GenAI4ELab/papercli-papers huggingface dataset:
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
Authors: Shimin Zhang; Qu Yang; Chenxiang Ma; Jibin Wu; Haizhou Li; Kay Chen Tan
Venue: AAAI 2024 (main)

Abstract: The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. To address this challenge, we propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF. The proposed model incorporates carefully designed somatic and dendritic compartments that are tailored to facilitate learning long-term temporal dependencies. Furthermore, the theoretical analysis is provided to validate the effectiveness of TC-LIF in propagating error gradients over an extended temporal duration. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, and high energy efficiency of the proposed TC-LIF model. Therefore, this work opens up a myriad of opportunities for solving challenging temporal processing tasks on emerging neuromorphic computing systems. Our code is publicly available at https://github.com/ZhangShimin1/TC-LIF.
Learning with Noisy Labels Using Hyperspherical Margin Weighting
Authors: Shuo Zhang; Yuwen Li; Zhongyu Wang; Jianqing Li; Chengyu Liu
Venue: AAAI 2024 (main)

Abstract: Datasets often include noisy labels, but learning from them is difficult. Since mislabeled examples usually have larger loss values in training, the small-loss trick is regarded as a standard metric to identify the clean example from the training set for better performance. Nonetheless, this proposal ignores that some clean but hard-to-learn examples also generate large losses. They could be misidentified by this criterion. In this paper, we propose a new metric called the Integrated Area Margin (IAM), which is superior to the traditional small-loss trick, particularly in recognizing the clean but hard-to-learn examples. According to the IAM, we further offer the Hyperspherical Margin Weighting (HMW) approach. It is a new sample weighting strategy that restructures the importance of each example. It should be highlighted that our approach is universal and can strengthen various methods in this field. Experiments on both benchmark and real-world datasets indicate that our HMW outperforms many state-of-the-art approaches in learning with noisy label tasks. Codes are available at https://github.com/Zhangshuojackpot/HMW.
One Step Closer to Unbiased Aleatoric Uncertainty Estimation
Authors: Wang Zhang; Ziwen Martin Ma; Subhro Das; Tsui-Wei Lily Weng; Alexandre Megretski; Luca Daniel; Lam M. Nguyen
Venue: AAAI 2024 (main)

Abstract: Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model) uncertainty. In this paper, we point out that the existing popular variance attenuation method highly overestimates aleatoric uncertainty. To address this issue, we proposed a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer approximation to the actual data uncertainty than the standard method.
Gaussian Process Neural Additive Models
Authors: Wei Zhang; Brian Barr; John Paisley
Venue: AAAI 2024 (main)

Abstract: Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance where interpretable and explainable models are required. The recent development of Neural Additive Models (NAMs) poses a major step in the direction of interpretable deep learning for tabular datasets. In this paper, we propose a new subclass of NAMs that utilize a single-layer neural network construction of the Gaussian process via random Fourier features, which we call Gaussian Process Neural Additive Models (GP-NAM). GP-NAMs have the advantage of a convex objective function and number of trainable parameters that grows linearly with feature dimensions. It suffers no loss in performance compared with deeper NAM approaches because GPs are well-suited to learning complex non-parametric univariate functions. We demonstrate the performance of GP-NAM on several tabular datasets, showing that it achieves comparable performance in both classification and regression tasks with a massive reduction in the number of parameters.
From Toxic to Trustworthy: Using Self-Distillation and Semi-supervised Methods to Refine Neural Networks
Authors: Xianda Zhang; Baolin Zheng; Jianbao Hu; Chengyang Li; Xiaoying Bai
Venue: AAAI 2024 (main)

Abstract: Despite the tremendous success of deep neural networks (DNNs) across various fields, their susceptibility to potential backdoor attacks seriously threatens their application security, particularly in safety-critical or security-sensitive ones. Given this growing threat, there is a pressing need for research into purging backdoors from DNNs. However, prior efforts on erasing backdoor triggers not only failed to withstand increasingly powerful attacks but also resulted in reduced model performance. In this paper, we propose From Toxic to Trustworthy (FTT), an innovative approach to eliminate backdoor triggers while simultaneously enhancing model accuracy. Following the stringent and practical assumption of limited availability of clean data, we introduce a self-attention distillation (SAD) method to remove the backdoor by aligning the shallow and deep parts of the network. Furthermore, we first devise a semi-supervised learning (SSL) method that leverages ubiquitous and available poisoned data to further purify backdoors and improve accuracy. Extensive experiments on various attacks and models have shown that our FTT can reduce the attack success rate from 97% to 1% and improve the accuracy of 4% on average, demonstrating its effectiveness in mitigating backdoor attacks and improving model performance. Compared to state-of-the-art (SOTA) methods, our FTT can reduce the attack success rate by 2 times and improve the accuracy by 5%, shedding light on backdoor cleansing.
Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation
Authors: Xinyu Zhang; Meng Kang; Shuai Lü
Venue: AAAI 2024 (main)

