Zhong Li

Researcher

Publications

Afficher par:

- Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
  
  Shida Wang, Zhong Li, Qianxiao Li
  
  ICLR 2024 | January 2024
  
  Spotlight
  
  Publication
- On the Generalization Properties of Diffusion Models
  
  Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian
  
  2023 Neural Information Processing Systems | September 2023
  
  Publication
- A Brief Survey on the Approximation Theory for Sequence Modelling
  
  Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
  
  Journal of Machine Learning | March 2023, Vol 2(1): pp. 1-30
  
  Publication
- On the Approximation Properties of Recurrent Encoder-Decoder Architectures
  
  Zhong Li, Haotian Jiang, Qianxiao Li
  
  2022 International Conference on Learning Representations | January 2022
  
  Spotlight
  
  Publication
- Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  Journal of Machine Learning Research | January 2022, Vol 23(42): pp. 1-85
  
  Publication
- Approximation Theory of Convolutional Architectures for Time Series Modelling
  
  Haotian Jiang, Zhong Li, Qianxiao Li
  
  2021 International Conference on Machine Learning | May 2021
  
  Publication
- On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  2021 International Conference on Learning Representations | January 2021
  
  Publication

- Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
  
  Shida Wang, Zhong Li, Qianxiao Li
  
  ICLR 2024 | January 2024
  
  Spotlight
  
  Publication
- On the Generalization Properties of Diffusion Models
  
  Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian
  
  2023 Neural Information Processing Systems | September 2023
  
  Publication
- A Brief Survey on the Approximation Theory for Sequence Modelling
  
  Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
  
  Journal of Machine Learning | March 2023, Vol 2(1): pp. 1-30
  
  Publication
- On the Approximation Properties of Recurrent Encoder-Decoder Architectures
  
  Zhong Li, Haotian Jiang, Qianxiao Li
  
  2022 International Conference on Learning Representations | January 2022
  
  Spotlight
  
  Publication
- Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  Journal of Machine Learning Research | January 2022, Vol 23(42): pp. 1-85
  
  Publication
- Approximation Theory of Convolutional Architectures for Time Series Modelling
  
  Haotian Jiang, Zhong Li, Qianxiao Li
  
  2021 International Conference on Machine Learning | May 2021
  
  Publication
- On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  2021 International Conference on Learning Representations | January 2021
  
  Publication
- Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
  
  Shida Wang, Zhong Li, Qianxiao Li
  
  ICLR 2024 | January 2024
  
  Spotlight
  
  Publication
- On the Generalization Properties of Diffusion Models
  
  Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian
  
  2023 Neural Information Processing Systems | September 2023
  
  Publication
- A Brief Survey on the Approximation Theory for Sequence Modelling
  
  Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
  
  Journal of Machine Learning | March 2023, Vol 2(1): pp. 1-30
  
  Publication
- On the Approximation Properties of Recurrent Encoder-Decoder Architectures
  
  Zhong Li, Haotian Jiang, Qianxiao Li
  
  2022 International Conference on Learning Representations | January 2022
  
  Spotlight
  
  Publication
- Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  Journal of Machine Learning Research | January 2022, Vol 23(42): pp. 1-85
  
  Publication
- Approximation Theory of Convolutional Architectures for Time Series Modelling
  
  Haotian Jiang, Zhong Li, Qianxiao Li
  
  2021 International Conference on Machine Learning | May 2021
  
  Publication
- On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  2021 International Conference on Learning Representations | January 2021
  
  Publication

- Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
  
  Shida Wang, Zhong Li, Qianxiao Li
  
  ICLR 2024 | January 2024
  
  Spotlight
  
  Publication
- On the Generalization Properties of Diffusion Models
  
  Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian
  
  2023 Neural Information Processing Systems | September 2023
  
  Publication
- On the Approximation Properties of Recurrent Encoder-Decoder Architectures
  
  Zhong Li, Haotian Jiang, Qianxiao Li
  
  2022 International Conference on Learning Representations | January 2022
  
  Spotlight
  
  Publication
- Approximation Theory of Convolutional Architectures for Time Series Modelling
  
  Haotian Jiang, Zhong Li, Qianxiao Li
  
  2021 International Conference on Machine Learning | May 2021
  
  Publication
- On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  2021 International Conference on Learning Representations | January 2021
  
  Publication
- A Brief Survey on the Approximation Theory for Sequence Modelling
  
  Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
  
  Journal of Machine Learning | March 2023, Vol 2(1): pp. 1-30
  
  Publication
- Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
  
  Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
  
  Journal of Machine Learning Research | January 2022, Vol 23(42): pp. 1-85
  
  Publication

Zhong Li

Publications

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks

On the Generalization Properties of Diffusion Models

A Brief Survey on the Approximation Theory for Sequence Modelling

On the Approximation Properties of Recurrent Encoder-Decoder Architectures

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

Approximation Theory of Convolutional Architectures for Time Series Modelling

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks

On the Generalization Properties of Diffusion Models

A Brief Survey on the Approximation Theory for Sequence Modelling

On the Approximation Properties of Recurrent Encoder-Decoder Architectures

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

Approximation Theory of Convolutional Architectures for Time Series Modelling

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks

On the Generalization Properties of Diffusion Models

A Brief Survey on the Approximation Theory for Sequence Modelling

On the Approximation Properties of Recurrent Encoder-Decoder Architectures

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

Approximation Theory of Convolutional Architectures for Time Series Modelling

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks

On the Generalization Properties of Diffusion Models

On the Approximation Properties of Recurrent Encoder-Decoder Architectures

Approximation Theory of Convolutional Architectures for Time Series Modelling

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis

A Brief Survey on the Approximation Theory for Sequence Modelling

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

Contact Zhong Li

Microsoft Research Lab – Asia