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Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information

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arXiv 2024年

作者： Xia, Runze Yin, Congchi Li, Piji 1 College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China 2 MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

The human visual system is capable of processing continuous streams of visual information, but how the brain encodes and retrieves recent visual memories during continuous visual processing remains unexplored. This study investigates the capacity of working memory to retain past information under continuous visual stimuli. And then we propose a new task Memory Disentangling, which aims to extract and decode past information from fMRI signals. To address the issue of interference from past memory information, we design a disentangled contrastive learning method inspired by the phenomenon of proactive interference. This method separates the information between adjacent fMRI signals into current and past components and decodes them into image descriptions. Experimental results demonstrate that this method effectively disentangles the information within fMRI signals. This research could advance brain-computer interfaces and mitigate the problem of low temporal resolution in fMRI. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Optimal Hessian/Jacobian-free nonconvex-PL bilevel optimization 24

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Optimal Hessian/Jacobian-free nonconvex-PL bilevel optimizat...

Proceedings of the 41st International Conference on machine Learning

作者： Feihu Huang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China and MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China.

Bilevel optimization is widely applied in many machine learning tasks such as hyper-parameter learning, meta learning and reinforcement learning. Although many algorithms recently have been developed to solve the bilevel optimization problems, they generally rely on the (strongly) convex lower-level problems. More recently, some methods have been proposed to solve the nonconvex-PL bilevel optimization problems, where their upper-level problems are possibly nonconvex, and their lower-level problems are also possibly nonconvex while satisfying Polyak-Łojasiewicz (PL) condition. However, these methods still have a high convergence complexity or a high computation complexity such as requiring compute expensive Hessian/Jacobian matrices and its inverses. In the paper, thus, we propose an efficient Hessian/Jacobian-free method (i.e., HJFBiO) with the optimal convergence complexity to solve the nonconvex-PL bilevel problems. Theoretically, under some mild conditions, we prove that our HJFBiO method obtains an optimal convergence rate of O( 1/T ), where T denotes the number of iterations, and has an optimal gradient complexity of O(ε-1) in finding an ε-stationary solution. We conduct some numerical experiments on the bilevel PL game and hyper-representation learning task to demonstrate efficiency of our proposed method.

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A Multi-Layer Random Walk Method for Local Dynamic Community Detection in Brain Functional Network

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A Multi-Layer Random Walk Method for Local Dynamic Community...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wen, Xuyun Zhang, Daoqiang Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu Nanjing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of Education Shanghai China

ISBN: (纸本)9781665401265

Detecting the time-varying community structure of brain functional network is very important to reveal dynamic properties of the human brain. Although several community detection methods have been proposed, they are limited in real application due to their poor performance in large dynamic network and difficulty in parameter setting without prior knowledge. To address these problems, this paper proposes a novel dynamic community detection method for the brain network based on random walk, named as ML-RW. This method uses local community discovery instead of global community detection to improve its ability to deal with large dynamic networks. Specifically, ML-RW first selects a query brain region and sends out multiple random walkers starting from this region to explore local community structures of all networks in dynamic network simultaneously. It updates the visiting probability vector of each walker by aggregating the transition probabilities from itself and two temporally adjacent networks. Since the influence strength from one network to another is adaptively tuned according to the relevance of visiting histories of two networks, ML-RW could guarantee the temporal smoothness of the detected modular structure without introducing the hyper-parameter and thus avoids the problem of parameter setting in existing methods. Experiments on two public real neuroimaging datasets demonstrate that our proposed method has more potential to capture subtle community variations in the brain region, stronger ability to discover biomarkers for brain diseases, and higher test-retest reliability than the conventional method. © 2021 IEEE.

关键词： Neuroimaging

Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks

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arXiv 2022年

作者： Zhang, Jiaqiang Wang, Senzhang Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Central South University China

Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models. Copyright © 2022, The Authors. All rights reserved.

关键词： Anomaly detection

Recovering from out-of-sample states via inverse dynamics in offline reinforcement learning 23

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Recovering from out-of-sample states via inverse dynamics in...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Ke Jiang Jia-yu Yao Xiaoyang Tan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics and MIIT Key Laboratory of Pattern Analysis and Machine Intelligence School of Electronic and Computer Engineering Peking University

We deal with the state distributional shift problem commonly encountered in offline reinforcement learning during test, where the agent tends to take unreliable actions at out-of-sample (unseen) states. Our idea is to encourage the agent to follow the so called state recovery principle when taking actions, i.e., besides long-term return, the immediate consequences of the current action should also be taken into account and those capable of recovering the state distribution of the behavior policy are preferred. For this purpose, an inverse dynamics model is learned and employed to guide the state recovery behavior of the new policy. Theoretically, we show that the proposed method helps aligning the transited state distribution of the new policy with the offline dataset at out-of-sample states, without the need of explicitly predicting the transited state distribution, which is usually difficult in high-dimensional and complicated environments. The effectiveness and feasibility of the proposed method is demonstrated with the state-of-the-art performance on the general offline RL benchmarks.

