Search Results for author:
Found 12 papers, 4 papers with code
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks
Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics of the plasma and controlling it.
Near-optimal Policy Identification in Active Reinforcement Learning
Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces.
Exploration via Planning for Information about the Optimal Trajectory
In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account.
How Useful are Gradients for OOD Detection Really?
One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection.
BATS: Best Action Trajectory Stitching
Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning algorithms to ensure the actions of the learned policy are constrained to the logged data.
Deep Attentive Variational Inference
Moreover, the architecture for this class of models favors local interactions among the latent variables between neighboring layers when designing the conditioning factors of the involved distributions.
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty.
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
However, this loss restricts the scope of applicable regression models, limits the ability to target many desirable properties (e. g. calibration, sharpness, centered intervals), and may produce poor conditional quantiles.
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction
We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinary differential equations.
Neural Dynamical Systems
We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models which incorporates prior knowledge in the form of systems of ordinary differential equations.
Offline Contextual Bayesian Optimization for Nuclear Fusion
One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction.
Offline Contextual Bayesian Optimization
In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find an optimal configuration.
