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Article ; Online: Hamiltonian-Driven Adaptive Dynamic Programming With Efficient Experience Replay.

Yang, Yongliang / Pan, Yongping / Xu, Cheng-Zhong / Wunsch, Donald C

IEEE transactions on neural networks and learning systems

2024 Volume 35, Issue 3, Page(s) 3278–3290

Abstract: This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for ... ...

Abstract	This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton-Jacobi-Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms of the convergence of the adaptive critic design and the stability of the closed-loop systems, where parameter convergence can be achieved under a weakened excitation condition. Simulation studies are investigated to verify the efficacy of the presented design scheme.
Language	English
Publishing date	2024-02-29
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3213566
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: GSB: Group superposition binarization for vision transformer with limited training samples.

Gao, Tian / Xu, Cheng-Zhong / Zhang, Le / Kong, Hui

Neural networks : the official journal of the International Neural Network Society

2024 Volume 172, Page(s) 106133

Abstract: Vision Transformer (ViT) has performed remarkably in various computer vision tasks. Nonetheless, affected by the massive amount of parameters, ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. In ... ...

Abstract	Vision Transformer (ViT) has performed remarkably in various computer vision tasks. Nonetheless, affected by the massive amount of parameters, ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. In addition, ViT generally demands heavy computing resources, which limit its deployment on resource-constrained devices. As a type of model-compression method, model binarization is potentially a good choice to solve the above problems. Compared with the full-precision one, the model with the binarization method replaces complex tensor multiplication with simple bit-wise binary operations and represents full-precision model parameters and activations with only 1-bit ones, which potentially solves the problem of model size and computational complexity, respectively. In this paper, we investigate a binarized ViT model. Empirically, we observe that the existing binarization technology designed for Convolutional Neural Networks (CNN) cannot migrate well to a ViT's binarization task. We also find that the decline of the accuracy of the binary ViT model is mainly due to the information loss of the Attention module and the Value vector. Therefore, we propose a novel model binarization technique, called Group Superposition Binarization (GSB), to deal with these issues. Furthermore, in order to further improve the performance of the binarization model, we have investigated the gradient calculation procedure in the binarization process and derived more proper gradient calculation equations for GSB to reduce the influence of gradient mismatch. Then, the knowledge distillation technique is introduced to alleviate the performance degradation caused by model binarization. Analytically, model binarization can limit the parameter's search space during parameter updates while training a model. Therefore, the binarization process can actually play an implicit regularization role and help solve the problem of overfitting in the case of insufficient training data. Experiments on three datasets with limited numbers of training samples demonstrate that the proposed GSB model achieves state-of-the-art performance among the binary quantization schemes and exceeds its full-precision counterpart on some indicators. Code and models are available at: https://github.com/IMRL/GSB-Vision-Transformer.
MeSH term(s)	Data Compression ; Knowledge ; Neural Networks, Computer
Language	English
Publishing date	2024-01-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2024.106133
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Article ; Online: Model-Free λ-Policy Iteration for Discrete-Time Linear Quadratic Regulation.

Yang, Yongliang / Kiumarsi, Bahare / Modares, Hamidreza / Xu, Chengzhong

IEEE transactions on neural networks and learning systems

2023 Volume 34, Issue 2, Page(s) 635–649

Abstract: This article presents a model-free λ -policy iteration ( λ -PI) for the discrete-time linear quadratic regulation (LQR) problem. To solve the algebraic Riccati equation arising from solving the LQR in an iterative manner, we define two novel matrix ... ...

Abstract	This article presents a model-free λ -policy iteration ( λ -PI) for the discrete-time linear quadratic regulation (LQR) problem. To solve the algebraic Riccati equation arising from solving the LQR in an iterative manner, we define two novel matrix operators, named the weighted Bellman operator and the composite Bellman operator. Then, the λ -PI algorithm is first designed as a recursion with the weighted Bellman operator, and its equivalent formulation as a fixed-point iteration with the composite Bellman operator is shown. The contraction and monotonic properties of the composite Bellman operator guarantee the convergence of the λ -PI algorithm. In contrast to the PI algorithm, the λ -PI does not require an admissible initial policy, and the convergence rate outperforms the value iteration (VI) algorithm. Model-free extension of the λ -PI algorithm is developed using the off-policy reinforcement learning technique. It is also shown that the off-policy variants of the λ -PI algorithm are robust against the probing noise. Finally, simulation examples are conducted to validate the efficacy of the λ -PI algorithm.
Language	English
Publishing date	2023-02-03
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2021.3098985
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Article ; Online: LAFIT: Efficient and Reliable Evaluation of Adversarial Defenses With Latent Features.

Yu, Yunrui / Gao, Xitong / Xu, Cheng-Zhong

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 46, Issue 1, Page(s) 354–369

Abstract: Deep convolutional neural networks (CNNs) can be easily tricked to give incorrect outputs by adding tiny perturbations to the input that are imperceptible to humans. This makes them susceptible to adversarial attacks, and poses significant security risks ...

