about
Predicting disease progression from short biomarker series using expert advice algorithmTheoretical and Experimental Analyses of Tensor-Based Regression and Classification.Modeling sparse connectivity between underlying brain sources for EEG/MEG.QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingf-GAN: Training Generative Neural Samplers using Variational Divergence MinimizationMultivariate analysis of noise in genetic regulatory networks.A regularized discriminative framework for EEG analysis with application to brain-computer interface.Large-scale EEG/MEG source localization with spatial flexibility.Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions.Continuous Hierarchical Representations with Poincaré Variational Auto-EncodersInterpolating Convex and Non-Convex Tensor Decompositions via the Subspace NormMultitask learning meets tensor factorization: task imputation via convex optimizationConvex Tensor Decomposition via Structured Schatten Norm RegularizationPerfect Dimensionality Recovery by Variational Bayesian PCAStatistical Performance of Convex Tensor DecompositionGlobal Analytic Solution for Variational Bayesian Matrix FactorizationInvariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer InterfacingLogistic Regression for Single Trial EEG Classification
P50
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P50
description
onderzoeker
@nl
researcher, machine learning, Microsoft Research Cambridge
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Ryota Tomioka
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Ryota Tomioka
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Ryota Tomioka
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Ryota Tomioka
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Ryota Tomioka
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Ryota Tomioka
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P101
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