activity
20162026
most citedAdaptive Multi-Teacher Multi-level Knowledge Distillation

222 citations · 644 across the 60 of their papers we have counts for

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Showing 2019Show all

13 papers · 1 filter

cs.RO2019★ 1 cited

Optimal Control of a Differentially Flat 2D Spring-Loaded Inverted Pendulum Model

Hua Chen, Patrick M. Wensing, Wei Zhang

This paper considers the optimal control problem of an extended spring-loaded inverted pendulum (SLIP) model with two additional actuators for active leg length and hip torque modu…

cs.CV2019

A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting

Wei Zhang, Wei Li, Lei Han

Very short-term convective storm forecasting, termed nowcasting, has long been an important issue and has attracted substantial interest. Existing nowcasting methods rely principal…

cs.LG2019★ 1 cited

Learning Robust Representations with Graph Denoising Policy Network

Lu Wang, Wenchao Yu, Wei Wang +5

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing p…

cs.RO2019

Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning

Guillermo A. Castillo, Bowen Weng, Wei Zhang +1

This paper presents a novel model-free reinforcement learning (RL) framework to design feedback control policies for 3D bipedal walking. Existing RL algorithms are often trained in…

cs.RO2019★ 4 cited

Reciprocal Collision Avoidance for General Nonlinear Agents using Reinforcement Learning

Hao Li, Bowen Weng, Abhishek Gupta +2

Finding feasible and collision-free paths for multiple nonlinear agents is challenging in the decentralized scenarios due to limited available information of other agents and compl…

cs.LG2019

The Expressivity and Training of Deep Neural Networks: toward the Edge of Chaos?

Gege Zhang, Gangwei Li, Ningwei Shen +1

Expressivity is one of the most significant issues in assessing neural networks. In this paper, we provide a quantitative analysis of the expressivity for the deep neural network (…