Bayesian Active Learning for Classification and Preference Learning - Neil Houlsby; Ferenc Huszar; Zoubin Ghahramani; Máté Lengyel (CoRR, 2011)

Collaborative Gaussian Processes for Preference Learning. - Neil Houlsby; José Miguel Hernández-Lobato; Ferenc Huszar; Zoubin Ghahramani (NIPS, 2012)

Active Learning for Interactive Visualization. - Tomoharu Iwata; Neil Houlsby; Zoubin Ghahramani (AISTATS, 2013)

Scalable Probabilistic Entity-Topic Modeling. - Neil Houlsby; Massimiliano Ciaramita (CoRR, 2013)

A Scalable Gibbs Sampler for Probabilistic Entity Linking. - Neil Houlsby; Massimiliano Ciaramita (ECIR, 2014)

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices. - José Miguel Hernández-Lobato; Neil Houlsby; Zoubin Ghahramani (ICML, 2014)

Probabilistic Matrix Factorization with Non-random Missing Data. - José Miguel Hernández-Lobato; Neil Houlsby; Zoubin Ghahramani (ICML, 2014)

Cold-start Active Learning with Robust Ordinal Matrix Factorization. - Neil Houlsby; José Miguel Hernández-Lobato; Zoubin Ghahramani (ICML, 2014)

A Filtering Approach to Stochastic Variational Inference. - Neil Houlsby; David M. Blei (NIPS, 2014)

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning. - Christian Buck; Jannis Bulian; Massimiliano Ciaramita; Wojciech Gajewski; Andrea Gesmundo; Neil Houlsby; Wei Wang 236 (ICLR, 2018)

Transfer Learning with Neural AutoML. - Catherine Wong; Neil Houlsby; Yifeng Lu; Andrea Gesmundo (NeurIPS, 2018)

Analyzing Language Learned by an Active Question Answering Agent. - Christian Buck; Jannis Bulian; Massimiliano Ciaramita; Wojciech Gajewski; Andrea Gesmundo; Neil Houlsby; Wei Wang 236 (CoRR, 2018)

Transfer Automatic Machine Learning. - Catherine Wong; Neil Houlsby; Yifeng Lu; Andrea Gesmundo (CoRR, 2018)

Self-Supervised GAN to Counter Forgetting. - Ting Chen; Xiaohua Zhai; Neil Houlsby (CoRR, 2018)

Self-Supervised Generative Adversarial Networks. - Ting Chen; Xiaohua Zhai; Marvin Ritter; Mario Lucic; Neil Houlsby (CoRR, 2018)

Neural Architecture Search Over a Graph Search Space. - Quentin de Laroussilhe; Stanislaw Jastrzebski; Neil Houlsby; Andrea Gesmundo (CoRR, 2018)

Self-Supervised GANs via Auxiliary Rotation Loss. - Ting Chen; Xiaohua Zhai; Marvin Ritter; Mario Lucic; Neil Houlsby (CVPR, 2019)

On Self Modulation for Generative Adversarial Networks. - Ting Chen; Mario Lucic; Neil Houlsby; Sylvain Gelly (ICLR (Poster), 2019)

Parameter-Efficient Transfer Learning for NLP. - Neil Houlsby; Andrei Giurgiu; Stanislaw Jastrzebski; Bruna Morrone; Quentin de Laroussilhe; Andrea Gesmundo; Mona Attariyan; Sylvain Gelly (ICML, 2019)

The Visual Task Adaptation Benchmark. - Xiaohua Zhai; Joan Puigcerver; Alexander Kolesnikov 003; Pierre Ruyssen; Carlos Riquelme; Mario Lucic; Josip Djolonga; André Susano Pinto; Maxim Neumann; Alexey Dosovitskiy; Lucas Beyer; Olivier Bachem; et al. (CoRR, 2019)

In-domain representation learning for remote sensing. - Maxim Neumann; André Susano Pinto; Xiaohua Zhai; Neil Houlsby (CoRR, 2019)

Large Scale Learning of General Visual Representations for Transfer. - Alexander Kolesnikov 003; Lucas Beyer; Xiaohua Zhai; Joan Puigcerver; Jessica Yung; Sylvain Gelly; Neil Houlsby (CoRR, 2019)

Self-Supervised Learning of Video-Induced Visual Invariances. - Michael Tschannen; Josip Djolonga; Marvin Ritter; Aravindh Mahendran; Neil Houlsby; Sylvain Gelly; Mario Lucic (CVPR, 2020)

