Energy
Multiwavelet-based Operator Learning for Differential Equations
The projected kernel is trained at multiple scales derived from using repeated computation of multiwavelet transform. This allows learning the complex dependencies at various scales and results in a resolution-independent scheme.
Constrained Sampling with Primal-Dual Langevin Monte Carlo
To do so, we bring classical optimization arguments for saddle-point algorithms to the geometry of Wasserstein space.
CNN Training data Test image Denoised image! CNN Test image Denoised image! CNN Training data Test image Denoised image!
Here we propose "GainTuning", a
LAVIB: A Large-scale Video Interpolation Benchmark
LA VIB comprises a large collection of high-resolution videos sourced from the web through an automated pipeline with minimal requirements for human verification. Metrics are computed for each video's motion magnitudes, luminance conditions, frame
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning Rong-Jun Qin
Offline reinforcement learning (RL) aims at learning effective policies from historical data without extra environment interactions.
Neur2SP: Neural Two-Stage Stochastic Programming
Having a mixed-integer linear program (MIP) or a nonlinear program (NLP) in the second stage further aggravates the intractability, even when specialized algorithms that exploit problem structure are employed.
Information-theoretic Task Selection for Meta-Reinforcement Learning
A common framework consists in modeling the range of tasks the agent may encounter as a distribution over all possible tasks.