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Convex Subspace Clustering by Adaptive Block Diagonal Representation

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arXiv 2020年

作者： Lin, Yunxia Chen, Songcan The College of Computer Science and Technology College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China

Subspace clustering is a class of extensively studied clustering methods where the spectral-type approaches are its important subclass. Its key first step is to desire learning a representation coefficient matrix with block diagonal structure. To realize this step, many methods were successively proposed by imposing different structure priors on the coefficient matrix. These impositions can be roughly divided into two categories, i.e., indirect and direct. The former introduces the priors such as sparsity and low rankness to indirectly or implicitly learn the block diagonal structure. However, the desired block diagonalty cannot necessarily be guaranteed for noisy data. While the latter directly or explicitly imposes the block diagonal structure prior such as block diagonal representation (BDR) to ensure so-desired block diagonalty even if the data is noisy but at the expense of losing the convexity that the former's objective possesses. For compensating their respective shortcomings, in this article, we follow the direct line to propose adaptive BDR (ABDR) which explicitly pursues block diagonalty without sacrificing the convexity of the indirect one. Specifically, inspired by Convex BiClustering, ABDR coercively fuses both columns and rows of the coefficient matrix via a specially designed convex regularizer, thus naturally enjoying their merits and adaptively obtaining the number of blocks. Finally, experimental results on synthetic and real benchmarks demonstrate the superiority of ABDR to the state-of-the-arts (SOTAs). Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding

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arXiv 2024年

作者： Yuan, Fan Qin, Chi Xu, Xiaogang Li, Piji College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China The Chinese University of Hong Kong Hong Kong

Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. However, these models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images. Many existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics. To address this issue, we propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD). This framework incorporates hallucination feedback at both object and sentence semantic levels. Remarkably, even with a marginal degree of training, this approach can alleviate over 15% of hallucination. Simultaneously, HELPD penalizes the output logits according to the image attention window to avoid being overly affected by generated text. HELPD can be seamlessly integrated with any LVLMs. Our experiments demonstrate that the proposed framework yields favorable results across multiple hallucination benchmarks. It effectively mitigates hallucination for different LVLMs and concurrently improves their text generation quality. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Nlkd: Using Coarse Annotations For Semantic Segmentation Based on Knowledge Distillation

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Nlkd: Using Coarse Annotations For Semantic Segmentation Bas...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dong Liang Yun Du Han Sun Liyan Zhang Ningzhong Liu Mingqiang Wei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization

ISBN: (纸本)9781728176055;9781728176062

Modern supervised learning relies on a large amount of training data, yet there are many noisy annotations in real datasets. For semantic segmentation tasks, pixel-level annotation noise is typically located at the edge of an object, while pixels within objects are fine-annotated. We argue the coarse annotations can provide instructive supervised information to guide model training rather than be discarded. This paper proposes a noise learning framework based on knowledge distillation NLKD, to improve segmentation performance on unclean data. It utilizes a teacher network to guide the student network that constitutes the knowledge distillation process. The teacher and student generate the pseudo-labels and jointly evaluate the quality of annotations to generate weights for each sample. Experiments demonstrate the effectiveness of NLKD, and we observe better performance with boundary-aware teacher networks and evaluation metrics. Furthermore, the proposed approach is model-independent and easy to implement, appropriate for integration with other tasks and models.

关键词： Training Annotations Semantics Supervised learning Training data Signal processing Noise robustness

Trust region-guided proximal policy optimization

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arXiv 2019年

作者： Wang, Yuhui He, Hao Tan, Xiaoyang Gan, Yaozhong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization

Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, as a model-free RL method, the success of PPO relies heavily on the effectiveness of its exploratory policy search. In this paper, we give an in-depth analysis on the exploration behavior of PPO, and show that PPO is prone to suffer from the risk of lack of exploration especially under the case of bad initialization, which may lead to the failure of training or being trapped in bad local optima. To address these issues, we proposed a novel policy optimization method, named Trust Region-Guided PPO (TRGPPO), which adaptively adjusts the clipping range within the trust region. We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well. Extensive experiments verify the advantage of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning