research
∙
07/17/2023
Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection
Voxel-based methods have achieved state-of-the-art performance for 3D ob...
share
research
∙
05/22/2023
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study
The excellent performance of deep neural networks is usually accompanied...
research
∙
04/28/2023
CORSD: Class-Oriented Relational Self Distillation
Knowledge distillation conducts an effective model compression method wh...
research
∙
04/24/2023
Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
Recently deep learning based quantitative structure-activity relationshi...
research
∙
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...
research
∙
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 ...
research
∙
07/12/2022
Contrastive Deep Supervision
The success of deep learning is usually accompanied by the growth in neu...
research
∙
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...
research
∙
05/25/2022
Region-aware Knowledge Distillation for Efficient Image-to-Image Translation
Recent progress in image-to-image translation has witnessed the success ...
research
∙
05/23/2022
PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection
The remarkable breakthroughs in point cloud representation learning have...
research
∙
05/17/2022
Cyber Risk Assessment for Capital Management
Cyber risk is an omnipresent risk in the increasingly digitized world th...
research
∙
03/12/2022
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation
Remarkable achievements have been attained with Generative Adversarial N...
research
∙
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...
research
∙
12/30/2021
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks
Quantized neural networks typically require smaller memory footprints an...
research
∙
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...
research
∙
06/04/2020
Integrating Machine Learning with Physics-Based Modeling
Machine learning is poised as a very powerful tool that can drastically ...
research
∙
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...
research
∙
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 ...
research
∙
12/04/2019
Universal approximation of symmetric and anti-symmetric functions
We consider universal approximations of symmetric and anti-symmetric fun...
research
∙
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...
research
∙
09/30/2019
Neural Canonical Transformation with Symplectic Flows
Canonical transformation plays a fundamental role in simplifying and sol...
research
∙
07/03/2019
Non-structured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
research
∙
06/27/2019
Deep neural network for Wannier function centers
We introduce a deep neural network (DNN) model that assigns the position...
research
∙
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...
research
∙
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...
research
∙
09/26/2018
Monge-Ampère Flow for Generative Modeling
We present a deep generative model, named Monge-Ampère flow, which build...
research
∙
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...
research
∙
12/10/2017
