1 code implementation • 3 Feb 2024 • Xinyu Peng, Ziyang Zheng, Wenrui Dai, Nuoqian Xiao, Chenglin Li, Junni Zou, Hongkai Xiong
In this paper, we propose the first unified interpretation for existing zero-shot methods from the perspective of approximating the conditional posterior mean for the reverse diffusion process of conditional sampling.
no code implementations • 17 Dec 2023 • Chenglin Li, Qianglong Chen, Liangyue Li, Caiyu Wang, Yicheng Li, Zulong Chen, Yin Zhang
While large language models (LLMs) have demonstrated exceptional performance in recent natural language processing (NLP) tasks, their deployment poses substantial challenges due to high computational and memory demands in real-world applications.
no code implementations • 16 Dec 2023 • Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou, Dapeng Wu, Hongkai Xiong
A gene-level GNN is established to adaptively learn gene-gene interactions and cell representations via the self-attention mechanism, and a cell-level GNN builds on the cell-cell graph that is constructed from the cell representations generated by the gene-level GNN.
1 code implementation • NeurIPS 2023 • Jin Li, Yaoming Wang, Xiaopeng Zhang, Bowen Shi, Dongsheng Jiang, Chenglin Li, Wenrui Dai, Hongkai Xiong, Qi Tian
Specifically, at the intermediate layer of the ViT, we utilize a spatial-aware density-based clustering algorithm to select representative tokens from the token sequence.
1 code implementation • 25 Oct 2023 • Han Li, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years.
1 code implementation • 15 Sep 2023 • Liyao Jiang, Chenglin Li, Haolan Chen, Xiaodong Gao, Xinwang Zhong, Yang Qiu, Shani Ye, Di Niu
Online advertisements are important elements in e-commerce sites, social media platforms, and search engines.
no code implementations • 23 May 2023 • Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei, Chenglin Li, Beibei Kong, Lei Chen, Chengxiang Zhuo, Bo Hu, Zang Li
At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks.
no code implementations • 5 Mar 2023 • Jixiang Luo, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
In this paper, we propose a novel framework for learned lossless compression of JPEG images that achieves end-to-end optimized prediction of the distribution of decoded DCT coefficients.
1 code implementation • CVPR 2023 • Yaoming Wang, Bowen Shi, Xiaopeng Zhang, Jin Li, Yuchen Liu, Wenrui Dai, Chenglin Li, Hongkai Xiong, Qi Tian
To mitigate the computational and storage demands, recent research has explored Parameter-Efficient Fine-Tuning (PEFT), which focuses on tuning a minimal number of parameters for efficient adaptation.
no code implementations • CVPR 2023 • Yuchen Liu, Yaoming Wang, Yabo Chen, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Then, we propose a novel unsupervised domain generalization approach, namely Dual Nearest Neighbors contrastive learning with strong Augmentation (DN^2A).
1 code implementation • 22 Nov 2022 • Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, XiaoHu Qie, Di Niu
CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain.
no code implementations • 6 Jun 2022 • Qi Wang, Ying Cui, Chenglin Li, Junni Zou, Hongkai Xiong
To reduce computational complexity, we first transform each to an equivalent but much simpler discrete problem with N\llL variables representing the partition of the L coordinates into N blocks, each with identical redundancy.
no code implementations • 31 May 2022 • Mingxing Xu, Chenglin Li, Wenrui Dai, Siheng Chen, Junni Zou, Pascal Frossard, Hongkai Xiong
Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).
no code implementations • 27 Apr 2022 • Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Subsequently, this local information is aligned and propagated to the preserved nodes to alleviate information loss in graph coarsening.
1 code implementation • 25 Apr 2022 • Ziyang Zheng, Wenrui Dai, Duoduo Xue, Chenglin Li, Junni Zou, Hongkai Xiong
This framework is general to endow arbitrary DNNs for solving linear inverse problems with convergence guarantees.
no code implementations • CVPR 2022 • Yaoming Wang, Yangzhou Jiang, Jin Li, Bingbing Ni, Wenrui Dai, Chenglin Li, Hongkai Xiong, Teng Li
Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance.
1 code implementation • 19 Nov 2021 • Chenglin Li, Mingjun Zhao, Huanming Zhang, Chenyun Yu, Lei Cheng, Guoqiang Shu, Beibei Kong, Di Niu
The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved.
no code implementations • 29 Sep 2021 • Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
Graph convolutional networks have been a powerful tool in representation learning of networked data.
no code implementations • 29 Sep 2021 • Yuankun Jiang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong
In this paper, we theoretically derive a bias-free and state/environment-dependent optimal baseline for DR, and analytically show its ability to achieve further variance reduction over the standard constant and state-dependent baselines for DR. We further propose a variance reduced domain randomization (VRDR) approach for policy gradient methods, to strike a tradeoff between the variance reduction and computational complexity in practice.
no code implementations • 3 Aug 2021 • Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information.
no code implementations • 18 Jun 2021 • Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
Furthermore, each filter in the spectral domain corresponds to a message passing scheme, and diverse schemes are implemented via the filter bank.
no code implementations • 31 May 2021 • Chenglin Li, Carrie Lu Tong, Di Niu, Bei Jiang, Xiao Zuo, Lei Cheng, Jian Xiong, Jianming Yang
Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently.
1 code implementation • 31 May 2021 • Chenglin Li, Di Niu, Bei Jiang, Xiao Zuo, Jianming Yang
However, the effectiveness of federated learning for HAR is affected by the fact that each user has different activity types and even a different signal distribution for the same activity type.
no code implementations • 1 Jan 2021 • Yuankun Jiang, Chenglin Li, Junni Zou, Wenrui Dai, Hongkai Xiong
To address this, in this paper, we propose a Bayesian linear regression with informative prior (IP-BLR) operator to leverage the data-dependent prior in the learning process of randomized value function, which can leverage the statistics of training results from previous iterations.
no code implementations • ICCV 2021 • Yaoming Wang, Yuchen Liu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Existing differentiable neural architecture search approaches simply assume the architectural distribution on each edge is independent of each other, which conflicts with the intrinsic properties of architecture.
no code implementations • 1 Jan 2021 • Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
In the variational E-step, graph topology is optimized by approximating the posterior probability distribution of the latent adjacency matrix with a neural network learned from node embeddings.
no code implementations • 1 Jan 2021 • Yuankun Jiang, Chenglin Li, Junni Zou, Wenrui Dai, Hongkai Xiong
To mitigate the model discrepancy between training and target (testing) environments, domain randomization (DR) can generate plenty of environments with a sufficient diversity by randomly sampling environment parameters in simulator.
no code implementations • 7 Dec 2020 • Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Therefore, it can relieve the over-smoothing issue and learn effective node representations over graphs with homophily or heterophily.
1 code implementation • 19 Oct 2020 • Wen Fei, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
We leverage the neural tangent kernel (NTK) theory to prove that our weight mean operation whitens activations and transits network into the chaotic regime like BN layer, and consequently, leads to an enhanced convergence.
no code implementations • 19 Jun 2020 • Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, Pascal Frossard
In this paper, we propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology.
no code implementations • 13 Jun 2018 • Rui Zhu, Chenglin Li, Di Niu, Hongwen Zhang, Husam Kinawi
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches.
Cryptography and Security
no code implementations • 30 May 2018 • Chenglin Li, Keith Mills, Rui Zhu, Di Niu, Hongwen Zhang, Husam Kinawi
As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications.
Cryptography and Security