Search Results for author: Tammo Rukat

Found 7 papers, 2 papers with code

Variational Boosted Soft Trees

no code implementations21 Feb 2023 Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov

Variational inference is often used to implement Bayesian neural networks, but is difficult to apply to GBMs, because the decision trees used as weak learners are non-differentiable.

Decision Making Out-of-Distribution Detection +3

Bayesian Nonparametric Boolean Factor Models

no code implementations28 Jun 2019 Tammo Rukat, Christopher Yau

We build upon probabilistic models for Boolean Matrix and Boolean Tensor factorisation that have recently been shown to solve these problems with unprecedented accuracy and to enable posterior inference to scale to Billions of observation.

Probabilistic Boolean Tensor Decomposition

1 code implementation ICML 2018 Tammo Rukat, Chris Holmes, Christopher Yau

Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules Boolean algebra.

Model Selection Tensor Decomposition

TensOrMachine: Probabilistic Boolean Tensor Decomposition

1 code implementation11 May 2018 Tammo Rukat, Chris C. Holmes, Christopher Yau

Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules of Boolean algebra.

Model Selection Tensor Decomposition

An interpretable latent variable model for attribute applicability in the Amazon catalogue

no code implementations30 Nov 2017 Tammo Rukat, Dustin Lange, Cédric Archambeau

Learning attribute applicability of products in the Amazon catalog (e. g., predicting that a shoe should have a value for size, but not for battery-type at scale is a challenge.

Attribute

Bayesian Boolean Matrix Factorisation

no code implementations ICML 2017 Tammo Rukat, Chris C. Holmes, Michalis K. Titsias, Christopher Yau

Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns.

Collaborative Filtering

Resting state brain networks from EEG: Hidden Markov states vs. classical microstates

no code implementations7 Jun 2016 Tammo Rukat, Adam Baker, Andrew Quinn, Mark Woolrich

Functional brain networks exhibit dynamics on the sub-second temporal scale and are often assumed to embody the physiological substrate of cognitive processes.

EEG Electroencephalogram (EEG)

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