71 citations · 604 across the 67 of their papers we have counts for
12 papers · 1 filter
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
Desi R. Ivanova, Adam Foster, Steven Kleinegesse +2
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian optimal e…
Online Variational Filtering and Parameter Learning
Andrew Campbell, Yuyang Shi, Tom Rainforth +1
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. A…
Group Equivariant Subsampling
Jin Xu, Hyunjik Kim, Tom Rainforth +1
Subsampling is used in convolutional neural networks (CNNs) in the form of pooling or strided convolutions, to reduce the spatial dimensions of feature maps and to allow the recept…
On Incorporating Inductive Biases into VAEs
Ning Miao, Emile Mathieu, N. Siddharth +2
We explain why directly changing the prior can be a surprisingly ineffective mechanism for incorporating inductive biases into VAEs, and introduce a simple and effective alternativ…
Learning Multimodal VAEs through Mutual Supervision
Tom Joy, Yuge Shi, Philip H. S. Torr +3
Multimodal VAEs seek to model the joint distribution over heterogeneous data (e.g.\ vision, language), whilst also capturing a shared representation across such modalities. Prior w…
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers
Andreas Kirsch, Tom Rainforth, Yarin Gal
Expanding on MacKay (1992), we argue that conventional model-based methods for active learning - like BALD - have a fundamental shortfall: they fail to directly account for the tes…