Hongkuan Zhou | DeepAI

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∙ 09/14/2023

What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving

More research attention has recently been given to end-to-end autonomous...

0 Hongkuan Zhou, et al. ∙

∙ 07/14/2023

DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training

Memory-based Temporal Graph Neural Networks are powerful tools in dynami...

0 Hongkuan Zhou, et al. ∙

∙ 05/30/2023

Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data

The growing interest in language-conditioned robot manipulation aims to ...

0 Hongkuan Zhou, et al. ∙

∙ 04/19/2023

HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN

Predicting the throughput of WLAN deployments is a classic problem that ...

0 Hongkuan Zhou, et al. ∙

∙ 03/21/2023

Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving

With the rapid development of Pattern Recognition and Computer Vision te...

0 Hongkuan Zhou, et al. ∙

∙ 09/21/2022

Learning from Symmetry: Meta-Reinforcement Learning with Symmetric Data and Language Instructions

Meta-reinforcement learning (meta-RL) is a promising approach that enabl...

0 Xiangtong Yao, et al. ∙

∙ 03/28/2022

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs

Many real world graphs contain time domain information. Temporal Graph N...

0 Hongkuan Zhou, et al. ∙

∙ 03/28/2022

Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware

Runtime-reconfigurable software coupled with reconfigurable hardware is ...

0 Hongkuan Zhou, et al. ∙

∙ 03/10/2022

Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA

Temporal Graph Neural Networks (TGNNs) are powerful models to capture te...

0 Hongkuan Zhou, et al. ∙

∙ 09/09/2021

SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings

Temporal Knowledge Graphs store events in the form of subjects, relation...

0 Hongkuan Zhou, et al. ∙

∙ 05/10/2021

Accelerating Large Scale Real-Time GNN Inference using Channel Pruning

Graph Neural Networks (GNNs) are proven to be powerful models to generat...

0 Hongkuan Zhou, et al. ∙

∙ 10/05/2020

Accurate, Efficient and Scalable Training of Graph Neural Networks

Graph Neural Networks (GNNs) are powerful deep learning models to genera...

0 Hanqing Zeng, et al. ∙

∙ 07/10/2019

GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning rep...

0 Hanqing Zeng, et al. ∙

∙ 10/28/2018

Accurate, Efficient and Scalable Graph Embedding

The Graph Convolutional Network (GCN) model and its variants are powerfu...

0 Hanqing Zeng, et al. ∙

∙ 09/03/2018

Sea Clutter Distribution Modeling: A Kernel Density Estimation Approach

An accurate sea clutter distribution is crucial for decision region dete...

0 Hongkuan Zhou, et al. ∙

∙ 01/11/2018

To Relay or not to Relay: Open Distance and Optimal Deployment for Linear Underwater Acoustic Networks

Existing works have widely studied relay-aided underwater acoustic netwo...

0 Yuzhou Li, et al. ∙

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