117 citations · 119 across the 3 of their papers we have counts for
4 papers · 1 filter
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Eric Zelikman, Sharon Zhou, Jeremy Irvin +7
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-…
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Sharon Zhou, Eric Zelikman, Fred Lu +3
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring…
CRUDE: Calibrating Regression Uncertainty Distributions Empirically
Eric Zelikman, Christopher Healy, Sharon Zhou +1
Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting. While there is extensive…
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents
Eric Zelikman, William Yin, Kenneth Wang
A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to chal…