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ML
2008
ACM
13 years 8 months ago
A bias/variance decomposition for models using collective inference
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...
Jennifer Neville, David Jensen
COGSCI
2004
120views more  COGSCI 2004»
13 years 8 months ago
Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers
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
CSDA
2008
122views more  CSDA 2008»
13 years 8 months ago
Bayesian inference for nonlinear multivariate diffusion models observed with error
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...
Andrew Golightly, Darren J. Wilkinson
ICFP
2009
ACM
14 years 8 months ago
Complete and decidable type inference for GADTs
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...
ICVS
2003
Springer
14 years 1 months ago
A Self-Referential Perceptual Inference Framework for Video Interpretation
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...
Christopher Town, David Sinclair