Search Results for author: S. M. Ali Eslami

Found 31 papers, 17 papers with code

Learning to encode spatial relations from natural language

no code implementations ICLR 2019 Tiago Ramalho, Tomas Kocisky‎, Frederic Besse, S. M. Ali Eslami, Gabor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann

Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.

Natural Language Processing

From data to functa: Your data point is a function and you can treat it like one

no code implementations28 Jan 2022 Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Rezende, Dan Rosenbaum

A powerful continuous alternative is then to represent these measurements using an implicit neural representation, a neural function trained to output the appropriate measurement value for any input spatial location.

Imputation Novel View Synthesis

Multimodal Few-Shot Learning with Frozen Language Models

no code implementations NeurIPS 2021 Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill

When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples.

Few-Shot Learning Language Modelling +2

From Motor Control to Team Play in Simulated Humanoid Football

1 code implementation25 May 2021 SiQi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess

In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.

Decision Making Imitation Learning +2

Generative Art Using Neural Visual Grammars and Dual Encoders

1 code implementation1 May 2021 Chrisantha Fernando, S. M. Ali Eslami, Jean-Baptiste Alayrac, Piotr Mirowski, Dylan Banarse, Simon Osindero

Whilst there are perhaps only a few scientific methods, there seem to be almost as many artistic methods as there are artists.

PolyGen: An Autoregressive Generative Model of 3D Meshes

2 code implementations ICML 2020 Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia

Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development.

3D Shape Generation Surface Reconstruction

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

4 code implementations30 May 2019 Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger

Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.

Inductive Bias Instance Segmentation +2

Consistent Jumpy Predictions for Videos and Scenes

no code implementations ICLR 2019 Ananya Kumar, S. M. Ali Eslami, Danilo Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

3D Scene Reconstruction Video Prediction

Meta-Learning surrogate models for sequential decision making

no code implementations28 Mar 2019 Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh

We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.

Bayesian Optimisation Decision Making +4

Consistent Generative Query Networks

1 code implementation ICLR 2019 Ananya Kumar, S. M. Ali Eslami, Danilo J. Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

3D Scene Reconstruction Video Prediction

Encoding Spatial Relations from Natural Language

2 code implementations4 Jul 2018 Tiago Ramalho, Tomáš Kočiský, Frederic Besse, S. M. Ali Eslami, Gábor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann

Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.

Natural Language Processing

Neural Processes

12 code implementations4 Jul 2018 Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh

A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.

Learning models for visual 3D localization with implicit mapping

1 code implementation4 Jul 2018 Dan Rosenbaum, Frederic Besse, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami

We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels.

Visual Localization

A Probabilistic U-Net for Segmentation of Ambiguous Images

7 code implementations NeurIPS 2018 Simon A. A. 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

To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.

Decision Making Semantic Segmentation

Generative Temporal Models with Spatial Memory for Partially Observed Environments

no code implementations ICML 2018 Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism.

Model-based Reinforcement Learning

Kickstarting Deep Reinforcement Learning

no code implementations10 Mar 2018 Simon Schmitt, Jonathan J. Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, S. M. Ali Eslami

Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance.

reinforcement-learning

Machine Theory of Mind

no code implementations ICML 2018 Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick

We design a Theory of Mind neural network -- a ToMnet -- which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone.

Meta-Learning

Emergence of Locomotion Behaviours in Rich Environments

6 code implementations7 Jul 2017 Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.

reinforcement-learning

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

1 code implementation9 Mar 2015 Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output.

Just-In-Time Learning for Fast and Flexible Inference

no code implementations NeurIPS 2014 S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn

Much of research in machine learning has centered around the search for inference algorithms that are both general-purpose and efficient.

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