About
I am a Senior Research Scientist in the Google Brain team. I have broad in interests in machine learning and artificial intelligence, with a particular focusses on scalable methods, vision, language, and generalization.
Prior to joining Google, I received a PhD from the Cambridge Computaqtional and Biological Learning lab, supervised by Zoubin Ghahramani, and Máté Lengyel. My areas of study were Bayesian ML, active learning, and cognitive science.
Publications
Self-Supervised Learning of Video-Induced Visual Invariances
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Xiaohua Zhai, Neil Houlsby, Sylvain Gelly, Mario LucicTo Appear: Computer Vision and Pattern Recognition (CVPR), 2020
[Read here]
In Domain Representation Learning for Remote Sensing
Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, Neil HoulsbyTo Appear: IEEE International Geoscience and Remote Sensing Symposium (IGASS), 2020
[Read here]
Big Transfer (BiT): General Visual Representation Learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil HoulsbyarXiv Preprint, 2019
[Read here]
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil HoulsbyarXiv Preprint, 2019
[Read here]
Self-Supervised GANs via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, Neil HoulsbyComputer Vision and Pattern Recognition (CVPR), 2019
[Read here]
Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain GellyInternational Conference on Machine Learning (ICML), 2019
[Read here]
On Self Modulation for Generative Adversarial Networks
Ting Chen, Mario Lucic, Neil Houlsby, Sylvain GellyInternational Conference on Learning Representations (ICLR), 2019
[Read here]
Transfer Learning with Neural AutoML
Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea GesmundoNeural Information Processing Systems (NeurIPS), 2018
[Read here]
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei WangInternational Conference on Learning Representations (ICLR), 2018
[Read here]
A Filtering Approach to Stochastic Variational Inference
Neil Houlsby, David BleiNeural Information Processing Systems (NeurIPS), 2014
[Read here]
Efficient Bayesian Active Learning and Matrix Modelling
Neil HoulsbyDoctor of Philosophy (PhD), University of Cambridge, 2014
[Read here]
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices
José Miguel Hernández-Lobato, Neil Houlsby, Zoubin GhahramaniInternational Conference on Machine Learning (ICML), 2014
[Read here]
Probabilistic Matrix Factorization with Non-random Missing Data
José Miguel Hernández-Lobato, Neil Houlsby, Zoubin GhahramaniInternational Conference on Machine Learning (ICML), 2014
[Read here]
Cold-start Active Learning with Robust Ordinal Matrix Factorization
Cold-start Active Learning with Robust Ordinal Matrix FactorizationInternational Conference on Machine Learning (ICML), 2014
[Read here]
A Scalable Gibbs Sampler for Probabilistic Entity Linking
Neil Houlsby, Massimiliano CiaramitaEuropean Conference on Information Retrieval (ECIR), 2014
[Read here]
Statistical Fitting of Undrained Strength Data
Neil Houlsby, Guy HoulsbyGéotechnique, 2013
[Read here]
Cognitive Tomography Reveals Complex Task-Independent Mental Representations
Neil Houlsby, Ferenc Huszár, Mohammad Ghassemi, Gergő Orbán, Daniel Wolpert, Máté LengyelCurrent Biology, 2013
[Read here]
Experimental Adaptive Bayesian Tomography
Konstantin Kravtsov, Stanislav Straupe, Igor Radchenko, Gleb Struchalin, Neil Houlsby, Sergey KulikPhyiscal Review A, 2013
[Read here]
Active learning for Interactive Visualization
Tomoharu Iwata, Neil Houlsby, Zoubin GhahramaniInternational Conference on AI and Statistics (AISTATS), 2013
[Read here]
Collaborative Gaussian Processes for Preference Learning
Neil Houlsby, Ferenc Huszár, Jose M. Hernández-lobato, Zoubin GhahramaniNeural Information Processing Systems (NeurIPS), 2012
[Read here]
Adaptive Bayesian Quantum Tomography
Ferenc Huszár, Neil HoulsbyPhysical Review A, 2012
[Read here]
Bayesian Active Learning for Gaussian Process Classification
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, Máté LengyelNIPS Workshop on Bayesian optimization, experimental design and bandits: Theory and applications, 2011
[Read here]