75 citations · 392 across the 36 of their papers we have counts for
36 papers
Learning Specialized Activation Functions for Physics-informed Neural Networks
Honghui Wang, Lu Lu, Shiji Song +1
Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty. In this work, we reveal the connection between the optimization difficulty of PINNs and a…
ActiveNeRF: Learning where to See with Uncertainty Estimation
Xuran Pan, Zihang Lai, Shiji Song +1
Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective,…
The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems
Haofeng Yuan, Peng Jiang, Shiji Song
Set covering problem is an important class of combinatorial optimization problems, which has been widely applied and studied in many fields. In this paper, we propose an improved c…
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding
Haojun Jiang, Yuanze Lin, Dongchen Han +2
Visual grounding, i.e., localizing objects in images according to natural language queries, is an important topic in visual language understanding. The most effective approaches fo…
Domain Adaptation via Prompt Learning
Chunjiang Ge, Rui Huang, Mixue Xie +4
Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approach…
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning
Wenjie Shi, Gao Huang, Shiji Song +1
Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent's behavior is usually difficult to interpret du…
Adaptive Focus for Efficient Video Recognition
Yulin Wang, Zhaoxi Chen, Haojun Jiang +3
In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency. It is observed that the most informative region in each…
CondenseNet V2: Sparse Feature Reactivation for Deep Networks
Le Yang, Haojun Jiang, Ruojin Cai +4
Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet has shown that this mecha…
Dynamic Neural Networks: A Survey
Yizeng Han, Gao Huang, Shiji Song +3
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynami…
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training
Yulin Wang, Zanlin Ni, Shiji Song +2
Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper a…
3D Object Detection with Pointformer
Xuran Pan, Zhuofan Xia, Shiji Song +2
Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer…
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Yulin Wang, Kangchen Lv, Rui Huang +3
The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and hig…
Regularizing Deep Networks with Semantic Data Augmentation
Yulin Wang, Gao Huang, Shiji Song +3
Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translati…
Meta-Semi: A Meta-learning Approach for Semi-supervised Learning
Yulin Wang, Jiayi Guo, Shiji Song +1
Deep learning based semi-supervised learning (SSL) algorithms have led to promising results in recent years. However, they tend to introduce multiple tunable hyper-parameters, maki…
Resolution Adaptive Networks for Efficient Inference
Le Yang, Yizeng Han, Xi Chen +3
Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redu…
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
Wenjie Shi, Gao Huang, Shiji Song +3
Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent's decision-making process is generally not transpare…
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
Kaichen Zhou, Shiji Song, Anke Xue +2
During recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems for its low-cost and intelligence. This paper proposes two smart…
Second-order Conic Programming Approach for Wasserstein Distributionally Robust Two-stage Linear Programs
Zhuolin Wang, Keyou You, Shiji Song +1
This paper proposes a second-order conic programming (SOCP) approach to solve distributionally robust two-stage stochastic linear programs over 1-Wasserstein balls. We start from t…
A selected review on reinforcement learning based control for autonomous underwater vehicles
Yachu Hsu, Hui Wu, Keyou You +1
Recently, reinforcement learning (RL) has been extensively studied and achieved promising results in a wide range of control tasks. Meanwhile, autonomous underwater vehicle (AUV) i…
Suspension Regulation of Medium-low-speed Maglev Trains via Deep Reinforcement Learning
Feiran Zhao, Keyou You, Shiji Song +2
The suspension regulation is critical to the operation of medium-low-speed maglev trains (mlsMTs). Due to uncertain environment, strong disturbances and high nonlinearity of the sy…
Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles
Wenjie Shi, Shiji Song, Cheng Wu +1
This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from…
Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning
Wenjie Shi, Shiji Song, Cheng Wu
Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe inst…
Implicit Semantic Data Augmentation for Deep Networks
Yulin Wang, Xuran Pan, Shiji Song +3
In this paper, we propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. Our…
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
Wenjie Shi, Shiji Song, Hui Wu +3
Model-free deep reinforcement learning (RL) algorithms have been widely used for a range of complex control tasks. However, slow convergence and sample inefficiency remain challeng…