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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...
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07/14/2023
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Memory-based Temporal Graph Neural Networks are powerful tools in dynami...
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05/30/2023
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
The growing interest in language-conditioned robot manipulation aims to ...
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04/19/2023
HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN
Predicting the throughput of WLAN deployments is a classic problem that ...
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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...
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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...
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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...
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03/28/2022
Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware
Runtime-reconfigurable software coupled with reconfigurable hardware is ...
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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...
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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...
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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...
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10/05/2020
Accurate, Efficient and Scalable Training of Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning models to genera...
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07/10/2019
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning rep...
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10/28/2018
Accurate, Efficient and Scalable Graph Embedding
The Graph Convolutional Network (GCN) model and its variants are powerfu...
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09/03/2018
Sea Clutter Distribution Modeling: A Kernel Density Estimation Approach
An accurate sea clutter distribution is crucial for decision region dete...
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01/11/2018