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09/10/2023
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Diffusion Probabilistic Models (DPMs) have achieved considerable success...
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07/20/2023
Fractional Denoising for 3D Molecular Pre-training
Coordinate denoising is a promising 3D molecular pre-training method, wh...
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06/16/2023
Power-law Dynamic arising from machine learning
We study a kind of new SDE that was arisen from the research on optimiza...
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04/07/2023
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in m...
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07/14/2022
Improved OOD Generalization via Conditional Invariant Regularizer
Recently, generalization on out-of-distribution (OOD) data with correlat...
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04/13/2022
Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs
Stochastic partial differential equations (SPDEs) are significant tools ...
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10/08/2021
Momentum Doesn't Change the Implicit Bias
The momentum acceleration technique is widely adopted in many optimizati...
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06/08/2021
Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics
Learning dynamics governed by differential equations is crucial for pred...
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01/08/2021
BN-invariant sharpness regularizes the training model to better generalization
It is arguably believed that flatter minima can generalize better. Howev...
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01/08/2021
Towards Accelerating Training of Batch Normalization: A Manifold Perspective
Batch normalization (BN) has become a crucial component across diverse d...
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12/04/2020
Non-Asymptotic Analysis of Excess Risk via Empirical Risk Landscape
In this paper, we provide a unified analysis of the excess risk of the m...
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06/24/2020
Dynamic of Stochastic Gradient Descent with State-Dependent Noise
Stochastic gradient descent (SGD) and its variants are mainstream method...
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10/18/2019
Interpreting Basis Path Set in Neural Networks
Based on basis path set, G-SGD algorithm significantly outperforms conve...
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07/23/2019
Off-policy Learning for Multiple Loggers
It is well known that the historical logs are used for evaluating and le...
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03/06/2019
Positively Scale-Invariant Flatness of ReLU Neural Networks
It was empirically confirmed by Keskar et al.SharpMinima that flatter mi...
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09/21/2018
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
In reinforcement learning (RL) , one of the key components is policy eva...
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09/21/2018
Target Transfer Q-Learning and Its Convergence Analysis
Q-learning is one of the most popular methods in Reinforcement Learning ...
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05/08/2018
Differential Equations for Modeling Asynchronous Algorithms
Asynchronous stochastic gradient descent (ASGD) is a popular parallel op...
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09/29/2017
Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling
When using stochastic gradient descent to solve large-scale machine lear...
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09/27/2016