Abstract: Recently, instance contrastive learning achieves good results in unsupervised domain adaptation. It reduces the distances between positive samples and the anchor, increases the distances between negative samples and the anchor, and learns discriminative feature representations for target samples. However, most recent methods for identifying positive and negative samples are based on whether the pseudo-labels of samples and the pseudo-label of the anchor correspond to the same class. Due to the lack of target labels, many uncertain data are mistakenly labeled during the training process, and many low training potential data are also utilized. To address these problems, we propose Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation (LUHP). We first propose a weight to measure the category uncertainty of the target sample. We can effectively filter the samples near the decision boundary through category uncertainty thresholds which are calculated by weights. Then we propose a new loss to focus on samples with high training potential. Finally, for anchors with low category uncertainty, we propose a sample reuse strategy to make the model more robust. We demonstrate the effectiveness of LUHP by showing the results of four datasets widely used in unsupervised domain adaptation.
Class-Attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
Authors: Xuechen Zhang; Mingchen Li; Jiasi Chen; Christos Thrampoulidis; Samet Oymak
Venue: AAAI 2024 (main)

Abstract: Modern classification problems exhibit heterogeneities across individual classes: Each class may have unique attributes, such as sample size, label quality, or predictability (easy vs difficult), and variable importance at test-time. Without care, these heterogeneities impede the learning process, most notably, when optimizing fairness objectives. Confirming this, under a gaussian mixture setting, we show that the optimal SVM classifier for balanced accuracy needs to be adaptive to the class attributes. This motivates us to propose CAP: An effective and general method that generates a class-specific learning strategy (e.g.~hyperparameter) based on the attributes of that class. This way, optimization process better adapts to heterogeneities. CAP leads to substantial improvements over the naive approach of assigning separate hyperparameters to each class. We instantiate CAP for loss function design and post-hoc logit adjustment, with emphasis on label-imbalanced problems. We show that CAP is competitive with prior art and its flexibility unlocks clear benefits for fairness objectives beyond balanced accuracy. Finally, we evaluate CAP on problems with label noise as well as weighted test objectives to showcase how CAP can jointly adapt to different heterogeneities.
Learning Multi-Task Sparse Representation Based on Fisher Information
Authors: Yayu Zhang; Yuhua Qian; Guoshuai Ma; Keyin Zheng; Guoqing Liu; Qingfu Zhang
Venue: AAAI 2024 (main)

Abstract: Multi-task learning deals with multiple related tasks simultaneously by sharing knowledge. In a typical deep multi-task learning model, all tasks use the same feature space and share the latent knowledge. If the tasks are weakly correlated or some features are negatively correlated, sharing all knowledge often leads to negative knowledge transfer among. To overcome this issue, this paper proposes a Fisher sparse multi-task learning method. It can obtain a sparse sharing representation for each task. In such a way, tasks share features on a sparse subspace. Our method can ensure that the knowledge transferred among tasks is beneficial. Specifically, we first propose a sparse deep multi-task learning model, and then introduce Fisher sparse module into traditional deep multi-task learning to learn the sparse variables of task. By alternately updating the neural network parameters and sparse variables, a sparse sharing representation can be learned for each task. In addition, in order to reduce the computational overhead, an heuristic method is used to estimate the Fisher information of neural network parameters. Experimental results show that, comparing with other methods, our proposed method can improve the performance for all tasks, and has high sparsity in multi-task learning.
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
Authors: Yinmin Zhang; Jie Liu; Chuming Li; Yazhe Niu; Yaodong Yang; Yu Liu; Wanli Ouyang
Venue: AAAI 2024 (main)

Abstract: Offline-to-online Reinforcement Learning (O2O RL) aims to improve the performance of offline pretrained policy using only a few online samples. Built on offline RL algorithms, most O2O methods focus on the balance between RL objective and pessimism, or the utilization of offline and online samples. In this paper, from a novel perspective, we systematically study the challenges that remain in O2O RL and identify that the reason behind the slow improvement of the performance and the instability of online finetuning lies in the inaccurate Q-value estimation inherited from offline pretraining. Specifically, we demonstrate that the estimation bias and the inaccurate rank of Q-value cause a misleading signal for the policy update, making the standard offline RL algorithms, such as CQL and TD3-BC, ineffective in the online finetuning. Based on this observation, we address the problem of Q-value estimation by two techniques: (1) perturbed value update and (2) increased frequency of Q-value updates. The first technique smooths out biased Q-value estimation with sharp peaks, preventing early-stage policy exploitation of sub-optimal actions. The second one alleviates the estimation bias inherited from offline pretraining by accelerating learning. Extensive experiments on the MuJoco and Adroit environments demonstrate that the proposed method, named SO2, significantly alleviates Q-value estimation issues, and consistently improves the performance against the state-of-the-art methods by up to 83.1%.
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
Authors: Yixuan Zhang; Boyu Li; Zenan Ling; Feng Zhou
Venue: AAAI 2024 (main)

