no code implementations • 25 Nov 2024 • Chang Bi, Kailun Bai, Xing Li, Xuekui Zhang
This hybrid approach incorporates both reference and query genomic data for feature engineering, enhancing the embedding learning process, increasing the effective sample size for unsupervised techniques, and improving the transferability of the supervised model trained on reference data when applied to query datasets.
no code implementations • 12 Nov 2024 • Mingxuan Sun, Xing Li, Han Wang
When facing time-variant problems in analog computing, the desirable RNN design requires finite-time convergence and robustness with respect to various types of uncertainties, due to the time-variant nature and difficulties in implementation.
no code implementations • 21 Oct 2024 • Xinyi Zhou, Xing Li, Yingzhao Lian, Yiwen Wang, Lei Chen, Mingxuan Yuan, Jianye Hao, Guangyong Chen, Pheng Ann Heng
We explicitly train the model to learn graph conditioning with a condition loss, which enhances the diffusion model's capacity to generate graphs that are both realistic and aligned with specified properties.
no code implementations • 16 Sep 2024 • Raika Karimi, Faezeh Faez, Yingxue Zhang, Xing Li, Lei Chen, Mingxuan Yuan, Mahdi Biparva
Contemporary hardware design benefits from the abstraction provided by high-level logic gates, streamlining the implementation of logic circuits.
no code implementations • 14 Sep 2024 • Zhaoyu Chen, Xing Li, Qian Huang, Qiang Geng, Tianjin Yang, Shihao Han
In addition, we present a D-Hyperpoint KANsMixer module, which is recursively applied to nested groupings of D-Hyperpoints to learn the action discrimination information and creatively integrates Kolmogorov-Arnold Networks (KAN) to enhance spatio-temporal interaction within D-Hyperpoints.
no code implementations • 9 Sep 2024 • Faezeh Faez, Raika Karimi, Yingxue Zhang, Xing Li, Lei Chen, Mingxuan Yuan, Mahdi Biparva
On the other hand, we employ a hierarchical graph representation learning strategy to improve the model's capacity for learning expressive graph-level representations of large AIGs, surpassing traditional plain GNNs.
1 code implementation • 4 Sep 2024 • Xufeng Yao, Yiwen Wang, Xing Li, Yingzhao Lian, Ran Chen, Lei Chen, Mingxuan Yuan, Hong Xu, Bei Yu
Our comparative analysis with established compilers such as Yosys and E-graph demonstrates significant improvements, highlighting the benefits of integrating large models into the early stages of circuit design.
no code implementations • 7 Jun 2024 • Xihan Li, Xing Li, Lei Chen, Xing Zhang, Mingxuan Yuan, Jun Wang
While deep learning has achieved significant success in various domains, its application to logic circuit design has been limited due to complex constraints and strict feasibility requirement.
no code implementations • 14 Mar 2024 • Xihan Li, Xing Li, Lei Chen, Xing Zhang, Mingxuan Yuan, Jun Wang
In this study, we introduce a generative neural model, the "Circuit Transformer", which eliminates such wrong predictions and produces logic circuits strictly equivalent to given Boolean functions.
no code implementations • 5 Jan 2024 • Yang Yang, Yury Kartynnik, Yunpeng Li, Jiuqiang Tang, Xing Li, George Sung, Matthias Grundmann
We present StreamVC, a streaming voice conversion solution that preserves the content and prosody of any source speech while matching the voice timbre from any target speech.
no code implementations • 19 Oct 2023 • Yiming Wang, Qian Huang, Bin Tang, Huashan Sun, Xing Li
In addition, most approaches ignore the spatial and channel redundancy.
1 code implementation • 22 Aug 2023 • Zhihai Wang, Lei Chen, Jie Wang, Xing Li, Yinqi Bai, Xijun Li, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu
In particular, we notice that the runtime of the Resub and Mfs2 operators often dominates the overall runtime of LS optimization processes.
no code implementations • 24 Jul 2023 • Beiya Dai, Xing Li, Qunyi Xie, Yulin Li, Xiameng Qin, Chengquan Zhang, Kun Yao, Junyu Han
To produce a comprehensive evaluation of MataDoc, we propose a novel benchmark ArbDoc, mainly consisting of document images with arbitrary boundaries in four typical scenarios.
