Winther, Ole

59 publications

ICLRW 2025 SweetBERT: Exploring BERT-Based Models for IUPAC Glycan Nomenclature Modeling Irene Rubia-Rodríguez, Henrik Nielsen, Garry P. Gippert, Kristian Barrett, Bernard Henrissat, Ole Winther

ICLR 2024 BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks Frederikke Isa Marin, Felix Teufel, Marc Horlacher, Dennis Madsen, Dennis Pultz, Ole Winther, Wouter Boomsma

ICLR 2024 DiffEnc: Variational Diffusion with a Learned Encoder Beatrix Miranda Ginn Nielsen, Anders Christensen, Andrea Dittadi, Ole Winther

ECCV 2024 Geometry Fidelity for Spherical Images Anders Christensen, Nooshin Mojab, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther, Mar Gonzalez Franco, Andrea Colaco

ICMLW 2024 Geometry Fidelity for Spherical Images Anders Christensen, Nooshin Mojab, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther, Mar Gonzalez-Franco, Andrea Colaco

TMLR 2023 Addressing Caveats of Neural Persistence with Deep Graph Persistence Leander Girrbach, Anders Christensen, Ole Winther, Zeynep Akata, A. Sophia Koepke

NeurIPS 2023 Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation Giorgio Giannone, Akash Srivastava, Ole Winther, Faez Ahmed

ICMLW 2023 Caveats of Neural Persistence in Deep Neural Networks Leander Girrbach, Anders Christensen, Ole Winther, Zeynep Akata, A. Sophia Koepke

ICCV 2023 Image-Free Classifier Injection for Zero-Shot Classification Anders Christensen, Massimiliano Mancini, A. Sophia Koepke, Ole Winther, Zeynep Akata

NeurIPS 2023 Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics Mathias Schreiner, Ole Winther, Simon Olsson

NeurIPSW 2023 Improving Precision in Language Models Learning from Invalid Samples Niels Larsen, Giorgio Giannone, Ole Winther, Kai Blin

NeurIPSW 2023 SecretoGen: Towards Prediction of Signal Peptides for Efficient Protein Secretion Felix Teufel, Carsten Stahlhut, Jan Refsgaard, Henrik Nielsen, Ole Winther, Dennis Madsen

ICML 2023 Unifying Molecular and Textual Representations via Multi-Task Language Modelling Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica

ICML 2023 Variational Open-Domain Question Answering Valentin Liévin, Andreas Geert Motzfeldt, Ida Riis Jensen, Ole Winther

NeurIPSW 2022 Few-Shot Diffusion Models Giorgio Giannone, Didrik Nielsen, Ole Winther

ICML 2022 Generalization and Robustness Implications in Object-Centric Learning Andrea Dittadi, Samuele S Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello

ECCVW 2022 Image Super-Resolution with Deep Variational Autoencoders Darius Chira, Ilian Haralampiev, Ole Winther, Andrea Dittadi, Valentin Liévin

ICML 2022 SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation Giorgio Giannone, Ole Winther

ICLR 2022 The Role of Pretrained Representations for the OOD Generalization of RL Agents Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

NeurIPSW 2021 Hierarchical Few-Shot Generative Models Giorgio Giannone, Ole Winther

ICLR 2021 On the Transfer of Disentangled Representations in Realistic Settings Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf

ICMLW 2021 Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

NeurIPS 2020 Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow Didrik Nielsen, Ole Winther

NeurIPS 2020 Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds Valentin Liévin, Andrea Dittadi, Anders Christensen, Ole Winther

NeurIPS 2020 SurVAE Flows: Surjections to Bridge the Gap Between VAEs and Flows Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling

NeurIPS 2019 BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther

AISTATS 2018 Bayesian Structure Learning for Dynamic Brain Connectivity Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo

NeurIPS 2018 Recurrent Relational Networks Rasmus Palm, Ulrich Paquet, Ole Winther

NeurIPS 2017 A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther

JMLR 2017 Bayesian Inference for Spatio-Temporal Spike-and-Slab Priors Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen

NeurIPS 2017 Hash Embeddings for Efficient Word Representations Dan Tito Svenstrup, Jonas Hansen, Ole Winther

ICML 2016 Autoencoding Beyond Pixels Using a Learned Similarity Metric Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther

ICML 2016 Auxiliary Deep Generative Models Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther

AISTATS 2016 Bayesian Generalised Ensemble Markov Chain Monte Carlo Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg

JMLR 2016 Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther

AAAI 2016 Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search Marco Fraccaro, Ulrich Paquet, Ole Winther

NeurIPS 2016 Ladder Variational Autoencoders Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther

NeurIPS 2016 Sequential Neural Models with Stochastic Layers Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther

NeurIPS 2014 Bayesian Inference for Structured Spike and Slab Priors Michael R Andersen, Ole Winther, Lars K. Hansen

JMLR 2013 Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models Manfred Opper, Ulrich Paquet, Ole Winther

JMLR 2011 Sparse Linear Identifiable Multivariate Modeling Ricardo Henao, Ole Winther

NeurIPS 2009 Bayesian Sparse Factor Models and DAGs Inference and Comparison Ricardo Henao, Ole Winther

JMLR 2009 Perturbation Corrections in Approximate Inference: Mixture Modelling Applications Ulrich Paquet, Ole Winther, Manfred Opper

NeurIPS 2008 Improving on Expectation Propagation Manfred Opper, Ulrich Paquet, Ole Winther

AISTATS 2007 Semi-Supervised Mean Fields Fei Wang, Shijun Wang, Changshui Zhang, Ole Winther

JMLR 2005 Expectation Consistent Approximate Inference Manfred Opper, Ole Winther

ALT 2004 Approximate Inference in Probabilistic Models Manfred Opper, Ole Winther

NeurIPS 2004 Expectation Consistent Free Energies for Approximate Inference Manfred Opper, Ole Winther

NeurIPS 2003 Variational Linear Response Manfred Opper, Ole Winther

NeurIPS 2002 Incremental Gaussian Processes Joaquin Quiñonero-candela, Ole Winther

NeCo 2002 Mean-Field Approaches to Independent Component Analysis Pedro A. d. F. R. Højen-Sørensen, Ole Winther, Lars Kai Hansen

NeurIPS 2001 TAP Gibbs Free Energy, Belief Propagation and Sparsity Lehel Csató, Manfred Opper, Ole Winther

NeurIPS 2000 Computing with Finite and Infinite Networks Ole Winther

NeurIPS 2000 Ensemble Learning and Linear Response Theory for ICA Pedro A. d. F. R. Højen-Sørensen, Ole Winther, Lars Kai Hansen

NeCo 2000 Gaussian Processes for Classification: Mean-Field Algorithms Manfred Opper, Ole Winther

NeurIPS 1999 Efficient Approaches to Gaussian Process Classification Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther

NeurIPS 1998 Mean Field Methods for Classification with Gaussian Processes Manfred Opper, Ole Winther

NeurIPS 1996 A Mean Field Algorithm for Bayes Learning in Large Feed-Forward Neural Networks Manfred Opper, Ole Winther

NeurIPS 1996 The Effect of Correlated Input Data on the Dynamics of Learning Søren Halkjær, Ole Winther