activity
20192023
most citedA Survey of Deep Reinforcement Learning in Video Games

149 citations · 222 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 3 cited

Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model

Wei Song, Jun Zhou, Mingjie Wang +3

This paper focuses on the recently popular task of point cloud completion guided by multimodal information. Although existing methods have achieved excellent performance by fusing…

cs.CV2022

BViT: Broad Attention based Vision Transformer

Nannan Li, Yaran Chen, Weifan Li +2

Recent works have demonstrated that transformer can achieve promising performance in computer vision, by exploiting the relationship among image patches with self-attention. While…

cs.CV2021★ 1 cited

Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search

Zixiang Ding, Yaran Chen, Nannan Li +2

Different from other deep scalable architecture-based NAS approaches, Broad Neural Architecture Search (BNAS) proposes a broad scalable architecture which consists of convolution a…

cs.CV2021★ 1 cited

ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search

Jiaqi Li, Haoran Li, Yaran Chen +5

Currently, an increasing number of model pruning methods are proposed to resolve the contradictions between the computer powers required by the deep learning models and the resourc…

stat.ML2020★ 68 cited

BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture

Zixiang Ding, Yaran Chen, Nannan Li +3

In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve…

cs.MA2019★ 149 cited

A Survey of Deep Reinforcement Learning in Video Games

Kun Shao, Zhentao Tang, Yuanheng Zhu +2

Deep reinforcement learning (DRL) has made great achievements since proposed. Generally, DRL agents receive high-dimensional inputs at each step, and make actions according to deep…