Search Results for author: Karol Gregor

Found 24 papers, 9 papers with code

Evolving Neural Update Rules for Sequence Learning

no code implementations29 Sep 2021 Karol Gregor, Peter Conway Humphreys

We consider the problem of searching, end to end, for effective weight and activation update rules governing online learning of a recurrent network on problems of character sequence memorisation and prediction.

Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm

no code implementations19 Jan 2021 Karol Gregor, Frederic Besse

We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms.

Artificial Life

Finding online neural update rules by learning to remember

no code implementations6 Mar 2020 Karol Gregor

We investigate learning of the online local update rules for neural activations (bodies) and weights (synapses) from scratch.


Learning Attractor Dynamics for Generative Memory

1 code implementation NeurIPS 2018 Yan Wu, Greg Wayne, Karol Gregor, Timothy Lillicrap

Based on the idea of memory writing as inference, as proposed in the Kanerva Machine, we show that a likelihood-based Lyapunov function emerges from maximising the variational lower-bound of a generative memory.


Temporal Difference Variational Auto-Encoder

1 code implementation ICLR 2019 Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber

To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction.

reinforcement-learning Reinforcement Learning (RL)

Variational Intrinsic Control

1 code implementation22 Nov 2016 Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra

In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent.

Reinforcement Learning (RL) Unsupervised Reinforcement Learning

What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?

no code implementations5 Jun 2016 Kevin Jarrett, Koray Kvukcuoglu, Karol Gregor, Yann Lecun

We also introduce a new single phase supervised learning procedure that places an L1 penalty on the output state of each layer of the network.

Object Recognition Unsupervised Pre-training

Neural Autoregressive Distribution Estimation

3 code implementations7 May 2016 Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle

We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density estimation.

Density Estimation Image Generation

Towards Conceptual Compression

1 code implementation NeurIPS 2016 Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra

We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling.

Ranked #61 on Image Generation on CIFAR-10 (bits/dimension metric)

Image Generation

One-Shot Generalization in Deep Generative Models

no code implementations16 Mar 2016 Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra

In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept.

BIG-bench Machine Learning Density Estimation +1

Towards Principled Unsupervised Learning

no code implementations19 Nov 2015 Ilya Sutskever, Rafal Jozefowicz, Karol Gregor, Danilo Rezende, Tim Lillicrap, Oriol Vinyals

Supervised learning is successful because it can be solved by the minimization of the training error cost function.

Domain Adaptation

DRAW: A Recurrent Neural Network For Image Generation

20 code implementations16 Feb 2015 Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.

Ranked #67 on Image Generation on CIFAR-10 (bits/dimension metric)

Foveation Image Generation

MADE: Masked Autoencoder for Distribution Estimation

16 code implementations12 Feb 2015 Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples.

Density Estimation Image Generation

Neural Variational Inference and Learning in Belief Networks

2 code implementations31 Jan 2014 Andriy Mnih, Karol Gregor

Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and none of the approximate inference methods that have been applied to them scale well.

Variational Inference

Deep AutoRegressive Networks

no code implementations31 Oct 2013 Karol Gregor, Ivo Danihelka, andriy mnih, Charles Blundell, Daan Wierstra

We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data.

Atari Games

A lattice filter model of the visual pathway

no code implementations NeurIPS 2012 Karol Gregor, Dmitri B. Chklovskii

Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals.

Structured sparse coding via lateral inhibition

no code implementations NeurIPS 2011 Arthur D. Szlam, Karol Gregor, Yann L. Cun

This work describes a conceptually simple method for structured sparse coding and dictionary design.

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