| A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs D George, W Lehrach, K Kansky, M Lázaro-Gredilla, C Laan, B Marthi, ... Science 358 (6368), eaag2612, 2017 | 304 | 2017 |
| A regularized discriminative model for the prediction of protein–peptide interactions WP Lehrach, D Husmeier, CKI Williams Bioinformatics 22 (5), 532-540, 2006 | 26 | 2006 |
| Learning higher-order sequential structure with cloned HMMs A Dedieu, N Gothoskar, S Swingle, W Lehrach, M Lázaro-Gredilla, ... arXiv preprint arXiv:1905.00507, 2019 | 14 | 2019 |
| Generative shape models: Joint text recognition and segmentation with very little training data X Lou, K Kansky, W Lehrach, CC Laan, B Marthi, D Phoenix, D George advances in neural information processing systems 29, 2016 | 11 | 2016 |
| Method and apparatus for recognizing objects visually using a recursive cortical network D George, KA Kansky, DS Phoenix, C Laan, W Lehrach, B Marthi US Patent 9,262,698, 2016 | 10 | 2016 |
| Segmenting bacterial and viral DNA sequence alignments with a trans-dimensional phylogenetic factorial hidden Markov model WP Lehrach, D Husmeier Journal of the Royal Statistical Society Series C: Applied Statistics 58 (3 …, 2009 | 10 | 2009 |
| Molecular threading: mechanical extraction, stretching and placement of DNA molecules from a liquid-air interface AC Payne, M Andregg, K Kemmish, M Hamalainen, C Bowell, A Bleloch, ... PloS one 8 (7), e69058, 2013 | 9 | 2013 |
| Pgmax: Factor graphs for discrete probabilistic graphical models and loopy belief propagation in jax G Zhou, A Dedieu, N Kumar, W Lehrach, M Lázaro-Gredilla, S Kushagra, ... arXiv preprint arXiv:2202.04110, 2022 | 8 | 2022 |
| Query training: Learning a worse model to infer better marginals in undirected graphical models with hidden variables M Lázaro-Gredilla, W Lehrach, N Gothoskar, G Zhou, A Dedieu, D George Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8252-8260, 2021 | 8 | 2021 |
| Explaining visual cortex phenomena using recursive cortical network A Lavin, JS Guntupalli, M Lázaro-Gredilla, W Lehrach, D George bioRxiv, 380048, 2018 | 7 | 2018 |
| A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model D George, M Lazaro-Gredilla, W Lehrach, A Dedieu, G Zhou Biorxiv, 2020.09. 09.290601, 2020 | 6 | 2020 |
| Systems and methods for generating data explanations for neural networks and related systems D George, KA Kansky, CR Laan, W Lehrach, BM Marthi, DS Phoenix, ... US Patent 11,551,057, 2023 | 4 | 2023 |
| System and method for a recursive cortical network D George, KA Kansky, DS Phoenix, B Marthi, C Laan, W Lehrach US Patent 9,373,085, 2016 | 4 | 2016 |
| System and method for a recursive cortical network D George, K Kansky, DS Phoenix, B Marthi, C Laan, W Lehrach US Patent 11,315,006, 2022 | 2 | 2022 |
| Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping G Zhou, W Lehrach, A Dedieu, M Lázaro-Gredilla, D George arXiv preprint arXiv:2112.03371, 2021 | 2 | 2021 |
| Bayesian machine learning methods for predicting protein-peptide interactions and detecting mosaic structures in DNA sequences alignments W Lehrach University of Edinburgh, 2010 | 2 | 2010 |
| System and method for a recursive cortical network D George, K Kansky, DS Phoenix, B Marthi, C Laan, W Lehrach US Patent App. 17/717,664, 2022 | 1 | 2022 |
| Method and system for query training M Lazaro-Gredilla, W Lehrach, N Gothoskar, Z GuangYao, A Dedieu, ... US Patent 11,157,793, 2021 | 1 | 2021 |
| Query Training: Learning and inference for directed and undirected graphical models M Lázaro-Gredilla, W Lehrach, N Gothoskar, G Zhou, A Dedieu, D George, ... stat 1050, 18, 2020 | 1 | 2020 |
| Learning undirected models via query training M Lazaro-Gredilla, W Lehrach, D George arXiv preprint arXiv:1912.02893, 2019 | 1 | 2019 |