Big Transfer (BiT): General Visual Representation Learning. - Alexander Kolesnikov 003; Lucas Beyer; Xiaohua Zhai; Joan Puigcerver; Jessica Yung; Sylvain Gelly; Neil Houlsby (ECCV (5), 2020)

Automatic Shortcut Removal for Self-Supervised Representation Learning. - Matthias Minderer; Olivier Bachem; Neil Houlsby; Michael Tschannen (ICML, 2020)

Training General Representations for Remote Sensing Using in-Domain Knowledge. - Maxim Neumann; André Susano Pinto; Xiaohua Zhai; Neil Houlsby (IGARSS, 2020)

Deep Ensembles for Low-Data Transfer Learning. - Basil Mustafa; Carlos Riquelme; Joan Puigcerver; André Susano Pinto; Daniel Keysers; Neil Houlsby (CoRR, 2020)

On Robustness and Transferability of Convolutional Neural Networks. - Josip Djolonga; Jessica Yung; Michael Tschannen; Rob Romijnders; Lucas Beyer; Alexander Kolesnikov 003; Joan Puigcerver; Matthias Minderer; Alexander D'Amour; Dan Moldovan; Sylvain Gelly; Neil Houlsby; et al. (CVPR, 2021)

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. - Alexey Dosovitskiy; Lucas Beyer; Alexander Kolesnikov 003; Dirk Weissenborn; Xiaohua Zhai; Thomas Unterthiner; Mostafa Dehghani 001; Matthias Minderer; Georg Heigold; Sylvain Gelly; Jakob Uszkoreit; Neil Houlsby (ICLR, 2021)

Scalable Transfer Learning with Expert Models. - Joan Puigcerver; Carlos Riquelme Ruiz; Basil Mustafa; Cédric Renggli; André Susano Pinto; Sylvain Gelly; Daniel Keysers; Neil Houlsby (ICLR, 2021)

A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. - Vincent Dumoulin; Neil Houlsby; Utku Evci; Xiaohua Zhai; Ross Goroshin; Sylvain Gelly; Hugo Larochelle (NeurIPS Datasets and Benchmarks, 2021)

Revisiting the Calibration of Modern Neural Networks. - Matthias Minderer; Josip Djolonga; Rob Romijnders; Frances Hubis; Xiaohua Zhai; Neil Houlsby; Dustin Tran; Mario Lucic (NeurIPS, 2021)

Scaling Vision with Sparse Mixture of Experts. - Carlos Riquelme; Joan Puigcerver; Basil Mustafa; Maxim Neumann; Rodolphe Jenatton; André Susano Pinto; Daniel Keysers; Neil Houlsby (NeurIPS, 2021)

MLP-Mixer: An all-MLP Architecture for Vision. - Ilya O. Tolstikhin; Neil Houlsby; Alexander Kolesnikov 003; Lucas Beyer; Xiaohua Zhai; Thomas Unterthiner; Jessica Yung; Andreas Steiner 001; Daniel Keysers; Jakob Uszkoreit; Mario Lucic; Alexey Dosovitskiy (NeurIPS, 2021)

Representation learning from videos in-the-wild: An object-centric approach. - Rob Romijnders; Aravindh Mahendran; Michael Tschannen; Josip Djolonga; Marvin Ritter; Neil Houlsby; Mario Lucic (WACV, 2021)

Supervised Transfer Learning at Scale for Medical Imaging. - Basil Mustafa; Aaron Loh; Jan Freyberg; Patricia MacWilliams; Megan Wilson; Scott Mayer McKinney; Marcin Sieniek; Jim Winkens; Yuan Liu; Peggy Bui; Shruthi Prabhakara; Umesh Telang; et al. (CoRR, 2021)

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. - Vincent Dumoulin; Neil Houlsby; Utku Evci; Xiaohua Zhai; Ross Goroshin; Sylvain Gelly; Hugo Larochelle (CoRR, 2021)

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size. - Jessica Yung; Rob Romijnders; Alexander Kolesnikov 003; Lucas Beyer; Josip Djolonga; Neil Houlsby; Sylvain Gelly; Mario Lucic; Xiaohua Zhai (CoRR, 2021)

The Benchmark Lottery. - Mostafa Dehghani 001; Yi Tay; Alexey A. Gritsenko; Zhe Zhao 001; Neil Houlsby; Fernando Diaz 001; Donald Metzler; Oriol Vinyals (CoRR, 2021)

