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Jiaoyang Huang
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2020 – today
- 2024
- [c9]Gérard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath:
High-dimensional SGD aligns with emerging outlier eigenspaces. ICLR 2024 - [i17]Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin:
Convergence Analysis of Probability Flow ODE for Score-based Generative Models. CoRR abs/2404.09730 (2024) - [i16]Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows. CoRR abs/2406.17263 (2024) - [i15]José A. Carrillo, Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Dongyi Wei:
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities. CoRR abs/2407.15693 (2024) - 2023
- [c8]Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang:
How Does Information Bottleneck Help Deep Learning? ICML 2023: 16049-16096 - [i14]Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance. CoRR abs/2302.11024 (2023) - [i13]Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang:
How Does Information Bottleneck Help Deep Learning? CoRR abs/2305.18887 (2023) - [i12]Gérard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath:
High-dimensional SGD aligns with emerging outlier eigenspaces. CoRR abs/2310.03010 (2023) - [i11]Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Sampling via Gradient Flows in the Space of Probability Measures. CoRR abs/2310.03597 (2023) - 2022
- [j3]Jiaoyang Huang, Daniel Zhengyu Huang, Qing Yang, Guang Cheng:
Power Iteration for Tensor PCA. J. Mach. Learn. Res. 23: 128:1-128:47 (2022) - [c7]Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang:
Robustness Implies Generalization via Data-Dependent Generalization Bounds. ICML 2022: 10866-10894 - [c6]Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu:
PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. LoG 2022: 27 - [i10]Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems. CoRR abs/2204.04386 (2022) - [i9]Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang:
Robustness Implies Generalization via Data-Dependent Generalization Bounds. CoRR abs/2206.13497 (2022) - [i8]Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Li-Ping Liu:
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations. CoRR abs/2211.14425 (2022) - 2021
- [c5]Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi:
How Shrinking Gradient Noise Helps the Performance of Neural Networks. IEEE BigData 2021: 1002-1007 - [c4]Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. NeurIPS 2021: 703-714 - [i7]Daniel Zhengyu Huang, Jiaoyang Huang:
Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model. CoRR abs/2102.10677 (2021) - [i6]Daniel Zhengyu Huang, Jiaoyang Huang:
Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution. CoRR abs/2103.00277 (2021) - [i5]Gérard Ben Arous, Daniel Zhengyu Huang, Jiaoyang Huang:
Long Random Matrices and Tensor Unfolding. CoRR abs/2110.10210 (2021) - 2020
- [c3]Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su:
Towards Understanding the Dynamics of the First-Order Adversaries. ICML 2020: 2484-2493 - [c2]Jiaoyang Huang, Horng-Tzer Yau:
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy. ICML 2020: 4542-4551 - [i4]Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su:
Towards Understanding the Dynamics of the First-Order Adversaries. CoRR abs/2010.10650 (2020)
2010 – 2019
- 2019
- [j2]Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Effect of Depth and Width on Local Minima in Deep Learning. Neural Comput. 31(7): 1462-1498 (2019) - [j1]Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning. Neural Comput. 31(12): 2293-2323 (2019) - [c1]Kenji Kawaguchi, Jiaoyang Huang:
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes. Allerton 2019: 92-99 - [i3]Kenji Kawaguchi, Jiaoyang Huang:
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes. CoRR abs/1908.02419 (2019) - [i2]Jiaoyang Huang, Horng-Tzer Yau:
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy. CoRR abs/1909.08156 (2019) - 2018
- [i1]Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling:
Effect of Depth and Width on Local Minima in Deep Learning. CoRR abs/1811.08150 (2018)
Coauthor Index
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