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ORCIDWei Huang 0034
Person information
- affiliation: RIKEN Center for Advanced Intelligence Projec, Tokyo, Japan
- affiliation (PhD 2021): University of Technology Sydney, Australia
Other persons with the same name
- Wei Huang — disambiguation page
- Wei Huang 0001 — Google, USA (and 1 more)
- Wei Huang 0002
![[0000-0003-0586-090X]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0003-0586-090X")
— Institute of Southwestern Communication, Science and Technology on Communication Security Laboratory, Chengdu, China (and 2 more) - Wei Huang 0003 — The Ohio State University, Columbus, USA
- Wei Huang 0004 — AMD Research (and 3 more)
- Wei Huang 0005 — Virginia Tech, Blacksburg, USA
- Wei Huang 0006 — Huazhong University of Science and Technology, School of Management, Wuhan, China
- Wei Huang 0007 — Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China (and 1 more)
- Wei Huang 0008 — Tianjin University of Technology, School of Computer and Communication Engineering, China (and 2 more)
- Wei Huang 0009 — Tsinghua University, Department of Electronic Engineering / TNList, Beijing, China
- Wei Huang 0010 — Hefei University of Technology, School of Computer Science and Information Engineering, China (and 1 more)
- Wei Huang 0011 — Southwestern University of Finance and Economics, School of Finance, Chengdu, China (and 1 more)
- Wei Huang 0012 — Shanghai Jiao Tong University, Department of Electrical Engineering, Institute of Image Communication and Network Engineering, China
- Wei Huang 0013 — Nanchang University, School of Information Engineering, China (and 3 more)
- Wei Huang 0014 — Tongji University, Shanghai, China (and 2 more)
- Wei Huang 0015 — Zhejiang University of Technology, College of Computer Science and Technology, Hangzhou, China (and 1 more)
- Wei Huang 0016 — University of Electronic Science and Technology of China, Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, China
- Wei Huang 0017 — Southeast University, Intelligent Transportation System Research Center, Nanjing, China
- Wei Huang 0018 — University of Westminster, UK (and 1 more)
- Wei Huang 0019 — University of Bedfordshire, UK (and 1 more)
- Wei Huang 0020 — Hefei University of Technology, School of Mathematics, China (and 1 more)
- Wei Huang 0021 — University of Electronic Science and Technology of China, National Key Laboratory of Science and Technology, Chengdu, China
- Wei Huang 0022 — Polytechnic University of Turin, Italy
- Wei Huang 0023 — Ocean University of China, Qingdao, China (and 1 more)
- Wei Huang 0024 — Nanjing Institute of Technology, School of Computer Engineering, Nanjing, China
- Wei Huang 0025 — QIHOO 360, China (and 1 more)
- Wei Huang 0026 — University of Science and Technology of China, School of Computer Science and Technology, Hefei, China (and 1 more)
- Wei Huang 0027 — University of Toronto, Department of Electrical and Computer Engineering, Toronto, Canada (and 1 more)
- Wei Huang 0028 — Chinese Academy of Sciences, Key Laboratory of Wireless-Optical Communications, Hefei, China (and 1 more)
- Wei Huang 0029 — University of Windsor, Department of Electrical and Computer Engineering, Windsor, Canada
- Wei Huang 0030 — Beijing Institute of Technology, School of Computer Science, Beijing Laboratory of Intelligent Information Technology, China
- Wayne Huang 0001 (aka: Wayne W. Huang, Wayne Wei Huang, Wei (Wayne) Huang, Wei Huang 0031) — Southern University of Science and Technology, College of Business, Shenzhen, China (and 5 more)
- Wei Huang 0032 — Iowa State University, Department of Electrical and Computer Engineering, Ames, IA, USA
- Wei Huang 0033 — Tianjin University of Technology, School of Computer Science and Engineering, Engineering Research Center of Learning-Based Intelligent System, MOE Key Laboratory of Computer Vision and Systems, China (and 1 more)
- Wei Huang 0035 — Purple Mountain Laboratories, Nanjing, China (and 1 more)
- Wei Huang 0036 — University of Science and Technology of China, National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application, Hefei, China
- Wei Huang 0037 — Fuzhou university, China
- Wei Huang 0038 — Inner Mongolia University, Hohhot, China
- Wei Huang 0039 — University of Science and Technology Beijing, Beijing, China (and 1 more)
- Wei Huang 0040 — University of New South Wales, School of Computer Science and Engineering, Kensington, NSW, Australia
