Shuxin Zheng | DeepAI

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∙ 06/08/2023

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Advances in deep learning have greatly improved structure prediction of ...

0 Shuxin Zheng, et al. ∙

∙ 10/09/2022

Invertible Rescaling Network and Its Extensions

Image rescaling is a commonly used bidirectional operation, which first ...

0 Mingqing Xiao, et al. ∙

∙ 10/04/2022

One Transformer Can Understand Both 2D 3D Molecular Data

Unlike vision and language data which usually has a unique format, molec...

0 Shengjie Luo, et al. ∙

∙ 08/19/2022

Predicting the protein-ligand affinity from molecular dynamics trajectories

The accurate protein-ligand binding affinity prediction is essential in ...

31 Yaosen Min, et al. ∙

∙ 07/20/2022

Quantized Training of Gradient Boosting Decision Trees

Recent years have witnessed significant success in Gradient Boosting Dec...

5 Yu Shi, et al. ∙

∙ 05/26/2022

Your Transformer May Not be as Powerful as You Expect

Relative Positional Encoding (RPE), which encodes the relative distance ...

0 Shengjie Luo, et al. ∙

∙ 03/09/2022

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

This technical note describes the recent updates of Graphormer, includin...

10 Yu Shi, et al. ∙

∙ 02/28/2022

An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets

This technical note describes the recent updates of Graphormer, includin...

0 Yu Shi, et al. ∙

∙ 06/23/2021

Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding

The attention module, which is a crucial component in Transformer, canno...

9 Shengjie Luo, et al. ∙

∙ 06/15/2021

First Place Solution of KDD Cup 2021 OGB Large-Scale Challenge Graph-Level Track

In this technical report, we present our solution of KDD Cup 2021 OGB La...

7 Chengxuan Ying, et al. ∙

∙ 06/09/2021

Do Transformers Really Perform Bad for Graph Representation?

The Transformer architecture has become a dominant choice in many domain...

16 Chengxuan Ying, et al. ∙

∙ 05/10/2021

How could Neural Networks understand Programs?

Semantic understanding of programs is a fundamental problem for programm...

0 Dinglan Peng, et al. ∙

∙ 02/16/2021

Revisiting Language Encoding in Learning Multilingual Representations

Transformer has demonstrated its great power to learn contextual word re...

0 Shengjie Luo, et al. ∙

∙ 06/22/2020

Modeling Lost Information in Lossy Image Compression

Lossy image compression is one of the most commonly used operators for d...

8 Yaolong Wang, et al. ∙

∙ 06/10/2020

MC-BERT: Efficient Language Pre-Training via a Meta Controller

Pre-trained contextual representations (e.g., BERT) have become the foun...

0 Zhenhui Xu, et al. ∙

∙ 05/12/2020

Invertible Image Rescaling

High-resolution digital images are usually downscaled to fit various dis...

8 Mingqing Xiao, et al. ∙

∙ 02/13/2020

Cross-Iteration Batch Normalization

A well-known issue of Batch Normalization is its significantly reduced e...

0 Zhuliang Yao, et al. ∙

∙ 02/12/2020

On Layer Normalization in the Transformer Architecture

The Transformer is widely used in natural language processing tasks. To ...

0 Ruibin Xiong, et al. ∙

∙ 09/19/2018

Capacity Control of ReLU Neural Networks by Basis-path Norm

Recently, path norm was proposed as a new capacity measure for neural ne...

0 Shuxin Zheng, et al. ∙

∙ 02/11/2018

Optimizing Neural Networks in the Equivalent Class Space

It has been widely observed that many activation functions and pooling m...

0 Qi Meng, et al. ∙

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