Search Results for author:
Found 5 papers, 3 papers with code
Residual Connections Harm Self-Supervised Abstract Feature Learning
We demonstrate that adding a weighting factor to decay the strength of identity shortcuts within residual networks substantially improves semantic feature learning in the state-of-the-art self-supervised masked autoencoding (MAE) paradigm.
Training neural operators to preserve invariant measures of chaotic attractors
In this paper, we propose an alternative framework designed to preserve invariant measures of chaotic attractors that characterize the time-invariant statistical properties of the dynamics.
Deep Stochastic Mechanics
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models.
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems.
Pure Exploration in Kernel and Neural Bandits
To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space and carefully deal with the induced model misspecification.
