Gunnar Rätsch

发表

Input space versus feature space in kernel-based methods

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, IEEE Trans. Neural Networks.

Predicting Time Series with Support Vector Machines

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1997, ICANN.

Fisher discriminant analysis with kernels

B. Scholkopf, S. Mika, G. Ratsch, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

The Need for Open Source Software in Machine Learning

Carl E. Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, 2007, J. Mach. Learn. Res..

Soft Margins for AdaBoost

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 2001, Machine Learning.

Kernel PCA and De-Noising in Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998, NIPS.

Analysis of nonstationary time series by mixtures of self-organizing predictors

J. Kohlmorgen, G. Ratsch, S. Lemm, 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

Large Scale Multiple Kernel Learning

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2006, J. Mach. Learn. Res..

Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project

Raymond K. Auerbach, Kevin Y. Yip, Stefan R. Henz, 2010, Science.

Automatic Relevance Determination For Deep Generative Models

Gunnar Rätsch, Theofanis Karaletsos, 2015, 1505.07765.

Weakly-Supervised Disentanglement Without Compromises

Ben Poole, B. Schölkopf, M. Tschannen, 2020, ICML.

Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

Kate B. Cook, Atina G. Coté, Mathew G. Lewsey, 2014, Cell.

The Molecular Taxonomy of Primary Prostate Cancer

Steven J. M. Jones, Joshua M. Stuart, Richard A. Moore, 2015, Cell.

Multiple reference genomes and transcriptomes for Arabidopsis thaliana

Vipin T. Sreedharan, Richard M. Clark, Gunnar Rätsch, 2011, Nature.

Methods for Transcriptome Analysis with Tiling Arrays and mRNA-Seq

Gunnar Rätsch, 2008 .

Publisher Correction: Building an international consortium for tracking coronavirus health status

Matej Oresic, Hedi Peterson, Casey S. Greene, 2020, Nature Medicine.

DNA methylation variation in Arabidopsis has a genetic basis and shows evidence of local adaptation

Richard M. Clark, Gunnar Rätsch, O. Stegle, 2014, 1410.5723.

META$^\mathbf{2}$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning.

Gunnar Rätsch, Harun Mustafa, Vincent Fortuin, 2019, 1909.13146.

GP-VAE: Deep Probabilistic Time Series Imputation

Gunnar Rätsch, S. Mandt, Vincent Fortuin, 2019, AISTATS.

Bayesian Neural Network Priors Revisited

Richard E. Turner, Mark van der Wilk, Gunnar Rätsch, 2021, ICLR.

An introduction to kernel-based learning algorithms

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2001, IEEE Trans. Neural Networks.

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation pdf

Francesco Locatello, Olivier Bachem, Mario Lucic, 2020, J. Mach. Learn. Res..

Dual roles of the nuclear cap-binding complex and SERRATE in pre-mRNA splicing and microRNA processing in Arabidopsis thaliana

Gunnar Rätsch, Wolfgang Busch, Timo Sachsenberg, 2008, Proceedings of the National Academy of Sciences.

WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU pdf

Martin Faltys, Tobias M. Merz, Gunnar Ratsch, 2020, SPACA.

Meta-Learning Mean Functions for Gaussian Processes

Gunnar Rätsch, Heiko Strathmann, Vincent Fortuin, 2019, 1901.08098.

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning pdf

Gunnar Ratsch, Alexander Immer, Vincent Fortuin, 2021, ICML.

Scalable Gaussian Process Variational Autoencoders

Stephan Mandt, Gunnar Ratsch, Vincent Fortuin, 2020, AISTATS.

Boosting, Margins and Beyond

Gunnar Ratsch, Gunnar Rätsch, 2007 .

On Disentanglement in Gaussian Process Variational Autoencoders pdf

Gunnar Ratsch, Vincent Fortuin, Simon Bing, 2021, ArXiv.

Bayesian representation learning with oracle constraints

Serge J. Belongie, Serge Belongie, Gunnar Ratsch, 2015, ICLR 2016.

Early prediction of respiratory failure in the intensive care unit pdf

Stephanie L. Hyland, Martin Faltys, Xinrui Lyu, 2021, ArXiv.

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

M. E. Khan, Gunnar Rätsch, M. Bauer, 2021, ICML.