The Shape Boltzmann Machine: A strong model of object shape. - S. M. Ali Eslami; Nicolas Heess; John M. Winn (CVPR, 2012)

A Generative Model for Parts-based Object Segmentation. - S. M. Ali Eslami; Christopher K. I. Williams (NIPS, 2012)

Just-In-Time Learning for Fast and Flexible Inference. - S. M. Ali Eslami; Daniel Tarlow; Pushmeet Kohli; John M. Winn (NIPS, 2014)

The Shape Boltzmann Machine: A Strong Model of Object Shape. - S. M. Ali Eslami; Nicolas Heess; Christopher K. I. Williams; John M. Winn (Int. J. Comput. Vis., 2014)

Consensus Message Passing for Layered Graphical Models. - Varun Jampani; S. M. Ali Eslami; Daniel Tarlow; Pushmeet Kohli; John M. Winn (AISTATS, 2015)

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. - Wittawat Jitkrittum; Arthur Gretton; Nicolas Heess; S. M. Ali Eslami; Balaji Lakshminarayanan; Dino Sejdinovic; Zoltán Szabó 001 (UAI, 2015)

The Pascal Visual Object Classes Challenge: A Retrospective. - Mark Everingham; S. M. Ali Eslami; Luc Van Gool; Christopher K. I. Williams; John M. Winn; Andrew Zisserman (Int. J. Comput. Vis., 2015)

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. - S. M. Ali Eslami; Nicolas Heess; Theophane Weber; Yuval Tassa; David Szepesvari; Koray Kavukcuoglu; Geoffrey E. Hinton (NIPS, 2016)

Unsupervised Learning of 3D Structure from Images. - Danilo Jimenez Rezende; S. M. Ali Eslami; Shakir Mohamed; Peter W. Battaglia; Max Jaderberg; Nicolas Heess (NIPS, 2016)

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. - S. M. Ali Eslami; Nicolas Heess; Theophane Weber; Yuval Tassa; Koray Kavukcuoglu; Geoffrey E. Hinton (CoRR, 2016)

Emergence of Locomotion Behaviours in Rich Environments. - Nicolas Heess; Dhruva TB; Srinivasan Sriram; Jay Lemmon; Josh Merel; Greg Wayne; Yuval Tassa; Tom Erez; Ziyu Wang 001; S. M. Ali Eslami; Martin A. Riedmiller; David Silver (CoRR, 2017)

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. - Scott E. Reed; Yutian Chen 001; Thomas Paine; Aäron van den Oord; S. M. Ali Eslami; Danilo J. Rezende; Oriol Vinyals; Nando de Freitas (ICLR (Poster), 2018)

Generative Temporal Models with Spatial Memory for Partially Observed Environments. - Marco Fraccaro; Danilo Jimenez Rezende; Yori Zwols; Alexander Pritzel; S. M. Ali Eslami; Fabio Viola (ICML, 2018)

Synthesizing Programs for Images using Reinforced Adversarial Learning. - Yaroslav Ganin; Tejas Kulkarni; Igor Babuschkin; S. M. Ali Eslami; Oriol Vinyals (ICML, 2018)

Conditional Neural Processes. - Marta Garnelo; Dan Rosenbaum; Christopher Maddison; Tiago Ramalho; David Saxton; Murray Shanahan; Yee Whye Teh; Danilo Jimenez Rezende; S. M. Ali Eslami (ICML, 2018)

Machine Theory of Mind. - Neil C. Rabinowitz; Frank Perbet; H. Francis Song; Chiyuan Zhang; S. M. Ali Eslami; Matthew M. Botvinick (ICML, 2018)

A Probabilistic U-Net for Segmentation of Ambiguous Images. - Simon Kohl; Bernardino Romera-Paredes; Clemens Meyer; Jeffrey De Fauw; Joseph R. Ledsam; Klaus H. Maier-Hein; S. M. Ali Eslami; Danilo Jimenez Rezende; Olaf Ronneberger (NeurIPS, 2018)

Learning and Querying Fast Generative Models for Reinforcement Learning. - Lars Buesing; Theophane Weber; Sébastien Racanière; S. M. Ali Eslami; Danilo Jimenez Rezende; David P. Reichert; Fabio Viola; Frederic Besse; Karol Gregor; Demis Hassabis; Daan Wierstra (CoRR, 2018)

Kickstarting Deep Reinforcement Learning. - Simon Schmitt; Jonathan J. Hudson; Augustin Zídek; Simon Osindero; Carl Doersch; Wojciech M. Czarnecki; Joel Z. Leibo; Heinrich Küttler; Andrew Zisserman; Karen Simonyan; S. M. Ali Eslami (CoRR, 2018)

Neural Processes. - Marta Garnelo; Jonathan Schwarz; Dan Rosenbaum; Fabio Viola; Danilo J. Rezende; S. M. Ali Eslami; Yee Whye Teh (CoRR, 2018)

Encoding Spatial Relations from Natural Language. - Tiago Ramalho; Tomás Kociský; Frederic Besse; S. M. Ali Eslami; Gábor Melis; Fabio Viola; Phil Blunsom; Karl Moritz Hermann (CoRR, 2018)

Consistent Generative Query Networks. - Ananya Kumar; S. M. Ali Eslami; Danilo J. Rezende; Marta Garnelo; Fabio Viola; Edward Lockhart; Murray Shanahan (CoRR, 2018)

Learning models for visual 3D localization with implicit mapping. - Dan Rosenbaum; Frederic Besse; Fabio Viola; Danilo J. Rezende; S. M. Ali Eslami (CoRR, 2018)

Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning. - Aishwarya Agrawal; Mateusz Malinowski; Felix Hill; S. M. Ali Eslami; Oriol Vinyals; Tejas Kulkarni (CoRR, 2018)

Attentive Neural Processes. - Hyunjik Kim; Andriy Mnih; Jonathan Schwarz; Marta Garnelo; S. M. Ali Eslami; Dan Rosenbaum; Oriol Vinyals; Yee Whye Teh (ICLR (Poster), 2019)

Meta-Learning surrogate models for sequential decision making. - Alexandre Galashov; Jonathan Schwarz; Hyunjik Kim; Marta Garnelo; David Saxton; Pushmeet Kohli; S. M. Ali Eslami; Yee Whye Teh (CoRR, 2019)

Data-Efficient Image Recognition with Contrastive Predictive Coding. - Olivier J. Hénaff; Aravind Srinivas; Jeffrey De Fauw; Ali Razavi; Carl Doersch; S. M. Ali Eslami; Aäron van den Oord (CoRR, 2019)

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities. - Simon A. A. Kohl; Bernardino Romera-Paredes; Klaus H. Maier-Hein; Danilo Jimenez Rezende; S. M. Ali Eslami; Pushmeet Kohli; Andrew Zisserman; Olaf Ronneberger (CoRR, 2019)

Unsupervised Doodling and Painting with Improved SPIRAL. - John F. J. Mellor; Eunbyung Park; Yaroslav Ganin; Igor Babuschkin; Tejas Kulkarni; Dan Rosenbaum; Andy Ballard; Theophane Weber; Oriol Vinyals; S. M. Ali Eslami (CoRR, 2019)

PolyGen: An Autoregressive Generative Model of 3D Meshes. - Charlie Nash; Yaroslav Ganin; S. M. Ali Eslami; Peter W. Battaglia (ICML, 2020)

Contrastive Training for Improved Out-of-Distribution Detection. - Jim Winkens; Rudy Bunel; Abhijit Guha Roy; Robert Stanforth; Vivek Natarajan; Joseph R. Ledsam; Patricia MacWilliams; Pushmeet Kohli; Alan Karthikesalingam; Simon Kohl; A. Taylan Cemgil; S. M. Ali Eslami; et al. (CoRR, 2020)

Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion. - Michael Bloesch; Jan Humplik; Viorica Patraucean; Roland Hafner; Tuomas Haarnoja; Arunkumar Byravan; Noah Yamamoto Siegel; Saran Tunyasuvunakool; Federico Casarini; Nathan Batchelor; Francesco Romano; Stefano Saliceti; et al. (CoRL, 2021)

Multimodal Few-Shot Learning with Frozen Language Models. - Maria Tsimpoukelli; Jacob Menick; Serkan Cabi; S. M. Ali Eslami; Oriol Vinyals; Felix Hill (NeurIPS, 2021)

Generative Art Using Neural Visual Grammars and Dual Encoders. - Chrisantha Fernando; S. M. Ali Eslami; Jean-Baptiste Alayrac; Piotr Mirowski; Dylan Banarse; Simon Osindero (CoRR, 2021)

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs. - Dan Rosenbaum; Marta Garnelo; Michal Zielinski; Charlie Beattie; Ellen Clancy; Andrea Huber; Pushmeet Kohli; Andrew W. Senior; John Jumper; Carl Doersch; S. M. Ali Eslami; Olaf Ronneberger; et al. (CoRR, 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

From data to functa: Your data point is a function and you can treat it like one. - Emilien Dupont; Hyunjik Kim; S. M. Ali Eslami; Danilo Jimenez Rezende; Dan Rosenbaum (ICML, 2022)

From data to functa: Your data point is a function and you should treat it like one. - Emilien Dupont; Hyunjik Kim; S. M. Ali Eslami; Danilo J. Rezende; Dan Rosenbaum (CoRR, 2022)

Self-supervised video pretraining yields strong image representations. - Nikhil Parthasarathy; S. M. Ali Eslami; João Carreira 001; Olivier J. Hénaff (CoRR, 2022)

From motor control to team play in simulated humanoid football. - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech M. Czarnecki; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Sci. Robotics, 2022)

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech Czarnecki 001; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Zenodo, 2022)

Self-supervised video pretraining yields robust and more human-aligned visual representations. - Nikhil Parthasarathy; S. M. Ali Eslami; João Carreira 001; Olivier J. Hénaff (NeurIPS, 2023)

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)

Advancing Multimodal Medical Capabilities of Gemini. - Lin Yang; Shawn Xu; Andrew Sellergren; Timo Kohlberger; Yuchen Zhou; Ira Ktena; Atilla P. Kiraly; Faruk Ahmed; Farhad Hormozdiari; Tiam Jaroensri; Eric Wang; Ellery Wulczyn; et al. (CoRR, 2024)

Advancing Geometry with AI: Multi-agent Generation of Polytopes. - Grzegorz Swirszcz; Adam Zsolt Wagner; Geordie Williamson; Sam Blackwell; Bogdan Georgiev; Alex Davies; S. M. Ali Eslami; Sébastien Racanière; Theophane Weber; Pushmeet Kohli (CoRR, 2025)

Advancing Conversational Diagnostic AI with Multimodal Reasoning. - Khaled Saab 003; Jan Freyberg; Chunjong Park; Tim Strother; Yong Cheng; Wei-Hung Weng; David G. T. Barrett; David Stutz; Nenad Tomasev; Anil Palepu; Valentin Liévin; Yash Sharma; et al. (CoRR, 2025)