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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Xinbiao Liu, Junyu Liu, Tongliang Luo, Yong Du, Yuxuan Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University China Pritzker School of Molecular Engineering The University of Chicago United States JD Explore Academy Chicago Quantum Exchange United States Kadanoff Center for Theoretical Physics United States qBraid Co. Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China

Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for most large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leading protocol to conquer this issue. As such, the performance enhancement of QNNs becomes the core in GSP. Empirical evidence showed that QNNs with handcraft symmetric ansätze generally experience better trainability than those with asymmetric ansätze, while theoretical explanations remain vague. To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima. We uncover that the advance of symmetric ansätze attributes to their large EQNTK value with low effective dimension, which requests few parameters and quantum circuit depth to reach the over-parameterization regime permitting a benign loss landscape and fast convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme to automatically tailor a symmetric ansatz from an over-parameterized and asymmetric one to greatly improve the performance of QNNs when the explicit symmetry information of Hamiltonian is unavailable. Extensive numerical simulations are conducted to validate the analytical results of EQNTK and the effectiveness of SP. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Hamiltonians

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Robust Imagined Speech Production Using AI-Generated Content network for Patients with Language Impairments

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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Galliot: Path Merging Based Betweenness Centrality Algorithm on GPU 42

Galliot: Path Merging Based Betweenness Centrality Algorithm...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zheng, Zhigao Zhao, Chen Xie, Peichen Dum, Bo School of Computer Science Wuhan University Wuhan 430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan 430072 Chin. Institute of Artificial Intelligence Wuhan University Wuhan 430072 Chin. Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan 430072 China Hubei Luojia Laboratory Wuhan 430072 China

ISBN: (纸本)9798350334142

Betweenness centrality (BC) is widely used to measure a vertex's significance by using the frequency of a vertex appearing in the shortest path between other vertices. However, most recent algorithms in BC computation suffer from the problem of high auxiliary memory consumption. To reduce BC computing's memory consumption, we propose a path-mergingbased algorithm called Galliot to calculate the BC values on GPU, which aims to minimize the on-board memory consumption and enable the BC computation of large-scale graphs. The proposed algorithm requires O(n) space and runs in O(mn) time on unweighted graphs. We present the theoretical principle for the proposed path merging method. Moreover, we propose a locality-oriented policy to maintain and update the worklist to improve GPU data locality. In addition, we conducted extensive experiments on NVIDIA GPUs to show the performance of Galliot. The results show that Galliot can process the larger graphs, which have 11.32× more vertices and 5.67× more edges than the graphs that recent works. Moreover, Galliot can achieve up to 38.77× speedup over the existing methods. © 2023 IEEE.

关键词： Graphics processing unit

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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PP卷 PP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 Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 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. Firstly, 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. The source code will be made publicly available. ©1979-2012 IEEE.

关键词： Latent semantic analysis

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Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

arXiv

引用

arXiv 2024年

作者： Zhong, Qihuang Wang, Kang Xu, Ziyang Liu, Juhua Ding, Liang Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China School of Computer Science University of Sydney Sydney2006 Australia

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. Prior studies involve addressing the calculation errors and step-missing errors, but neglect the semantic misunderstanding errors, which is the major factor limiting the reasoning performance of LLMs. To this end, we propose a simple-yet-effective method, namely Deeply Understanding the Problems (DUP), to improve the LLMs’ math problem-solving ability by addressing semantic misunderstanding errors. The core of our method is to encourage the LLMs to deeply understand the problems and extract the key problem-solving information used for better reasoning. Extensive experiments on 10 diverse reasoning benchmarks show that our DUP method consistently outperforms the other counterparts by a large margin. More encouragingly, DUP achieves a new SOTA result on the GSM8K benchmark, with an accuracy of 97.1% under the zero-shot setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Boosting Semi-Supervised Object Detection in Remote Sensing Images with Active Teaching

arXiv

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arXiv 2024年

作者： Zhang, Boxuan Wang, Zengmao Du, Bo The School of Computer Science Wuhan University Wuhan430072 China The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence The Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

The lack of object-level annotations poses a significant challenge for object detection in remote sensing images. To address this issue, active learning and semi-supervised learning techniques have been proposed to enhance the quality and quantity of annotations. Active learning focuses on selecting the most informative samples for annotation, while semi-supervised learning leverages the knowledge from unlabeled samples. In this paper, we propose a novel active learning method to boost semi-supervised object detection for remote sensing images with a teacher-student network, called SSOD-AT. The proposed method incorporates a RoI Comparison module (RoICM) to generate high-confidence pseudo-labels for Regions of Interest (RoIs). Meanwhile, the RoICM is utilized to identify the top-K uncertain images. To reduce redundancy in the top-K uncertain images for human labeling, a diversity criterion is introduced based on object-level prototypes of different categories using both labeled and pseudo-labeled images. Extensive experiments on DOTA and DIOR two popular datasets demonstrate that our proposed method outperforms state-of-the-art methods for object detection in remote sensing images. Compared with the best performance in the SOTA methods, the proposed method achieves 1% improvement at most cases in the whole active learning. © 2024, CC BY.