Abstract	Deep convolutional neural networks (CNNs) can be easily tricked to give incorrect outputs by adding tiny perturbations to the input that are imperceptible to humans. This makes them susceptible to adversarial attacks, and poses significant security risks to deep learning systems, and presents a great challenge in making CNNs robust against such attacks. An influx of defense strategies have thus been proposed to improve the robustness of CNNs. Current attack methods, however, may fail to accurately or efficiently evaluate the robustness of defending models. In this paper, we thus propose a unified l
Language	English
Publishing date	2023-12-05
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2023.3323698
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Night-Rider

Gao, Tianxiao / Zhao, Mingle / Xu, Chengzhong / Kong, Hui

Nocturnal Vision-aided Localization in Streetlight Maps Using Invariant Extended Kalman Filtering

2024

Abstract: Vision-aided localization for low-cost mobile robots in diverse environments has attracted widespread attention recently. Although many current systems are applicable in daytime environments, nocturnal visual localization is still an open problem owing ... ...

Abstract	Vision-aided localization for low-cost mobile robots in diverse environments has attracted widespread attention recently. Although many current systems are applicable in daytime environments, nocturnal visual localization is still an open problem owing to the lack of stable visual information. An insight from most nocturnal scenes is that the static and bright streetlights are reliable visual information for localization. Hence we propose a nocturnal vision-aided localization system in streetlight maps with a novel data association and matching scheme using object detection methods. We leverage the Invariant Extended Kalman Filter (InEKF) to fuse IMU, odometer, and camera measurements for consistent state estimation at night. Furthermore, a tracking recovery module is also designed for tracking failures. Experiments on multiple real nighttime scenes validate that the system can achieve remarkably accurate and robust localization in nocturnal environments.
Keywords	Computer Science - Robotics
Subject code	629
Publishing date	2024-01-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Distributional Perspective on Multiagent Cooperation With Deep Reinforcement Learning.

Huang, Liwei / Fu, Mingsheng / Rao, Ananya / Irissappane, Athirai A / Zhang, Jie / Xu, Chengzhong

IEEE transactions on neural networks and learning systems

2024 Volume 35, Issue 3, Page(s) 4246–4259

Abstract: Among various value decomposition-based multiagent reinforcement learning (MARL) algorithms, the overall performance of the multiagent system is represented by a scalar global Q value and optimized by minimizing the temporal difference (TD) error with ... ...

Abstract	Among various value decomposition-based multiagent reinforcement learning (MARL) algorithms, the overall performance of the multiagent system is represented by a scalar global Q value and optimized by minimizing the temporal difference (TD) error with respect to that global Q value. However, the global Q value cannot accurately model the distributed dynamics of the multiagent system, since it is only a simplified representation for different individual Q values of agents. To explicitly consider the correlations between different cooperative agents, in this article, we propose a distributional framework and construct a practical model called distributional multiagent cooperation (DMAC) from a novel distributional perspective. Specifically, in DMAC, we view the individual Q value for the executed action of a random agent as a value distribution, whose expectation can further represent the overall performance. Then, we employ distributional RL to minimize the difference between the estimated distribution and its target for the optimization. The advantage of DMAC is that the distributed dynamics of agents can be explicitly modeled, and this results in better performance. To verify the effectiveness of DMAC, we conduct extensive experiments under nine different scenarios of the StarCraft Multiagent Challenge (SMAC). Experimental results show that the DMAC can significantly outperform the baselines with respect to the average median test win rate.
Language	English
Publishing date	2024-02-29
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3202097
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Secure State Estimation for Artificial Neural Networks With Unknown-But-Bounded Noises: A Homomorphic Encryption Scheme.

Zhu, Kaiqun / Wang, Zidong / Ding, Derui / Dong, Hongli / Xu, Cheng-Zhong

IEEE transactions on neural networks and learning systems

2024 Volume PP

Abstract: This article is concerned with the secure state estimation problem for artificial neural networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote estimator are connected via open and bandwidth-limited communication networks. ... ...

Abstract	This article is concerned with the secure state estimation problem for artificial neural networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote estimator are connected via open and bandwidth-limited communication networks. Using the encoding-decoding mechanism (EDM) and the Paillier encryption technique, a novel homomorphic encryption scheme (HES) is introduced, which aims to ensure the secure transmission of measurement information within communication networks that are constrained by bandwidth. Under this encoding-decoding-based HES, the data being transmitted can be encrypted into ciphertexts comprising finite bits. The emphasis of this research is placed on the development of a secure set-membership state estimation algorithm, which allows for the computation of estimates using encrypted data without the need for decryption, thereby ensuring data security throughout the entire estimation process. Taking into account the unknown-but-bounded noises, the underlying ANN, and the adopted HES, sufficient conditions are determined for the existence of the desired ellipsoidal set. The related secure state estimator gains are then derived by addressing optimization problems using the Lagrange multiplier method. Lastly, an example is presented to verify the effectiveness of the proposed secure state estimation approach.
Language	English
Publishing date	2024-04-24
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2024.3389873
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Asymmetric Convolution: An Efficient and Generalized Method to Fuse Feature Maps in Multiple Vision Tasks.

