75 citations · 392 across the 36 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…