Abstract: Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and cause classifiers to inherit these biases. In this paper, we demonstrate that despite only having access to the biased labels, it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning. In the context of confident learning, low self-confidence usually indicates potential label errors; however, this is not always the case. Instances, particularly those from underrepresented groups, might exhibit low confidence scores for reasons other than labeling errors. To address this limitation, our approach employs truncation of the confidence score and extends the confidence interval of the probabilistic threshold. Additionally, we incorporate with co-teaching paradigm for providing a more robust and reliable selection of fair instances and effectively mitigating the adverse effects of biased labels. Through extensive experimentation and evaluation of various datasets, we demonstrate the efficacy of our approach in promoting fairness and reducing the impact of label bias in machine learning models.
Enhancing Representation of Spiking Neural Networks via Similarity-Sensitive Contrastive Learning
Authors: Yuhan Zhang; Xiaode Liu; Yuanpei Chen; Weihang Peng; Yufei Guo; Xuhui Huang; Zhe Ma
Venue: AAAI 2024 (main)

Abstract: Spiking neural networks (SNNs) have attracted intensive attention as a promising energy-efficient alternative to conventional artificial neural networks (ANNs) recently, which could transmit information in form of binary spikes rather than continuous activations thus the  multiplication of activation and weight could be replaced by addition to save energy. However, the binary spike representation form will sacrifice the expression performance of SNNs and lead to accuracy degradation compared with ANNs. Considering improving feature representation is beneficial to training an accurate SNN model, this paper focuses on enhancing the feature representation of the SNN. To this end, we establish a similarity-sensitive contrastive learning framework, where SNN could capture significantly more information from its ANN counterpart to improve representation by Mutual Information (MI) maximization with layer-wise sensitivity to similarity.  In specific, it enriches the SNN’s feature representation by pulling the positive pairs of SNN's and ANN's feature representation of each layer from the same input samples closer together while pushing the negative pairs from different samples further apart. Experimental results show that our method consistently outperforms the current state-of-the-art algorithms on both popular non-spiking static and neuromorphic datasets.
Cached Transformers: Improving Transformers with Differentiable Memory Cachde
Authors: Zhaoyang Zhang; Wenqi Shao; Yixiao Ge; Xiaogang Wang; Jinwei Gu; Ping Luo
Venue: AAAI 2024 (main)

Abstract: This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens. GRC attention enables attending to both past and current tokens, increasing the receptive field of attention and allowing for exploring long-range dependencies. By utilizing a recurrent gating unit to continuously update the cache, our model achieves significant advancements in \textbf{six} language and vision tasks, including language modeling, machine translation, ListOPs, image classification, object detection, and instance segmentation. Furthermore, our approach surpasses previous memory-based techniques in tasks such as language modeling and displays the ability to be applied to a broader range of situations.
An Implicit Trust Region Approach to Behavior Regularized Offline Reinforcement Learning
Authors: Zhe Zhang; Xiaoyang Tan
Venue: AAAI 2024 (main)

Abstract: We revisit behavior regularization, a popular approach to mitigate the extrapolation error in offline reinforcement learning (RL), showing that current behavior regularization may suffer from unstable learning and hinder policy improvement. Motivated by this, a novel reward shaping-based behavior regularization method is proposed, where the log-probability ratio between the learned policy and the behavior policy is monitored during learning. We show that this is equivalent to an implicit but computationally lightweight trust region mechanism, which is beneficial to mitigate the influence of estimation errors of the value function, leading to more stable performance improvement. Empirical results on the popular D4RL benchmark verify the effectiveness of the presented method with promising performance compared with some state-of-the-art offline RL algorithms.
Towards a Theoretical Understanding of Why Local Search Works for Clustering with Fair-Center Representation
Authors: Zhen Zhang; Junfeng Yang; Limei Liu; Xuesong Xu; Guozhen Rong; Qilong Feng
Venue: AAAI 2024 (main)