Guest Editorial: Automated Machine Learning. - Hugo Jair Escalante; Quanming Yao; Wei-Wei Tu; Nelishia Pillay; Rong Qu; Yang Yu 001; Neil Houlsby (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Scaling Vision Transformers. - Xiaohua Zhai; Alexander Kolesnikov 003; Neil Houlsby; Lucas Beyer (CVPR, 2022)

Simple Open-Vocabulary Object Detection. - Matthias Minderer; Alexey A. Gritsenko; Austin Stone; Maxim Neumann; Dirk Weissenborn; Alexey Dosovitskiy; Aravindh Mahendran; Anurag Arnab; Mostafa Dehghani 001; Zhuoran Shen; Xiao Wang 038; Xiaohua Zhai; et al. (ECCV (10), 2022)

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes. - Alexander Kolesnikov 003; André Susano Pinto; Lucas Beyer; Xiaohua Zhai; Jeremiah Harmsen; Neil Houlsby (NeurIPS, 2022)

Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts. - Basil Mustafa; Carlos Riquelme; Joan Puigcerver; Rodolphe Jenatton; Neil Houlsby (NeurIPS, 2022)

Learning to Merge Tokens in Vision Transformers. - Cédric Renggli; André Susano Pinto; Neil Houlsby; Basil Mustafa; Joan Puigcerver; Carlos Riquelme (CoRR, 2022)

On the surprising tradeoff between ImageNet accuracy and perceptual similarity. - Manoj Kumar; Neil Houlsby; Nal Kalchbrenner; Ekin D. Cubuk (CoRR, 2022)

Unifying Language Learning Paradigms. - Yi Tay; Mostafa Dehghani 001; Vinh Q. Tran 002; Xavier Garcia; Dara Bahri; Tal Schuster; Huaixiu Steven Zheng; Neil Houlsby; Donald Metzler (CoRR, 2022)

Simple Open-Vocabulary Object Detection with Vision Transformers. - Matthias Minderer; Alexey A. Gritsenko; Austin Stone; Maxim Neumann; Dirk Weissenborn; Alexey Dosovitskiy; Aravindh Mahendran; Anurag Arnab; Mostafa Dehghani 001; Zhuoran Shen; Xiao Wang 038; Xiaohua Zhai; et al. (CoRR, 2022)

Robust and Efficient Medical Imaging with Self-Supervision. - Shekoofeh Azizi; Laura Culp; Jan Freyberg; Basil Mustafa; Sebastien Baur; Simon Kornblith; Ting Chen 007; Patricia MacWilliams; S. Sara Mahdavi; Ellery Wulczyn; Boris Babenko; Megan Wilson; et al. (CoRR, 2022)

PaLI: A Jointly-Scaled Multilingual Language-Image Model. - Xi Chen 071; Xiao Wang 038; Soravit Changpinyo; A. J. Piergiovanni; Piotr Padlewski; Daniel Salz; Sebastian Goodman; Adam Grycner; Basil Mustafa; Lucas Beyer; Alexander Kolesnikov 003; Joan Puigcerver; et al. (CoRR, 2022)

Location-Aware Self-Supervised Transformers. - Mathilde Caron; Neil Houlsby; Cordelia Schmid (CoRR, 2022)

Image-and-Language Understanding from Pixels Only. - Michael Tschannen; Basil Mustafa; Neil Houlsby (CoRR, 2022)

Underspecification Presents Challenges for Credibility in Modern Machine Learning. - Alexander D'Amour; Katherine A. Heller; Dan Moldovan; Ben Adlam; Babak Alipanahi; Alex Beutel; Christina Chen; Jonathan Deaton; Jacob Eisenstein; Matthew D. Hoffman; Farhad Hormozdiari; Neil Houlsby; et al. (J. Mach. Learn. Res., 2022)

Sparse MoEs meet Efficient Ensembles. - James Urquhart Allingham; Florian Wenzel; Zelda E. Mariet; Basil Mustafa; Joan Puigcerver; Neil Houlsby; Ghassen Jerfel; Vincent Fortuin; Balaji Lakshminarayanan; Jasper Snoek; Dustin Tran; Carlos Riquelme Ruiz; et al. (Trans. Mach. Learn. Res., 2022)

Do better ImageNet classifiers assess perceptual similarity better? - Manoj Kumar; Neil Houlsby; Nal Kalchbrenner; Ekin Dogus Cubuk (Trans. Mach. Learn. Res., 2022)

CLIPPO: Image-and-Language Understanding from Pixels Only. - Michael Tschannen; Basil Mustafa; Neil Houlsby (CVPR, 2023)