1 code implementation • 7 Apr 2023 • Yiyuan Yang, Rongshang Li, Qiquan Shi, Xijun Li, Gang Hu, Xing Li, Mingxuan Yuan
This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP).
1 code implementation • 29 Mar 2023 • Xinxin Hu, Haotian Chen, Junjie Zhang, Hongchang Chen, Shuxin Liu, Xing Li, Yahui Wang, xiangyang xue
Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors.
1 code implementation • 28 Mar 2023 • Xinxin Hu, Haotian Chen, Hongchang Chen, Shuxin Liu, Xing Li, Shibo Zhang, Yahui Wang, xiangyang xue
But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work.
no code implementations • CVPR 2023 • Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, Xing Li
Experimental results on multi-datasets show that: (1) our method can be easily plugged into the current various stereo matching models to improve generalization performance; (2) our method can reduce the significant volatility of generalization performance among different training epochs; (3) we find that the current methods prefer to choose the best results among different training epochs as generalization performance, but it is impossible to select the best performance by ground truth in practice.
no code implementations • 6 Dec 2022 • Jianchuan Chen, Wentao Yi, Tiantian Wang, Xing Li, Liqian Ma, Yangyu Fan, Huchuan Lu
The integrated features acting as the latent code are anchored to the SMPLX mesh in the canonical space.
no code implementations • 13 Oct 2022 • Xing Li, Manuel Baum, Oliver Brock
We introduce a Learning from Demonstration (LfD) approach for contact-rich manipulation tasks with articulated mechanisms.
no code implementations • 11 Jun 2022 • Jingcheng Zhou, Wei Wei, Xing Li, Bowen Pang, Zhiming Zheng
Deep learning utilizing deep neural networks (DNNs) has achieved a lot of success recently in many important areas such as computer vision, natural language processing, and recommendation systems.
1 code implementation • 16 Nov 2021 • Xing Li, Qian Huang, Zhijian Wang, Zhenjie Hou, Tianjin Yang, Zhuang Miao
Instead of capturing spatio-temporal local structures, SequentialPointNet encodes the temporal evolution of static appearances to recognize human actions.
1 code implementation • 9 Nov 2021 • Yuzhe Gao, Xing Li, Jiajian Zhang, Yu Zhou, Dian Jin, Jing Wang, Shenggao Zhu, Xiang Bai
We leverage a Siamese ComplementaryModule to fully exploit the continuity characteristic of the textinstances in the temporal dimension, which effectively alleviatesthe missed detection of the text instances, and hence ensuresthe completeness of each text trajectory.
1 code implementation • 17 Feb 2021 • Xing Li, Haichun Yang, Jiaxin He, Aadarsh Jha, Agnes B. Fogo, Lee E. Wheless, Shilin Zhao, Yuankai Huo
Reducing outcome variance is an essential task in deep learning based medical image analysis.
no code implementations • 19 Jan 2021 • Chang Li, Qian Huang, Xing Li, Qianhan Wu
We employ depth motion images (DMI) as the templates to generate the multi-scale static representation of actions.
no code implementations • 2 Jan 2021 • Xing Li, Wei Wei, Xiangnan Feng, Zhiming Zheng
Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning.
no code implementations • 31 Jul 2020 • Xing Li, Wei Wei, Xiangnan Feng, Xue Liu, Zhiming Zheng
The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction , etc.
no code implementations • 11 Jul 2020 • Sascha Rosbach, Xing Li, Simon Großjohann, Silviu Homoceanu, Stefan Roth
Furthermore, the temporal attention mechanism learns persistent interaction with other vehicles over an extended planning horizon.
no code implementations • 7 Dec 2019 • Sascha Rosbach, Vinit James, Simon Großjohann, Silviu Homoceanu, Xing Li, Stefan Roth
In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions.
3 code implementations • ACL 2019 • Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang
Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences.
no code implementations • 23 Jun 2017 • Xiaojun Chen, Lu Xu, Xing Li, Jan Egger
Patient-specific cranial implants are important and necessary in the surgery of cranial defect restoration.