research
∙
04/24/2023
Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
Recently deep learning based quantitative structure-activity relationshi...
share
research
∙
02/14/2023
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?
Molecular docking, given a ligand molecule and a ligand binding site (ca...
research
∙
02/14/2023
Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?
Molecular conformation generation (MCG) is a fundamental and important p...
research
∙
02/13/2023
Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM
The central problem in cryo-electron microscopy (cryo-EM) is to recover ...
research
∙
02/12/2023
3D Molecular Generation via Virtual Dynamics
Structure-based drug design, i.e., finding molecules with high affinitie...
research
∙
07/20/2022
Quantized Training of Gradient Boosting Decision Trees
Recent years have witnessed significant success in Gradient Boosting Dec...
research
∙
04/13/2022
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals
We present an efficient method of pretraining large-scale autoencoding l...
research
∙
03/09/2022
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
This technical note describes the recent updates of Graphormer, includin...
research
∙
02/28/2022
An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets
This technical note describes the recent updates of Graphormer, includin...
research
∙
06/23/2021
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding
The attention module, which is a crucial component in Transformer, canno...
research
∙
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...
research
∙
06/09/2021
Do Transformers Really Perform Bad for Graph Representation?
The Transformer architecture has become a dominant choice in many domain...
research
∙
05/10/2021
How could Neural Networks understand Programs?
Semantic understanding of programs is a fundamental problem for programm...
research
∙
02/27/2021
Transformers with Competitive Ensembles of Independent Mechanisms
An important development in deep learning from the earliest MLPs has bee...
research
∙
02/25/2021
LazyFormer: Self Attention with Lazy Update
Improving the efficiency of Transformer-based language pre-training is a...
research
∙
02/18/2021
Less is More: Pre-training a Strong Siamese Encoder Using a Weak Decoder
Many real-world applications use Siamese networks to efficiently match t...
research
∙
02/16/2021
Revisiting Language Encoding in Learning Multilingual Representations
Transformer has demonstrated its great power to learn contextual word re...
research
∙
08/04/2020
Taking Notes on the Fly Helps BERT Pre-training
How to make unsupervised language pre-training more efficient and less r...
research
∙
06/28/2020
Rethinking Positional Encoding in Language Pre-training
How to explicitly encode positional information into neural networks is ...
research
∙
06/28/2020
Rethinking the Positional Encoding in Language Pre-training
How to explicitly encode positional information into neural networks is ...
research
∙
06/10/2020
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Pre-trained contextual representations (e.g., BERT) have become the foun...
research
∙
05/12/2020
Invertible Image Rescaling
High-resolution digital images are usually downscaled to fit various dis...
research
∙
08/25/2019
LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm
Multiclass decomposition splits a multiclass classification problem into...
research
∙
07/16/2019
Light Multi-segment Activation for Model Compression
Model compression has become necessary when applying neural networks (NN...
research
∙
09/27/2018
