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Found 6 papers, 3 papers with code
Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors.
Novel-View Acoustic Synthesis from 3D Reconstructed Rooms
Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sources, we estimate the sound anywhere in the scene.
Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning
We present Spatial LibriSpeech, a spatial audio dataset with over 650 hours of 19-channel audio, first-order ambisonics, and optional distractor noise.
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning
In this work, we demonstrate that encoding environment dynamics in the reward function (REED) dramatically reduces the number of preference labels required in state-of-the-art preference-based RL frameworks.
Contrastive Self-Supervised Learning for Skeleton Representations
Human skeleton point clouds are commonly used to automatically classify and predict the behaviour of others.
On the role of Lip Articulation in Visual Speech Perception
Previous research has shown that traditional metrics used to optimize and assess models for generating lip motion from speech are not a good indicator of subjective opinion of animation quality.
