Search Results for author:
Found 9 papers, 3 papers with code
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks
Therefore, it is desirable for the trained model to adapt to new tasks that have limited data samples available.
Unsupervised Monocular Depth Learning in Dynamic Scenes
We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consistency being the sole source of supervision.
Fine-Grained Stochastic Architecture Search
Mobile neural architecture search (NAS) methods automate the design of small models but state-of-the-art NAS methods are expensive to run.
Adversarially Robust Frame Sampling with Bounded Irregularities
In recent years, video analysis tools for automatically extracting meaningful information from videos are widely studied and deployed.
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving.
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.
Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm
Lane change is a challenging task which requires delicate actions to ensure safety and comfort.
EnsembleNet: End-to-End Optimization of Multi-headed Models
Ensembling is a universally useful approach to boost the performance of machine learning models.
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal.
