Rethinking Closed-Loop Training for Autonomous Driving. | 0 | 0.34 | 2022 |
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks | 0 | 0.34 | 2021 |
Deep Structured Reactive Planning | 0 | 0.34 | 2021 |
Self-Supervised Representation Learning from Flow Equivariance. | 0 | 0.34 | 2021 |
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving | 0 | 0.34 | 2021 |
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting | 5 | 0.89 | 2021 |
Just Label What You Need - Fine-Grained Active Selection for P&P through Partially Labeled Scenes. | 0 | 0.34 | 2021 |
End-to-end Contextual Perception and Prediction with Interaction Transformer | 0 | 0.34 | 2020 |
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World | 0 | 0.34 | 2020 |
PnPNet: End-to-End Perception and Prediction With Tracking in the Loop | 1 | 0.36 | 2020 |
End-To-End Interpretable Neural Motion Planner | 11 | 0.52 | 2019 |
Learning to Reweight Examples for Robust Deep Learning. | 35 | 0.86 | 2018 |
Differentiable Compositional Kernel Learning for Gaussian Processes. | 1 | 0.35 | 2018 |
Incorporating Relation Paths in Neural Relation Extraction. | 13 | 0.54 | 2017 |
Efficient Summarization with Read-Again and Copy Mechanism. | 0 | 0.34 | 2016 |