S. M. Ali Eslami

research

∙ 10/12/2022

Self-supervised video pretraining yields strong image representations

Videos contain far more information than still images and hold the poten...

0 Nikhil Parthasarathy, et al. ∙

research

∙ 01/28/2022

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

It is common practice in deep learning to represent a measurement of the...

0 Emilien Dupont, et al. ∙

research

∙ 06/26/2021

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs

Cryo-electron microscopy (cryo-EM) has revolutionized experimental prote...

0 Dan Rosenbaum, et al. ∙

research

∙ 06/25/2021

Multimodal Few-Shot Learning with Frozen Language Models

When trained at sufficient scale, auto-regressive language models exhibi...

0 Maria Tsimpoukelli, et al. ∙

research

∙ 05/25/2021

From Motor Control to Team Play in Simulated Humanoid Football

Intelligent behaviour in the physical world exhibits structure at multip...

2 Siqi Liu, et al. ∙

research

∙ 05/01/2021

Generative Art Using Neural Visual Grammars and Dual Encoders

Whilst there are perhaps only a few scientific methods, there seem to be...

42 Chrisantha Fernando, et al. ∙

research

∙ 07/10/2020

Contrastive Training for Improved Out-of-Distribution Detection

Reliable detection of out-of-distribution (OOD) inputs is increasingly u...

3 Jim Winkens, et al. ∙

research

∙ 02/23/2020

PolyGen: An Autoregressive Generative Model of 3D Meshes

Polygon meshes are an efficient representation of 3D geometry, and are o...

17 Charlie Nash, et al. ∙

research

∙ 10/02/2019

Unsupervised Doodling and Painting with Improved SPIRAL

We investigate using reinforcement learning agents as generative models ...

13 John F. J. Mellor, et al. ∙

research

∙ 05/30/2019

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

Medical imaging only indirectly measures the molecular identity of the t...

5 Simon A. A. Kohl, et al. ∙

research

∙ 05/22/2019

Data-Efficient Image Recognition with Contrastive Predictive Coding

Large scale deep learning excels when labeled images are abundant, yet d...

3 Olivier J. Hénaff, et al. ∙

research

∙ 03/28/2019

Meta-Learning surrogate models for sequential decision making

Meta-learning methods leverage past experience to learn data-driven indu...

22 Alexandre Galashov, et al. ∙

research

∙ 07/05/2018

Consistent Generative Query Networks

Stochastic video prediction is usually framed as an extrapolation proble...

6 Ananya Kumar, et al. ∙

research

∙ 07/04/2018

Encoding Spatial Relations from Natural Language

Natural language processing has made significant inroads into learning t...

2 Tiago Ramalho, et al. ∙

research

∙ 07/04/2018

Learning models for visual 3D localization with implicit mapping

We propose a formulation of visual localization that does not require co...

0 Dan Rosenbaum, et al. ∙

research

∙ 07/04/2018

Neural Processes

A neural network (NN) is a parameterised function that can be tuned via ...

6 Marta Garnelo, et al. ∙

research

∙ 07/04/2018

Conditional Neural Processes

Deep neural networks excel at function approximation, yet they are typic...

2 Marta Garnelo, et al. ∙

research

∙ 06/13/2018

A Probabilistic U-Net for Segmentation of Ambiguous Images

Many real-world vision problems suffer from inherent ambiguities. In cli...

0 Simon A. A. Kohl, et al. ∙

research

∙ 04/25/2018

Generative Temporal Models with Spatial Memory for Partially Observed Environments

In model-based reinforcement learning, generative and temporal models of...

0 Marco Fraccaro, et al. ∙

research

∙ 04/03/2018

Synthesizing Programs for Images using Reinforced Adversarial Learning

Advances in deep generative networks have led to impressive results in r...

0 Yaroslav Ganin, et al. ∙

research

∙ 03/10/2018

Kickstarting Deep Reinforcement Learning

We present a method for using previously-trained 'teacher' agents to kic...

0 Simon Schmitt, et al. ∙

research

∙ 02/21/2018

Machine Theory of Mind

Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'...

0 Neil C. Rabinowitz, et al. ∙

research

∙ 02/08/2018

Learning and Querying Fast Generative Models for Reinforcement Learning

A key challenge in model-based reinforcement learning (RL) is to synthes...

0 Lars Buesing, et al. ∙

research

∙ 10/27/2017

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions

Deep autoregressive models have shown state-of-the-art performance in de...

0 Scott Reed, et al. ∙

research

∙ 07/07/2017

Emergence of Locomotion Behaviours in Rich Environments

The reinforcement learning paradigm allows, in principle, for complex be...

0 Nicolas Heess, et al. ∙

research

∙ 07/03/2016

Unsupervised Learning of 3D Structure from Images

A key goal of computer vision is to recover the underlying 3D structure ...

0 Danilo Jimenez Rezende, et al. ∙

research

∙ 03/28/2016

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

We present a framework for efficient inference in structured image model...

0 S. M. Ali Eslami, et al. ∙

research

∙ 03/09/2015

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

We propose an efficient nonparametric strategy for learning a message op...

0 Wittawat Jitkrittum, et al. ∙

research

∙ 10/27/2014

Consensus Message Passing for Layered Graphical Models

Generative models provide a powerful framework for probabilistic reasoni...

0 Varun Jampani, et al. ∙

S. M. Ali Eslami

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