83 citations · 167 across the 8 of their papers we have counts for
8 papers
Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
Chris Whittle, Ge Yang, Matthew Evans +1
Machine learning has become an effective tool for processing the extensive data sets produced by large physics experiments. Gravitational-wave detectors are now listening to the un…
To the Noise and Back: Diffusion for Shared Autonomy
Takuma Yoneda, Luzhe Sun, Ge Yang +2
Shared autonomy is an operational concept in which a user and an autonomous agent collaboratively control a robotic system. It provides a number of advantages over the extremes of…
Overcoming the Spectral Bias of Neural Value Approximation
Ge Yang, Anurag Ajay, Pulkit Agrawal
Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest…
Strong Lensing Source Reconstruction Using Continuous Neural Fields
Siddharth Mishra-Sharma, Ge Yang
From the nature of dark matter to the rate of expansion of our Universe, observations of distant galaxies distorted through strong gravitational lensing have the potential to answe…
Rapid Locomotion via Reinforcement Learning
Gabriel B Margolis, Ge Yang, Kartik Paigwar +2
Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots. We present an end-to-end learned controller that achieves record agility for…
Bilinear value networks
Zhang-Wei Hong, Ge Yang, Pulkit Agrawal
The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficie…
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang +2
In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data…
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Bradly C. Stadie, Ge Yang, Rein Houthooft +5
We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-. Results are present…