15 citations · 23 across the 18 of their papers we have counts for
4 papers · 1 filter
Features are fate: a theory of transfer learning in high-dimensional regression
Javan Tahir, Surya Ganguli, Grant M. Rotskoff
With the emergence of large-scale pre-trained neural networks, methods to adapt such "foundation" models to data-limited downstream tasks have become a necessity. Fine-tuning, pref…
Statistical Spatially Inhomogeneous Diffusion Inference
Yinuo Ren, Yiping Lu, Lexing Ying +1
Inferring a diffusion equation from discretely-observed measurements is a statistical challenge of significant importance in a variety of fields, from single-molecule tracking in b…
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling p…
Global convergence of neuron birth-death dynamics
Grant Rotskoff, Samy Jelassi, Joan Bruna +1
Neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for the favorable training properties of "o…