and repulsion. Experiments on RAbD benchmark show that our approach effectively optimizes the energy of generated antibodies and achieves state-of-the-art performance in designing high-quality antibodies with low total energy and high binding affinity simultaneously, demonstrating the superiority of our approach.
|
[
"['Xiangxin Zhou' 'Dongyu Xue' 'Ruizhe Chen' 'Zaixiang Zheng' 'Liang Wang'\n 'Quanquan Gu']"
] |
null | null |
2403.16582
| null | null |
http://arxiv.org/pdf/2403.16582v1
|
2024-03-25T09:49:42Z
|
2024-03-25T09:49:42Z
|
In the Search for Optimal Multi-view Learning Models for Crop
Classification with Global Remote Sensing Data
|
Crop classification is of critical importance due to its role in studying crop pattern changes, resource management, and carbon sequestration. When employing data-driven techniques for its prediction, utilizing various temporal data sources is necessary. Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction. However, they face substantial challenges when dealing with multiple input patterns. The literature offers limited guidance for Multi-View Learning (MVL) scenarios, as it has primarily focused on exploring fusion strategies with specific encoders and validating them in local regions. In contrast, we investigate the impact of simultaneous selection of the fusion strategy and the encoder architecture evaluated on a global-scale cropland and crop-type classifications. We use a range of five fusion strategies (Input, Feature, Decision, Ensemble, Hybrid) and five temporal encoder architectures (LSTM, GRU, TempCNN, TAE, L-TAE) as possible MVL model configurations. The validation is on the CropHarvest dataset that provides optical, radar, and weather time series, and topographic information as input data. We found that in scenarios with a limited number of labeled samples, a unique configuration is insufficient for all the cases. Instead, a specialized combination, including encoder and fusion strategy, should be meticulously sought. To streamline this search process, we suggest initially identifying the optimal encoder architecture tailored for a particular fusion strategy, and then determining the most suitable fusion strategy for the classification task. We provide a technical framework for researchers exploring crop classification or related tasks through a MVL approach.
|
[
"['Francisco Mena' 'Diego Arenas' 'Andreas Dengel']"
] |
null | null |
2403.16591
| null | null |
http://arxiv.org/pdf/2403.16591v3
|
2024-04-02T14:28:06Z
|
2024-03-25T10:06:45Z
|
Deciphering the Interplay between Local Differential Privacy, Average
Bayesian Privacy, and Maximum Bayesian Privacy
|
The swift evolution of machine learning has led to emergence of various definitions of privacy due to the threats it poses to privacy, including the concept of local differential privacy (LDP). Although widely embraced and utilized across numerous domains, this conventional approach to measure privacy still exhibits certain limitations, spanning from failure to prevent inferential disclosure to lack of consideration for the adversary's background knowledge. In this comprehensive study, we introduce Bayesian privacy and delve into the intricate relationship between LDP and its Bayesian counterparts, unveiling novel insights into utility-privacy trade-offs. We introduce a framework that encapsulates both attack and defense strategies, highlighting their interplay and effectiveness. The relationship between LDP and Maximum Bayesian Privacy (MBP) is first revealed, demonstrating that under uniform prior distribution, a mechanism satisfying $xi$-LDP will satisfy $xi$-MBP and conversely $xi$-MBP also confers 2$xi$-LDP. Our next theoretical contribution are anchored in the rigorous definitions and relationships between Average Bayesian Privacy (ABP) and Maximum Bayesian Privacy (MBP), encapsulated by equations $epsilon_{p,a} leq frac{1}{sqrt{2}}sqrt{(epsilon_{p,m} + epsilon)cdot(e^{epsilon_{p,m} + epsilon} - 1)}$. These relationships fortify our understanding of the privacy guarantees provided by various mechanisms. Our work not only lays the groundwork for future empirical exploration but also promises to facilitate the design of privacy-preserving algorithms, thereby fostering the development of trustworthy machine learning solutions.
|
[
"['Xiaojin Zhang' 'Yulin Fei' 'Wei Chen']"
] |
null | null |
2403.16594
| null | null |
http://arxiv.org/pdf/2403.16594v1
|
2024-03-25T10:13:52Z
|
2024-03-25T10:13:52Z
|
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for
Medical Image Segmentation
|
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty. Uncertainty estimation (UE) methods provide potential solutions for evaluating prediction reliability and improving the model confidence calibration. Despite increasing interest in UE, challenges persist, such as the need for explicit methods to capture aleatoric uncertainty and align uncertainty estimates with real-life disagreements among domain experts. This paper proposes an Expert Disagreement-Guided Uncertainty Estimation (EDUE) for medical image segmentation. By leveraging variability in ground-truth annotations from multiple raters, we guide the model during training and incorporate random sampling-based strategies to enhance calibration confidence. Our method achieves 55% and 23% improvement in correlation on average with expert disagreements at the image and pixel levels, respectively, better calibration, and competitive segmentation performance compared to the state-of-the-art deep ensembles, requiring only a single forward pass.
