Search Results for author: Max Jaderberg

Found 27 papers, 11 papers with code

Faster Improvement Rate Population Based Training

no code implementations28 Sep 2021 Valentin Dalibard, Max Jaderberg

Our experiments show that FIRE PBT is able to outperform PBT on the ImageNet benchmark and match the performance of networks that were trained with a hand-tuned learning rate schedule.

Perception-Prediction-Reaction Agents for Deep Reinforcement Learning

no code implementations26 Jun 2020 Adam Stooke, Valentin Dalibard, Siddhant M. Jayakumar, Wojciech M. Czarnecki, Max Jaderberg

We employ a temporal hierarchy, using a slow-ticking recurrent core to allow information to flow more easily over long time spans, and three fast-ticking recurrent cores with connections designed to create an information asymmetry.

reinforcement-learning

A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern

no code implementations16 Dec 2019 Wojciech Marian Czarnecki, Simon Osindero, Razvan Pascanu, Max Jaderberg

The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Skorokhodov and Burtsev, 2019) demonstrated that one can empirically find arbitrary 2D binary patterns inside loss surfaces of popular neural networks.

Stabilizing Transformers for Reinforcement Learning

5 code implementations ICML 2020 Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant M. Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell

Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting.

General Reinforcement Learning Language Modelling +2

Distilling Policy Distillation

no code implementations6 Feb 2019 Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg

The transfer of knowledge from one policy to another is an important tool in Deep Reinforcement Learning.

reinforcement-learning

A Generalized Framework for Population Based Training

no code implementations5 Feb 2019 Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, Pramod Gupta

Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training.

Open-ended Learning in Symmetric Zero-sum Games

no code implementations23 Jan 2019 David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel

Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.

Mix & Match - Agent Curricula for Reinforcement Learning

no code implementations ICML 2018 Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu

We introduce Mix and match (M&M) – a training framework designed to facilitate rapid and effective learning in RL agents that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents.

reinforcement-learning

Mix&Match - Agent Curricula for Reinforcement Learning

no code implementations5 Jun 2018 Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu

(2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state.

reinforcement-learning

Population Based Training of Neural Networks

6 code implementations27 Nov 2017 Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, Koray Kavukcuoglu

Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm.

Machine Translation Model Selection

Grounded Language Learning in a Simulated 3D World

1 code implementation20 Jun 2017 Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, Phil Blunsom

Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.

Grounded language learning

Sobolev Training for Neural Networks

no code implementations NeurIPS 2017 Wojciech Marian Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Świrszcz, Razvan Pascanu

In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have access to derivatives of the target output with respect to the input - for example when the ground truth function is itself a neural network such as in network compression or distillation.

Understanding Synthetic Gradients and Decoupled Neural Interfaces

1 code implementation ICML 2017 Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu

When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking - without waiting for a true error gradient to be backpropagated - resulting in Decoupled Neural Interfaces (DNIs).

Reinforcement Learning with Unsupervised Auxiliary Tasks

3 code implementations16 Nov 2016 Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu

We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task.

reinforcement-learning

Decoupled Neural Interfaces using Synthetic Gradients

4 code implementations ICML 2017 Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu

Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates.

Spatial Transformer Networks

44 code implementations NeurIPS 2015 Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu

Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner.

Translation

Deep Structured Output Learning for Unconstrained Text Recognition

no code implementations18 Dec 2014 Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

We develop a representation suitable for the unconstrained recognition of words in natural images: the general case of no fixed lexicon and unknown length.

Language Modelling Multi-Task Learning

Reading Text in the Wild with Convolutional Neural Networks

no code implementations4 Dec 2014 Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

In this work we present an end-to-end system for text spotting -- localising and recognising text in natural scene images -- and text based image retrieval.

Image Retrieval Region Proposal +2

Speeding up Convolutional Neural Networks with Low Rank Expansions

no code implementations15 May 2014 Max Jaderberg, Andrea Vedaldi, Andrew Zisserman

The focus of this paper is speeding up the evaluation of convolutional neural networks.

Model Compression

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