Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children rec...
David M. Sobel, Joshua B. Tenenbaum, Alison Gopnik
Diffusion processes governed by stochastic differential equations (SDEs) are a well established tool for modelling continuous time data from a wide range of areas. Consequently, t...
GADTs have proven to be an invaluable language extension, a.o. for ensuring data invariants and program correctness. Unfortunately, they pose a tough problem for type inference: w...
Tom Schrijvers, Simon L. Peyton Jones, Martin Sulz...
This paper presents an extensible architectural model for general content-based analysis and indexing of video data which can be customised for a given problem domain. Video interp...