Learning Graphical Models
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions Rui Y ang
These Q values are often objectives that offline algorithms aim to approximate.
Gated Inference Network: Inference and Learning State-Space Models
This paper advances temporal reasoning within dynamically changing high-dimensional noisy observations, focusing on a latent space that characterizes the nonlinear dynamics of objects in their environment.
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
We draw parallels between the behavior of LLMs and the evolution of human culture, as the latter has been extensively studied by cognitive scientists for decades.
A Bayesian Approach to Data Point Selection
Thus, we propose a novel Bayesian approach to DPS.