Abstract: The representative k-median problem generalizes the classical clustering formulations in that it partitions the data points into several disjoint demographic groups and poses a lower-bound constraint on the number of opened facilities from each group, such that all the groups are fairly represented by the opened facilities. Due to its simplicity, the local-search heuristic that optimizes an initial solution by iteratively swapping at most a constant number of closed facilities for the same number of opened ones (denoted by the O(1)-swap heuristic) has been frequently used in the representative k-median problem. Unfortunately, despite its good performance exhibited in experiments, whether the O(1)-swap heuristic has provable approximation guarantees for the case where the number of groups is more than 2 remains an open question for a long time. As an answer to this question, we show that the O(1)-swap heuristic
(1) is guaranteed to yield a constant-factor approximation solution if the number of groups is a constant, and
(2) has an unbounded approximation ratio otherwise.
Our main technical contribution is a new approach for theoretically analyzing local-search heuristics, which derives the approximation ratio of the O(1)-swap heuristic via linearly combining the increased clustering costs induced by a set of  hierarchically organized swaps.
Symmetric Self-Paced Learning for Domain Generalization
Authors: Di Zhao; Yun Sing Koh; Gillian Dobbie; Hongsheng Hu; Philippe Fournier-Viger
Venue: AAAI 2024 (main)

Abstract: Deep learning methods often suffer performance degradation due to domain shift, where discrepancies exist between training and testing data distributions.
Domain generalization mitigates this problem by leveraging information from multiple source domains to enhance model generalization capabilities for unseen domains.
However, existing domain generalization methods typically present examples to the model in a random manner, overlooking the potential benefits of structured data presentation.
To bridge this gap, we propose a novel learning strategy, Symmetric Self-Paced Learning (SSPL), for domain generalization.
SSPL consists of a Symmetric Self-Paced training scheduler and a Gradient-based Difficulty Measure (GDM).
Specifically, the proposed training scheduler initially focuses on easy examples, gradually shifting emphasis to harder examples as training progresses.
GDM dynamically evaluates example difficulty through the gradient magnitude with respect to the example itself.
Experiments across five popular benchmark datasets demonstrate the effectiveness of the proposed learning strategy.
Dynamic Reactive Spiking Graph Neural Network
Authors: Han Zhao; Xu Yang; Cheng Deng; Junchi Yan
Venue: AAAI 2024 (main)

Abstract: Spiking Graph Neural Networks are emerging tools for analyzing graph data along with low energy consumption and certain biological fidelity. Existing methods directly integrate same-reactive spiking neurons into graph neural networks for processing propagated graphs. However, such same-reactive neurons are not biological-functionality enough compared to the brain's dynamic-reactive ones, limiting the model's expression. Meanwhile, insufficient long-range neighbor information can be excavated with the few-step propagated graph, restricting discrimination of graph spiking embeddings. Inspired by the dynamic cognition in the brain, we propose a Dynamic Reactive Spiking Graph Neural Network that can enhance model's expressive ability in higher biological fidelity. Specifically, we design dynamic reactive spiking neurons to process spiking graph inputs, which have unique optimizable thresholds to spontaneously explore dynamic reactive states between neurons. Moreover, discriminative graph positional spikes are learned and integrated adaptively into spiking outputs through our neurons, thereby exploring long-range neighbors more thoroughly. Finally, with the dynamic reactive mechanism and learnable positional integration, we can obtain a powerful and highly bio-fidelity model with low energy consumption. Experiments on various domain-related datasets can demonstrate the effectiveness of our model. Our code is available at https://github.com/hzhao98/DRSGNN.
Learning Visual Abstract Reasoning through Dual-Stream Networks
Authors: Kai Zhao; Chang Xu; Bailu Si
Venue: AAAI 2024 (main)

Abstract: Visual abstract reasoning tasks present challenges for deep neural networks, exposing limitations in their capabilities. In this work, we present a neural network model that addresses the challenges posed by Raven’s Progressive Matrices (RPM). Inspired by the two-stream hypothesis of visual processing, we introduce the Dual-stream Reasoning Network (DRNet), which utilizes two parallel branches to capture image features. On top of the two streams, a reasoning module first learns to merge the high-level features of the same image. Then, it employs a rule extractor to handle combinations involving the eight context images and each candidate image, extracting discrete abstract rules and utilizing an multilayer perceptron (MLP) to make predictions. Empirical results demonstrate that the proposed DRNet achieves state-of-the-art average performance across multiple RPM benchmarks. Furthermore, DRNet demonstrates robust generalization capabilities, even extending to various out-of-distribution scenarios. The dual streams within DRNet serve distinct functions by addressing local or spatial information. They are then integrated into the reasoning module, leveraging abstract rules to facilitate the execution of visual reasoning tasks. These findings indicate that the dual-stream architecture could play a crucial role in visual abstract reasoning.