Linfeng Zhang | DeepAI

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Featured Co-authors

Xin Chen
129 publications
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85 publications
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83 publications
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71 publications
Han Wang
67 publications
Weinan E
61 publications
Xue Lin
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Li Yi
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∙ 12/16/2022

Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

The success of deep learning heavily relies on large-scale data with com...

1 Runpei Dong, et al. ∙

∙ 08/17/2022

DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation

Machine learning assisted modeling of the inter-atomic potential energy ...

0 Duo Zhang, et al. ∙

∙ 07/12/2022

Contrastive Deep Supervision

The success of deep learning is usually accompanied by the growth in neu...

0 Linfeng Zhang, et al. ∙

∙ 06/21/2022

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made i...

0 Wenfei Li, et al. ∙

∙ 05/25/2022

Region-aware Knowledge Distillation for Efficient Image-to-Image Translation

Recent progress in image-to-image translation has witnessed the success ...

0 Linfeng Zhang, et al. ∙

∙ 05/23/2022

PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

The remarkable breakthroughs in point cloud representation learning have...

0 Linfeng Zhang, et al. ∙

∙ 05/17/2022

Cyber Risk Assessment for Capital Management

Cyber risk is an omnipresent risk in the increasingly digitized world th...

0 Wing Fung Chong, et al. ∙

∙ 03/12/2022

Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation

Remarkable achievements have been attained with Generative Adversarial N...

0 Linfeng Zhang, et al. ∙

∙ 01/05/2022

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms

High-performance computing, together with a neural network model trained...

0 Zhuoqiang Guo, et al. ∙

∙ 12/30/2021

Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks

Quantized neural networks typically require smaller memory footprints an...

2 Runpei Dong, et al. ∙

∙ 08/01/2020

DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory

We propose a general machine learning-based framework for building an ac...

0 Yixiao Chen, et al. ∙

∙ 06/04/2020

Integrating Machine Learning with Physics-Based Modeling

Machine learning is poised as a very powerful tool that can drastically ...

0 Weinan E, et al. ∙

∙ 04/24/2020

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

We present the GPU version of DeePMD-kit, which, upon training a deep ne...

0 Denghui Lu, et al. ∙

∙ 12/05/2019

Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks

Microblogs are widely used to express people's opinions and feelings in ...

0 Yuni Lai, et al. ∙

∙ 12/04/2019

Universal approximation of symmetric and anti-symmetric functions

We consider universal approximations of symmetric and anti-symmetric fun...

0 Jiequn Han, et al. ∙

∙ 11/27/2019

Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks

The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 201...

0 Leonardo Zepeda-Núñez, et al. ∙

∙ 09/30/2019

Neural Canonical Transformation with Symplectic Flows

Canonical transformation plays a fundamental role in simplifying and sol...

0 Shuo-Hui Li, et al. ∙

∙ 07/03/2019

Non-structured DNN Weight Pruning Considered Harmful

Large deep neural network (DNN) models pose the key challenge to energy ...

3 Yanzhi Wang, et al. ∙

∙ 06/27/2019

Deep neural network for Wannier function centers

We introduce a deep neural network (DNN) model that assigns the position...

0 Linfeng Zhang, et al. ∙

∙ 05/17/2019

Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

Convolutional neural networks have been widely deployed in various appli...

0 Linfeng Zhang, et al. ∙

∙ 10/28/2018

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

An active learning procedure called Deep Potential Generator (DP-GEN) is...

0 Linfeng Zhang, et al. ∙

∙ 09/26/2018

Monge-Ampère Flow for Generative Modeling

We present a deep generative model, named Monge-Ampère flow, which build...

0 Linfeng Zhang, et al. ∙

∙ 12/11/2017

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Recent developments in many-body potential energy representation via dee...

0 Han Wang, et al. ∙

∙ 12/10/2017

Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic Methodology

A new approach for efficiently exploring the configuration space and com...

0 Linfeng Zhang, et al. ∙