5 papers
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…
Contextual Salience for Fast and Accurate Sentence Vectors
Eric Zelikman, Richard Socher
Unsupervised vector representations of sentences or documents are a major building block for many language tasks such as sentiment classification. However, current methods are unin…