Energy
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control.
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
Rather, we aim to rapidly and accurately sample from the posterior distribution.
Stepping Forward on the Last Mile
An empirical study on how training with forward gradients navigates in the loss landscape is further explored.
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This phenomenon is especially prominent in high-noise settings.
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Then we propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies.
Constant Acceleration Flow
CAF introduces acceleration as an additional learnable variable, allowing for more expressive and accurate estimation of the ODE flow.
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
However, learned PDE solvers based on Lagrangian particle discretizations, which are the preferred approach to problems with free surfaces or complex physics, remain largely unexplored.
Model-Based Transfer Learning for Contextual Reinforcement Learning
Deep reinforcement learning (RL) is a powerful approach to complex decision making. However, one issue that limits its practical application is its brittleness, sometimes failing to train in the presence of small changes in the environment.
Transition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions.