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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Yujia Bu, Tong Ding, Jianhao Hao, Zecheng Huang, Tiejun Yu, Zhaofei NERCVT School of Computer Science Peking University China National Key Laboratory for Multimedia Information Processing Peking University China School of Computer Science Peking University China Institution for Artificial Intelligence Peking University China

Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over artificial Neural Networks (ANNs) in terms of energy efficiency and interpretability. Nonetheless, similar to ANNs, the robustness of SNNs remains a challenge, especially when facing adversarial attacks. Existing techniques, whether adapted from ANNs or specifically designed for SNNs, exhibit limitations in training SNNs or defending against strong attacks. In this paper, we propose a novel approach to enhance the robustness of SNNs through gradient sparsity regularization. We observe that SNNs exhibit greater resilience to random perturbations compared to adversarial perturbations, even at larger scales. Motivated by this, we aim to narrow the gap between SNNs under adversarial and random perturbations, thereby improving their overall robustness. To achieve this, we theoretically prove that this performance gap is upper bounded by the gradient sparsity of the probability associated with the true label concerning the input image, laying the groundwork for a practical strategy to train robust SNNs by regularizing the gradient sparsity. We validate the effectiveness of our approach through extensive experiments on both image-based and event-based datasets. The results demonstrate notable improvements in the robustness of SNNs. Our work highlights the importance of gradient sparsity in SNNs and its role in enhancing robustness. Copyright 2024 by the author(s)

关键词： Generative adversarial networks

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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Pruning Networks With Cross-Layer Ranking & k-Reciprocal Nearest Filters

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IEEE Transactions on Neural Networks and Learning Systems 2023年第11期34卷 9139-9148页

作者： Lin, Mingbao Cao, Liujuan Zhang, Yuxin Shao, Ling Lin, Chia-Wen Ji, Rongrong School of Informatics Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China Tencent Youtu Laboratory Shanghai200233 China School of Informatics Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City Computer Science Department Xiamen361005 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates National Tsing Hua University Department of Electrical Engineering and the Institute of Communications Engineering Hsinchu30013 Taiwan Institute of Artificial Intelligence Xiamen University Xiamen361005 China

This article focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a 'long-tail' pruning problem in magnitude-based weight pruning methods and then propose a computation-aware measurement for individual weight importance, followed by a cross-layer ranking (CLR) of weights to identify and remove the bottom-ranked weights. Consequently, the per-layer sparsity makes up the pruned network structure in our filter pruning. Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters. To pick the preserved filters from these recommended groups, we further devise a ${k}$ -reciprocal nearest filter (RNF) selection scheme where the selected filters fall into the intersection of these recommended groups. Both our pruned network structure and the filter selection are nonlearning processes, which, thus, significantly reduces the pruning complexity and differentiates our method from existing works. We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and 95.0% parameters from VGGNet-16 with even 0.3% accuracy improvements. On ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only 1.7% top-five accuracy drops. Our project is available at https://***/lmbxμCLR-RNF. © 2012 IEEE.

关键词： computer architecture

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Cross-Modal Recipe Retrieval With Fine-Grained Prompting Alignment and Evidential Semantic Consistency

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IEEE Transactions on Multimedia 2025年 27卷 2783-2794页

作者： Huang, Xu Liu, Jin Zhang, Zhizhong Xie, Yuan Tang, Yongqiang Zhang, Wensheng Cui, Xiaohui Wuhan University School of Computer Science Wuhan430072 China Ministry of Education Wuhan430062 China East China Normal University School of Computer Science and Technology Shanghai200062 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Alignment between the food images and the corresponding recipes is an emerging cross-modal representation learning task. In this task, the recipes are composed of three components, i.e., food title, ingredient lists, and cooking instructions, which require a fine-grained alignment between the features of the two modalities. Existing methods usually aggregate the recipes into global embeddings and then align them with the global image embeddings. Meanwhile, semantic classification is frequently used in these methods to regularize the embeddings of the two modalities. While these methods are efficient, there remain two problems. (1) Forcing the alignment between the global images and recipes embeddings may result in losing the component-specific information. (2) The high diversity of food appearance leads to high uncertainty in the semantic classification of food images and recipes. To solve these problems, we propose a Fine-grained Prompting and Alignment (FPA) model to enhance the feature extraction and bring more component-specific information for fine-grained alignment. Furthermore, to regularize the semantic information contained in the cross-modal features, we design an Evidential Semantic Consistency (ESC) loss to keep the cross-modal semantic consistency. We have conducted comprehensive experiments on the benchmark dataset Recipe1M and the state-of-the-art results on the cross-modal recipe retrieval task demonstrate the effectiveness of our method. © 1999-2012 IEEE.

关键词： Alignment

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Automatic Detection of Cognitive Presence in Online Collaborative Learning Discussions Using Knowledge Information 4

Automatic Detection of Cognitive Presence in Online Collabor...

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4th International Conference on Educational Technology, ICET 2024

作者： Luo, Sheng Ye, Junmin Yu, Shuang Yin, Xinghan Ren, Wen Si, Kaiyan Zhao, Gang Central China Normal University School of Computer Science Hubei China Central China Normal University Faculty of Artificial Intelligence Education Hubei China Central China Normal University School of Computer Science Wuhan China Central China Normal University Hubei Key Laboratory of Digital Education Faculty of Artificial Intelligence In Education Wuhan China

ISBN: (纸本)9798350376944

Learners' cognition is crucial in online collaborative learning, and cognitive presence is an important indicator of cognition. Therefore, how to automatically detect cognitive presence in student discussions is a hot topic in current research. However, existing studies have not considered the knowledge information in collaborative learning discussions, which is relevant to students' cognition. Based on this, this study proposed an automatic detection approach that utilizes knowledge information and deep learning models to automatically detect cognitive presence in student discussions. We conducted our experiments on a dataset containing 8795 pieces of data. The experimental results show that the proposed model achieves an accuracy of 77.63%, which outperforms all baseline models. Our work provides an accurate and efficient tool for automatically analyzing cognitive presence and helps teachers implement interventions. © 2024 IEEE.