Han, Wencheng / Dong, Xingping / Zhang, Yiyuan / Crandall, David / Xu, Cheng-Zhong / Shen, Jianbing

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: Fusing features from different sources is a critical aspect of many computer vision tasks. Existing approaches can be roughly categorized as parameter-free or learnable operations. However, parameter-free modules are limited in their ability to benefit ... ...

Abstract	Fusing features from different sources is a critical aspect of many computer vision tasks. Existing approaches can be roughly categorized as parameter-free or learnable operations. However, parameter-free modules are limited in their ability to benefit from offline learning, leading to poor performance in some challenging situations. Learnable fusing methods are often space-consuming and timeconsuming, particularly when fusing features with different shapes. To address these shortcomings, we conducted an in-depth analysis of the limitations associated with both fusion methods. Based on our findings, we propose a generalized module named Asymmetric Convolution Module (ACM). This module can learn to encode effective priors during offline training and efficiently fuse feature maps with different shapes in specific tasks. Specifically, we propose a mathematically equivalent method for replacing costly convolutions on concatenated features. This method can be widely applied to fuse feature maps across different shapes. Furthermore, distinguished from parameter-free operations that can only fuse two features of the same type, our ACM is general, flexible, and can fuse multiple features of different types. To demonstrate the generality and efficiency of ACM, we integrate it into several state-of-the-art models on three representative vision tasks: visual object tracking, referring video object segmentation, and monocular 3D object detection. Extensive experimental results on three tasks and several datasets demonstrate that our new module can bring significant improvements and noteworthy efficiency.
Language	English
Publishing date	2024-05-14
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3400873
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Federated Noisy Client Learning.

Tam, Kahou / Li, Li / Han, Bo / Xu, Chengzhong / Fu, Huazhu

IEEE transactions on neural networks and learning systems

2023 Volume PP

Abstract: Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized to preserve data privacy. However, standard FL methods ignore the noisy client issue, which may harm ... ...

Abstract	Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized to preserve data privacy. However, standard FL methods ignore the noisy client issue, which may harm the overall performance of the shared model. We first investigate the critical issue caused by noisy clients in FL and quantify the negative impact of the noisy clients in terms of the representations learned by different layers. We have the following two key observations: 1) the noisy clients can severely impact the convergence and performance of the global model in FL and 2) the noisy clients can induce greater bias in the deeper layers than the former layers of the global model. Based on the above observations, we propose federated noisy client learning (Fed-NCL), a framework that conducts robust FL with noisy clients. Specifically, Fed-NCL first identifies the noisy clients through well estimating the data quality and model divergence. Then robust layerwise aggregation is proposed to adaptively aggregate the local models of each client to deal with the data heterogeneity caused by the noisy clients. We further perform label correction on the noisy clients to improve the generalization of the global model. Experimental results on various datasets demonstrate that our algorithm boosts the performances of different state-of-the-art systems with noisy clients. Our code is available at https://github.com/TKH666/Fed-NCL.
Language	English
Publishing date	2023-12-01
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2023.3336050
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Efficient and robust estimation of single-vehicle crash severity: A mixed logit model with heterogeneity in means and variances.

Li, Zhenning / Wang, Chengyue / Liao, Haicheng / Li, Guofa / Xu, Chengzhong

Accident; analysis and prevention

2023 Volume 196, Page(s) 107446

Abstract: This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a ... ...

Abstract	This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a comprehensive understanding of the complexities surrounding crash severity. The analysis is grounded in a dataset of 39,788 crash records from the UK's STATS19 database, which includes variables such as road type, speed limits, and lighting conditions. A comparative evaluation of estimation methods, including pseudo-random, Halton, and scrambled and randomized Halton sequences, demonstrates the superior performance of the latter. Specifically, our estimation approach excels in goodness-of-fit, as measured by ρ
MeSH term(s)	Humans ; Logistic Models ; Accidents, Traffic/prevention & control ; Lighting ; Databases, Factual ; Wounds and Injuries
Language	English
Publishing date	2023-12-28
Publishing country	England
Document type	Journal Article
ZDB-ID	210223-7
ISSN	1879-2057 ; 0001-4575
ISSN (online)	1879-2057
ISSN	0001-4575
DOI	10.1016/j.aap.2023.107446
Database	MEDical Literature Analysis and Retrieval System OnLINE

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