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Found 10 papers, 2 papers with code
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data.
NeurIPS 2022 Competition: Driving SMARTS
The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS.
Vehicle Type Specific Waypoint Generation
We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior.
Critic Sequential Monte Carlo
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors.
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction.
Semi-supervised Sequential Generative Models
We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available.
Planning as Inference in Epidemiological Models
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation
On top of that, we extend our model with an additional latent variable and augment the dataset to train a controller that is robust to unsafe commands, such as asking it to turn into a wall.
Imitation Learning of Factored Multi-agent Reactive Models
We apply recent advances in deep generative modeling to the task of imitation learning from biological agents.