- Wei Huang 0041 — Télécom SudParis, SAMOVAR, IP-Paris, Palaiseau, France
- Wei Huang 0042 — University of Hong Kong, Department of Electrical and Electronic Engineering, Hong Kong
- Wei Huang 0043 — Hunan University, Changsha, China
- Wei Huang 0044 — Hunan University, Changsha, Hunan, China
- Wei Huang 0045 — National Geomatics Center of China, Beijing, China
- Wei Huang 0046 — Independent Researcher, Beijing, China
- Wei Huang 0047 — Chongqing Medical University First Affiliated Hospital, Chongqing, China
- Wei Huang 0048 — Sun Yat-sen University, Zhuhai, China
- Wei Huang 0049 — Peng Cheng Laboratory, Shenzhen, Guangdong, China
- Wei Huang 0050 — Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
- Wei Huang 0051 — Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
- Wei Huang 0052 — Yanshan University, Qinhuangdao, China
- Wei Huang 0053 — Shanghai Dianji University, Shanghai, China
- Wei Huang 0054 — RIKEN, Tokyo, Japan
- Wei Huang 0055 — Jilin University, Changchun, Jilin, China
- Wei Huang 0056 — Beijing Language and Culture University, Beijing, China
- Wei Huang 0057 — SPIC Chongqing Hechuan Power Generation Co., Ltd, Chongqing, China
- Wei Huang 0058 — Tianjin University of Technology, Tianjin, China
- Wei Huang 0059 — Wuhan University, Wuhan, China
- Wei Huang 0060 — ShanghaiTech University, Shanghai, China
- Wei Huang 0061 — Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
- Wei Huang 0062 — Nanjing University of Information Science and Technology, Nanjing, China
- Wei Huang 0063 — University of Electronic Science and Technology of China, Chengdu, China
- Wei Huang 0064 — Central South University, Changsha, China
- Wei Huang 0065 — ESDEMC Technology LLC., Rolla, MO, USA
- Wei Huang 0066 — City University of Hong Kong, Hong Kong, SAR, China
- Wei Huang 0067 — Beijing University of Posts and Telecommunications, China
- Wei Huang 0068 — Northwestern Polytechnical University, Xi'an, China
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2020 – today
- 2025
[j4]Li Wang, Wei Huang:
On the convergence analysis of over-parameterized variational autoencoders: a neural tangent kernel perspective. Mach. Learn. 114(1): 15 (2025)- [c34]Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki:
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons. AISTATS 2025: 2854-2862 - [c33]Wei Huang, Wuyang Chen, Zhiqiang Xu, Zhangyang Wang, Taiji Suzuki:
Exact and Rich Feature Learning Dynamics of Two-Layer Linear Networks. CPAL 2025: 1087-1111 - [c32]Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, Ye Shi:
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. CVPR 2025: 19963-19973 - [c31]Andi Han, Wei Huang, Yuan Cao, Difan Zou:
On the Feature Learning in Diffusion Models. ICLR 2025 - [c30]Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen:
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. ICLR 2025 - [c29]Xin Zheng, Wei Huang, Chuan Zhou, Ming Li, Shirui Pan:
Test-Time Graph Neural Dataset Search With Generative Projection. ICML 2025 - [c28]Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki:
Provable In-Context Vector Arithmetic via Retrieving Task Concepts. ICML 2025 - [c27]Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki:
On the Role of Label Noise in the Feature Learning Process. ICML 2025 - [c26]Hongyi Wang, Shiyuan Ren, Wei Huang, Miao Zhang, Xiang Deng, Yixin Bao, Liqiang Nie:
Understanding the Forgetting of (Replay-based) Continual Learning via Feature Learning: Angle Matters. ICML 2025 - [c25]Yue Wang, Qizhou Wang, Feng Liu, Wei Huang, Yali Du, Xiaojiang Du, Bo Han:
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs. ICML 2025 - [i36]Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou:
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? CoRR abs/2502.04725 (2025) - [i35]Yue Wang, Qizhou Wang, Feng Liu, Wei Huang, Yali Du, Xiaojiang Du, Bo Han:
GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models. CoRR abs/2503.09117 (2025) - [i34]Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki:
On the Role of Label Noise in the Feature Learning Process. CoRR abs/2505.18909 (2025) - [i33]Yilan Chen, Zhichao Wang, Wei Huang, Andi Han, Taiji Suzuki, Arya Mazumdar:
Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel. CoRR abs/2506.11357 (2025) - [i32]Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki:
Provable In-Context Vector Arithmetic via Retrieving Task Concepts. CoRR abs/2508.09820 (2025) - [i31]Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie:
Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression. CoRR abs/2509.23779 (2025) - [i30]Junsoo Oh, Wei Huang, Taiji Suzuki:
Mamba Can Learn Low-Dimensional Targets In-Context via Test-Time Feature Learning. CoRR abs/2510.12026 (2025) - [i29]Wei Huang, Andi Han, Yujin Song, Yilan Chen, Denny Wu, Difan Zou, Taiji Suzuki:
How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime? CoRR abs/2510.17526 (2025) - [i28]Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Bo Xue, Qingfu Zhang, Hau-San Wong, Taiji Suzuki:
Consistency Is Not Always Correct: Towards Understanding the Role of Exploration in Post-Training Reasoning. CoRR abs/2511.07368 (2025) - [i27]Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Hau-San Wong, Qingfu Zhang, Taiji Suzuki:
Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training. CoRR abs/2511.07372 (2025) - 2024
- [c24]Hao Wu, Yuxuan Liang, Wei Xiong, Zhengyang Zhou, Wei Huang, Shilong Wang, Kun Wang:
Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. AAAI 2024: 15906-15914 - [c23]Hongxia Li, Wei Huang, Jingya Wang, Ye Shi:
Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. CVPR 2024: 12151-12161 - [c22]Wei Huang, Ye Shi, Zhongyi Cai, Taiji Suzuki:
Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. ICLR 2024 - [c21]Guibin Zhang, Kun Wang, Wei Huang, Yanwei Yue, Yang Wang, Roger Zimmermann, Aojun Zhou, Dawei Cheng, Jin Zeng, Yuxuan Liang:
Graph Lottery Ticket Automated. ICLR 2024 - [c20]Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong:
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples. ICML 2024: 4642-4695 - [c19]Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang:
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs. KDD 2024: 3164-3175 - [c18]Yilan Chen, Wei Huang, Lily Weng:
Provable and Efficient Dataset Distillation for Kernel Ridge Regression. NeurIPS 2024 - [c17]Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong:
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. NeurIPS 2024 - [c16]Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Kumar Jawanpuria, Bamdev Mishra:
SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining. NeurIPS 2024 - [c15]Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki:
On the Comparison between Multi-modal and Single-modal Contrastive Learning. NeurIPS 2024 - [c14]Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie:
Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. NeurIPS 2024 - [c13]Bikang Pan, Wei Huang, Ye Shi:
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. NeurIPS 2024 - [i26]Hongxia Li, Wei Huang, Jingya Wang, Ye Shi:
Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. CoRR abs/2403.00041 (2024) - [i25]Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra:
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining. CoRR abs/2406.02214 (2024) - [i24]Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong:
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples. CoRR abs/2406.03944 (2024) - [i23]Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang:
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs. CoRR abs/2406.12539 (2024) - [i22]Siqing Li, Jin-Duk Park, Wei Huang, Xin Cao, Won-Yong Shin, Zhiqiang Xu:
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs. CoRR abs/2409.06323 (2024) - [i21]Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie:
Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. CoRR abs/2409.19345 (2024) - [i20]Bikang Pan, Wei Huang, Ye Shi:
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. CoRR abs/2409.19610 (2024) - [i19]Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen:
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. CoRR abs/2410.04870 (2024) - [i18]Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong:
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. CoRR abs/2411.02199 (2024) - [i17]Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki:
On the Comparison between Multi-modal and Single-modal Contrastive Learning. CoRR abs/2411.02837 (2024) - [i16]Andi Han, Wei Huang, Yuan Cao, Difan Zou:
On the Feature Learning in Diffusion Models. CoRR abs/2412.01021 (2024) - [i15]Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, Ye Shi:
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. CoRR abs/2412.01256 (2024) - 2023
- [j3]Wei Huang, Chunrui Liu, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng:
Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection. Trans. Mach. Learn. Res. 2023 (2023) - [c12]Wuyang Chen, Wei Huang, Zhangyang Wang:
"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization. AutoML 2023: 14/1-29 - [c11]Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang:
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. NeurIPS 2023 - [c10]Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng:
Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS 2023 - [c9]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Understanding and Improving Feature Learning for Out-of-Distribution Generalization. NeurIPS 2023 - [i14]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR abs/2304.11327 (2023) - [i13]Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki:
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective. CoRR abs/2306.13926 (2023) - [i12]Hao Wu, Shilong Wang, Yuxuan Liang, Zhengyang Zhou, Wei Huang, Wei Xiong, Kun Wang:
Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. CoRR abs/2312.08403 (2023) - [i11]Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang:
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. CoRR abs/2312.13923 (2023) - 2022
- [j2]Li Wang, Wei Huang, Miao Zhang, Shirui Pan, Xiaojun Chang, Steven Weidong Su:
Pruning graph neural networks by evaluating edge properties. Knowl. Based Syst. 256: 109847 (2022) - [c8]Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou:
Auto-scaling Vision Transformers without Training. ICLR 2022 - [c7]Wei Huang, Yayong Li, Weitao Du, Richard Y. D. Xu, Jie Yin, Ling Chen, Miao Zhang:
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective. ICLR 2022 - [c6]Wuyang Chen, Wei Huang, Xinyu Gong, Boris Hanin, Zhangyang Wang:
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis. NeurIPS 2022 - [c5]Miao Zhang, Wei Huang, Bin Yang:
Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations. NeurIPS 2022 - [c4]Miao Zhang, Li Wang, David Campos, Wei Huang, Chenjuan Guo, Bin Yang:
Weighted Mutual Learning with Diversity-Driven Model Compression. NeurIPS 2022 - [i10]Wei Huang, Chunrui Liu, Yilan Chen, Tianyu Liu, Richard Yi Da Xu:
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning. CoRR abs/2202.01958 (2022) - [i9]Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou:
Auto-scaling Vision Transformers without Training. CoRR abs/2202.11921 (2022) - [i8]Wuyang Chen, Wei Huang, Xinyu Gong, Boris Hanin, Zhangyang Wang:
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis. CoRR abs/2205.05662 (2022) - 2021
- [b1]Wei Huang:
Understanding deep learning through ultra-wide neural networks. University of Technology Sydney, Australia, 2021 - [j1]Wei Huang, Richard Yi Da Xu:
Gaussian process latent variable model factorization for context-aware recommender systems. Pattern Recognit. Lett. 151: 281-287 (2021) - [c3]Wei Huang, Weitao Du, Richard Yi Da Xu:
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization. IJCAI 2021: 2577-2583 - [c2]Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng:
On the Equivalence between Neural Network and Support Vector Machine. NeurIPS 2021: 23478-23490 - [i7]Wei Huang, Yayong Li, Weitao Du, Richard Yi Da Xu, Jie Yin, Ling Chen:
Wide Graph Neural Networks: Aggregation Provably Leads to Exponentially Trainability Loss. CoRR abs/2103.03113 (2021) - [i6]Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Wei Huang, Gholamreza Haffari:
Differentiable Architecture Search Without Training Nor Labels: A Pruning Perspective. CoRR abs/2106.11542 (2021) - [i5]Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng:
On the Equivalence between Neural Network and Support Vector Machine. CoRR abs/2111.06063 (2021) - 2020
- [c1]Wei Huang, Richard Yi Da Xu, Weitao Du, Yutian Zeng, Yunce Zhao:
Mean Field Theory for Deep Dropout Networks: Digging up Gradient Backpropagation Deeply. ECAI 2020: 1215-1222 - [i4]Wei Huang, Weitao Du, Richard Yi Da Xu:
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization. CoRR abs/2004.05867 (2020) - [i3]Wei Huang, Weitao Du, Richard Yi Da Xu, Chunrui Liu:
Implicit bias of deep linear networks in the large learning rate phase. CoRR abs/2011.12547 (2020)
2010 – 2019
- 2019
- [i2]Wei Huang, Richard Yi Da Xu, Weitao Du, Yutian Zeng, Yunce Zhao:
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply. CoRR abs/1912.09132 (2019) - [i1]Wei Huang, Richard Yi Da Xu:
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems. CoRR abs/1912.09593 (2019)
Coauthor Index
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