关键词： Remote sensing

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Improving Acoustic Echo Cancellation by Mixing Speech Local and Global Features with Transformer

Improving Acoustic Echo Cancellation by Mixing Speech Local ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yajie Liu Xinmeng Xu Weiping Tu Yuhong Yang Li Xiao School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

We propose MiT-Net, a novel mix-transformer neural network with a pyramid encoder operating in the time domain, for the task of acoustic echo cancellation. The MiT-Net formulates acoustic echo cancellation as a supervised speech separation problem, in which near-end speech is separated from a single microphone recording and sent to the far end, and consists of two key components. First, we apply a pyramid encoder, which adopts the coarse-to-fine structure, to extract the latent correlations between double-end signals and to fuse them in a multiscale manner. Second, we propose a mix-transformer, a combination of local and global attention in a parallel way, to leverage local and global speech information for separation. Experimental results show that the proposed method outperforms recent AEC methods in terms of objective evaluation metrics. In addition, exploring the correlation between speech local and global features by using the mix-transformer significantly improves the system performance and shows more robustness than the conventional transformer.

关键词： Echo cancellers Correlation Fuses System performance Transformers Feature extraction Acoustics

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Bi-Causal: Group Activity Recognition via Bidirectional Causality

Bi-Causal: Group Activity Recognition via Bidirectional Caus...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Youliang Zhang Wenxuan Liu Danni Xu Zhuo Zhou Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University of Technology National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current approaches in Group Activity Recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Inter-actions (HOIs). This study prioritizes the consideration of both HRs and HOIs, emphasizing their interdependence. Notably, employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight, we propose a Bidirectional-Causal GAR network. This network establishes a causality commu-nication channel while modeling relations and interactions, enabling reciprocal enhancement between human-object interactions and human relations, ensuring their mutual consistency. Additionally, an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches. Our project page: https://***/***/

关键词： computer vision Computational modeling Cause effect analysis Communication channels Activity recognition

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Multi-speaker Direction of Arrival Estimation Using Audio and Visual Modalities with Convolutional Neural network

Multi-speaker Direction of Arrival Estimation Using Audio an...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yulin Wu Ruimin Hu Xiaochen Wang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

In reality, audible and visible sound sources are closely aligned, and they can help humans locate sources exactly. To exploit the complementarity between audio and visual data in multi-speaker direction of arrival (DoA) estimation, we propose a novel network consisting of 3D convolution neural networks (3D-CNNs) and 2D-CNNs mixture networks with residual dense blocks. It has two main advantages: 1) both input audio and visual features are low-level signal representation: the real and imaginary parts of STFT coefficients for the audio feature and pixel coordinates for the visual feature, which can allow the network to learn to extract the most informative high-level features. 2) 3D-CNNs with the residual dense block are used for audio and visual feature mapping along the time and frequency axis. The following 2D-CNNs are to ensemble the high-level features along the DoA axis. Experimental results demonstrate promising SSL performance.

关键词：

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HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Quality Assessment Metric

HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Q...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuanming Zheng Jiaxuan Yao Xiangyu Deng Yuhong Yang Ruiqi Liao Weiping Tu Cedar Lin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objective metric for assessing timbre quality, spatial quality, and overall quality of binaural audio, guided by human auditory perception. Our contributions include: (1) the Multi-scale Auditory Guided Feature Extraction (MAGFE) module, incorporating gammatone frequency cepstral coefficients for better alignment with human perception; (2) Perceptual Weighted Loss (PWL), optimizing the weighting of timbre quality (TQ) and spatial quality (SQ) loss based on subjective test data; and (3) data augmentation techniques to enhance robustness by amplifying perceptual distortions. Experimental results show HAPG-SAQAM improves correlation with subjective scores by 10%, with ablation studies confirming the contributions of its components to enhanced spatial and overall audio quality.

关键词： Measurement Accuracy Spatial audio Performance gain Feature extraction Robustness Quality assessment Timbre Speech processing Optimization

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