|
[
"['Kudaibergen Abutalip' 'Numan Saeed' 'Ikboljon Sobirov'\n 'Vincent Andrearczyk' 'Adrien Depeursinge' 'Mohammad Yaqub']"
] |
null | null |
2403.16607
| null | null |
http://arxiv.org/pdf/2403.16607v1
|
2024-03-25T10:38:17Z
|
2024-03-25T10:38:17Z
|
Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction
and Defect-Focus
|
Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm. This work introduces Style Filter, a tailored methodology for industrial contexts. By selectively filtering source domain data before knowledge transfer, Style Filter reduces the quantity of data while maintaining or even enhancing the performance of transfer learning strategy. Offering label-free operation, minimal reliance on prior knowledge, independence from specific models, and re-utilization, Style Filter is evaluated on authentic industrial datasets, highlighting its effectiveness when employed before conventional transfer strategies in the deep learning domain. The results underscore the effectiveness of Style Filter in real-world industrial applications.
|
[
"['Chen Li' 'Ruijie Ma' 'Xiang Qian' 'Xiaohao Wang' 'Xinghui Li']"
] |
null | null |
2403.16610
| null | null |
http://arxiv.org/pdf/2403.16610v1
|
2024-03-25T10:40:04Z
|
2024-03-25T10:40:04Z
|
Distributed collaborative anomalous sound detection by embedding sharing
|
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn an anomalous sound detection model while keeping their raw data private from each other. In the context of industrial machine anomalous sound detection, each client possesses data from different machines or different operational states, making it challenging to learn through federated learning or split learning. In our proposed method, each client calculates embeddings using a common pre-trained model developed for sound data classification, and these calculated embeddings are aggregated on the server to perform anomalous sound detection through outlier exposure. Experiments showed that our proposed method improves the AUC of anomalous sound detection by an average of 6.8%.
|
[
"['Kota Dohi' 'Yohei Kawaguchi']"
] |
null | null |
2403.16612
| null | null |
http://arxiv.org/pdf/2403.16612v2
|
2024-04-04T12:35:33Z
|
2024-03-25T10:42:48Z
|
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
|
Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big impact on the world. Calibration of the neural networks provides a way to ensure our confidence in the predictions. However, calibrating regression models is an under-researched topic, especially in forecasters. We calibrate a UNet++ based architecture, which was shown to outperform physics-based models in temperature anomalies. We show that with a slight trade-off between prediction error and calibration error, it is possible to get more reliable and sharper forecasts. We believe that calibration should be an important part of safety-critical machine learning applications such as weather forecasters.
|
[
"['Busra Asan' 'Abdullah Akgül' 'Alper Unal' 'Melih Kandemir' 'Gozde Unal']"
] |
null | null |
2403.16630
| null | null |
http://arxiv.org/pdf/2403.16630v1
|
2024-03-25T11:20:23Z
|
2024-03-25T11:20:23Z
|
A comparative analysis of embedding models for patent similarity
|
This paper makes two contributions to the field of text-based patent similarity. First, it compares the performance of different kinds of patent-specific pretrained embedding models, namely static word embeddings (such as word2vec and doc2vec models) and contextual word embeddings (such as transformers based models), on the task of patent similarity calculation. Second, it compares specifically the performance of Sentence Transformers (SBERT) architectures with different training phases on the patent similarity task. To assess the models' performance, we use information about patent interferences, a phenomenon in which two or more patent claims belonging to different patent applications are proven to be overlapping by patent examiners. Therefore, we use these interferences cases as a proxy for maximum similarity between two patents, treating them as ground-truth to evaluate the performance of the different embedding models. Our results point out that, first, Patent SBERT-adapt-ub, the domain adaptation of the pretrained Sentence Transformer architecture proposed in this research, outperforms the current state-of-the-art in patent similarity. Second, they show that, in some cases, large static models performances are still comparable to contextual ones when trained on extensive data; thus, we believe that the superiority in the performance of contextual embeddings may not be related to the actual architecture but rather to the way the training phase is performed.
|
[
"['Grazia Sveva Ascione' 'Valerio Sterzi']"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.