关键词： Collaborative learning

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PM2.5 concentration prediction model based on SA-LSTM

PM2.5 concentration prediction model based on SA-LSTM

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2023 International Conference on artificial intelligence, Systems and Network Security, AISNS 2023

作者： Wang, Zhen Liu, Lizhi Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Artificial Intelligence Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798400716966

PM2.5 concentration is one of the important indicators for measuring air quality. In order to enhance the precision of PM2.5 concentration forecasting, a short-Term prediction model for PM2.5 concentration based on SA-LSTM (Self Attention Long Short Term Memory) is proposed. LSTM stores and transmits the update information of the previous time step through cell state and calculates the output vectors of each time step in the temporal input. Self Attention extracts the internal dependency relationships between multiple output vectors and weights them to obtain a key output vector that integrates global input information. The experiment selected air quality monitoring data from the Yangshoujing Avenue Automatic Air Quality Monitoring Station in Yidu City, Yichang City from January 1, 2019 to December 31, 2020. The results showed that using the SA-LSTM model to predict the PM2.5 concentration in the next hour reduced RMSE and MAE by 18% and 11% respectively, and increased R2 by 3.78% compared to the benchmark LSTM model. © 2023 ACM.

关键词： Long short-term memory

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Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction

arXiv

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arXiv 2024年

作者： Curtis, Aidan Kumar, Nishanth Cao, Jing Lozano-Pérez, Tomás Kaelbling, Leslie Pack MIT Computer Science and Artificial Intelligence Laboratory United States

Recent developments in pretrained large language models (LLMs) applied to robotics have demonstrated their capacity for sequencing a set of discrete skills to achieve open-ended goals in simple robotic tasks. In this paper, we examine the topic of LLM planning for a set of continuously parameterized skills whose execution must avoid violations of a set of kinematic, geometric, and physical constraints. We prompt the LLM to output code for a function with open parameters, which, together with environmental constraints, can be viewed as a Continuous Constraint Satisfaction Problem (CCSP). This CCSP can be solved through sampling or optimization to find a skill sequence and continuous parameter settings that achieve the goal while avoiding constraint violations. Additionally, we consider cases where the LLM proposes unsatisfiable CCSPs, such as those that are kinematically infeasible, dynamically unstable, or lead to collisions, and re-prompt the LLM to form a new CCSP accordingly. Experiments across simulated and real-world domains demonstrate that our proposed strategy, PRoC3S, is capable of solving a wide range of complex manipulation tasks with realistic constraints much more efficiently and effectively than existing baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Constraint satisfaction problems

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Learning to Look Around: Enhancing Teleoperation and Learning with a Human-like Actuated Neck

arXiv

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arXiv 2024年

作者： Sen, Bipasha Wang, Michelle Thakur, Nandini Agarwal, Aditya Agrawal, Pulkit MIT Computer Science and Artificial Intelligence Laboratory United States

We introduce a teleoperation system that integrates a 5-DOF actuated neck, designed to replicate natural human head movements and perception. By enabling behaviors like "peeking" or "tilting", the system provides operators with a more intuitive and comprehensive view of the environment, improving task performance, reducing cognitive load, and facilitating complex whole-body manipulation. We demonstrate the benefits of natural perception across seven challenging teleoperation tasks, showing how the actuated neck enhances the scope and efficiency of remote operation. Furthermore, we investigate its role in training autonomous policies through imitation learning. In three distinct tasks, the actuated neck supports better spatial awareness, reduces distribution shift, and enables adaptive task-specific adjustments compared to a static wide-angle camera. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote control

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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Two Different Approaches of Applying ANFIS Based MPPT for a PV Water Pumping System with a SEPIC Converter

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Applied Solar Energy (English translation of Geliotekhnika) 2024年第3期60卷 383-399页

作者： Miqoi, S. Tidhaf, B. El Ougli, A. Team of Embedded Systems Renewable Energy and Artificial Intelligence National School of Applied Sciences Mohammed First University Oujda Oujda Morocco Computer Science Signal Automation and Cognitivism Laboratory Faculty of Sciences Dhar El Mahraz Fez Fez30050 Morocco

Abstract: The main objective of this work is to enhance the performance of the Photovoltaic water pumping system to cover the water requirement in rural areas. To do so, it is important to make sure that the PV array produces its maximum power at all times, which can be influenced by external condition (mainly the temperature and irradiation). Hence, we are employing the Adaptive Neuro-Fuzzy Inference System based MPPT in two ways. The ANFIS controller is considered more accurate and efficient as it uses an artificial neural network to learn from training data and generate fuzzy rules based on that data. Both approaches of ANFIS are used to control the duty cycle of the SEPIC converter, which connects the PV panel to the DC motor feeding the water pump. The system combining the PV panel, the SEPIC converter, the controller and the DC motor, is designed and simulated under MATLAB/Simulink. The performance of the proposed methods is tested under various meteorological conditions. © Allerton Press, Inc. 2024. ISSN 0003-701X, Applied Solar Energy, 2024, Vol. 60, No. 3, pp. 383–399. Allerton Press, Inc., 2024. ISSN 0003-701X, Applied Solar Energy, 2024. Allerton Press, Inc., 2024.

关键词： DC motors

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