Transcending Scaling Laws with 0.1% Extra Compute. - Yi Tay; Jason Wei; Hyung Won Chung; Vinh Q. Tran 002; David R. So; Siamak Shakeri; Xavier Garcia; Huaixiu Steven Zheng; Jinfeng Rao; Aakanksha Chowdhery; Denny Zhou; Donald Metzler; et al. (EMNLP, 2023)

Massively Scaling Heteroscedastic Classifiers. - Mark Collier; Rodolphe Jenatton; Basil Mustafa; Neil Houlsby; Jesse Berent; Effrosyni Kokiopoulou (ICLR, 2023)

Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints. - Aran Komatsuzaki; Joan Puigcerver; James Lee-Thorp; Carlos Riquelme Ruiz; Basil Mustafa; Joshua Ainslie; Yi Tay; Mostafa Dehghani 001; Neil Houlsby (ICLR, 2023)

UL2: Unifying Language Learning Paradigms. - Yi Tay; Mostafa Dehghani 001; Vinh Q. Tran 002; Xavier Garcia; Jason Wei; Xuezhi Wang 002; Hyung Won Chung; Dara Bahri; Tal Schuster; Huaixiu Steven Zheng; Denny Zhou; Neil Houlsby; et al. (ICLR, 2023)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

Adaptive Computation with Elastic Input Sequence. - Fuzhao Xue; Valerii Likhosherstov; Anurag Arnab; Neil Houlsby; Mostafa Dehghani 001; Yang You 001 (ICML, 2023)

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. - Mostafa Dehghani 001; Basil Mustafa; Josip Djolonga; Jonathan Heek; Matthias Minderer; Mathilde Caron; Andreas Steiner 001; Joan Puigcerver; Robert Geirhos; Ibrahim M. Alabdulmohsin; Avital Oliver; Piotr Padlewski; et al. (NeurIPS, 2023)

Scaling Open-Vocabulary Object Detection. - Matthias Minderer; Alexey A. Gritsenko; Neil Houlsby (NeurIPS, 2023)

Image Captioners Are Scalable Vision Learners Too. - Michael Tschannen; Manoj Kumar; Andreas Steiner 001; Xiaohua Zhai; Neil Houlsby; Lucas Beyer (NeurIPS, 2023)

PaLI-X: On Scaling up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CoRR, 2023)

Dual PatchNorm. - Manoj Kumar; Mostafa Dehghani 001; Neil Houlsby (Trans. Mach. Learn. Res., 2023)

Frozen Feature Augmentation for Few-Shot Image Classification. - Andreas Bär; Neil Houlsby; Mostafa Dehghani 001; Manoj Kumar (CVPR, 2024)

On Scaling Up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CVPR, 2024)

Scaling Laws for Sparsely-Connected Foundation Models. - Elias Frantar; Carlos Riquelme Ruiz; Neil Houlsby; Dan Alistarh; Utku Evci (ICLR, 2024)

From Sparse to Soft Mixtures of Experts. - Joan Puigcerver; Carlos Riquelme Ruiz; Basil Mustafa; Neil Houlsby (ICLR, 2024)

SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation. - Jonathan Roberts 004; Kai Han 001; Neil Houlsby; Samuel Albanie (NeurIPS, 2024)

Location-Aware Self-Supervised Transformers for Semantic Segmentation. - Mathilde Caron; Neil Houlsby; Cordelia Schmid (WACV, 2024)

Capabilities of Gemini Models in Medicine. - Khaled Saab 003; Tao Tu 001; Wei-Hung Weng; Ryutaro Tanno; David Stutz; Ellery Wulczyn; Fan Zhang; Tim Strother; Chunjong Park; Elahe Vedadi; Juanma Zambrano Chaves; Szu-Yeu Hu; et al. (CoRR, 2024)

Semantica: An Adaptable Image-Conditioned Diffusion Model. - Manoj Kumar; Neil Houlsby; Emiel Hoogeboom (CoRR, 2024)

PaliGemma: A versatile 3B VLM for transfer. - Lucas Beyer; Andreas Steiner 001; André Susano Pinto; Alexander Kolesnikov 003; Xiao Wang 038; Daniel Salz; Maxim Neumann; Ibrahim Alabdulmohsin; Michael Tschannen; Emanuele Bugliarello; Thomas Unterthiner; Daniel Keysers; et al. (CoRR, 2024)

Compression Scaling Laws:Unifying Sparsity and Quantization. - Elias Frantar; Utku Evci; Wonpyo Park; Neil Houlsby; Dan Alistarh (CoRR, 2025)