Energy
Learning from Highly Sparse Spatio-temporal Data
Incomplete spatio-temporal data in the real world has spawned much research.
COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation
Our insight is that satellite's earth observation photos are not just images
Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees
We provide non-asymptotic convergence rates for LSE and SME under i.i.d.
Federated Learning over Connected Modes Dennis Grinwald
In each communication round, each client uniformly samples points in the neighborhood of their projected point (top-right plot), and jointly train the solution simplex.
Understanding and Minimising Outlier Features in Transformer Training Bobby He
Despite their widespread use, our understanding of deep neural networks (NNs) and their training dynamics is very much incomplete.
The Value of Reward Lookahead in Reinforcement Learning
In reinforcement learning (RL), agents sequentially interact with changing environments while aiming to maximize the obtained rewards.