Nesting probabilistic programs por Rainforth, T

Automating inference, learning, and design using probabilistic programming por Rainforth, T

Beyond Bayesian model averaging over paths in probabilistic programs with stochastic support por Reichelt, T, Ong, L, Rainforth, T

In-context learning learns label relationships but is not conventional learning por Kossen, J, Gal, Y, Rainforth, T

Amortized Monte Carlo integration por Goliński, A, Wood, F, Rainforth, T

SelfCheck: using LLMs to zero-shot check their own step-by-step reasoning por Miao, N, Teh, YW, Rainforth, T

On the expected size of conformal prediction sets por Dhillon, GS, Deligiannidis, G, Rainforth, T

Making better use of unlabelled data in Bayesian Active learning por Bickford Smith, F, Foster, A, Rainforth, T

Target–aware Bayesian inference: how to beat optimal conventional estimators por Rainforth, T, Goliński, A, Wood, F, Zaidi, S

Capturing label characteristics in VAEs por Joy, T, Schmon, S, Torr, P, Narayanaswamy, S, Rainforth, T

A statistical approach to assessing neural network robustness por Webb, S, Rainforth, T, Teh, Y, Mudigonda, P

Ceramic microstructures : property control by processing / por 178576 Lee, William E., Rainforth, W. Mark

Online variational filtering and parameter learning por Campbell, A, Shi, Y, Rainforth, T, Doucet, A

Auto-encoding sequential Monte Carlo por Le, T, Igl, M, Rainforth, T, Jin, T, Wood, F

Disentangling disentanglement in variational autoencoders por Mathieu, E, Rainforth, T, Siddharth, N, Teh, Y

Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support por Zhou, Y, Yang, H, Teh, YW, Rainforth, T

RIGHT IDEA, WRONG TIME: THE WISENET SCIENCE SHOP 1988-1990 por Gabriele Bammer, Merrelyn Emery, Linda Gowing, Jennifer Rainforth

Enlace del recurso

Modern Bayesian experimental design por Rainforth, T, Foster, A, Ivanova, DR, Bickford Smith, F

Towards a theoretical understanding of the robustness of variational autoencoders por Camuto, A, Willetts, M, Roberts, S, Holmes, C, Rainforth, T

On nesting Monte Carlo estimators por Rainforth, T, Cornish, R, Yang, H, Warrington, A, Wood, F