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Found 12 papers, 0 papers with code
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning
A pervasive phenomenon in machine learning applications is distribution shift, where training and deployment conditions for a machine learning model differ.
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
Unlike existing end-to-end autonomous driving frameworks, PPAD models the interactions among ego, agents, and the dynamic environment in an autoregressive manner by interleaving the Prediction and Planning processes at every timestep, instead of a single sequential process of prediction followed by planning.
SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation
To take full advantage of the historical frames high-efficiently, we shunt the historical points into two groups with reference to the current points.
From One to Many: Dynamic Cross Attention Networks for LiDAR and Camera Fusion
The whole fusion architecture named Dynamic Cross Attention Network (DCAN) exploits multi-level image features and adapts to multiple representations of point clouds, which allows DCA to serve as a plug-in fusion module.
Bootstrap Motion Forecasting With Self-Consistent Constraints
Also, to model the multi-modality in motion forecasting, we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision.
Ranked #9 on
Motion Forecasting
on Argoverse CVPR 2020
Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation
Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially for large instances, and that objects are too close to separate each other.
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation
The Sparse Feature Encoder extracts the local context information for each point, and the Sparse Geometry Feature Enhancement enhances the geometric properties of a sparse point cloud via multi-scale sparse projection and attentive multi-scale fusion.
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation
By utilizing these two modules iteratively, features can be propagated between two different representations.
TPCN: Temporal Point Cloud Networks for Motion Forecasting
We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction.
Ranked #52 on
Motion Forecasting
on Argoverse CVPR 2020
Provably adaptive reinforcement learning in metric spaces
We study reinforcement learning in continuous state and action spaces endowed with a metric.
HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection
We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder (VFE) of different scales at point-wise level and project into multiple pseudo-image feature maps.
Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm
We design new algorithms for the combinatorial pure exploration problem in the multi-arm bandit framework.
