Marta Garnelo | DeepAI

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∙ 05/17/2023

Exploring the Space of Key-Value-Query Models with Intention

Attention-based models have been a key element of many recent breakthrou...

0 Marta Garnelo, et al. ∙

∙ 10/08/2021

Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity

Strategic diversity is often essential in games: in multi-player games, ...

0 Marta Garnelo, et al. ∙

∙ 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. ∙

∙ 06/08/2021

Time-series Imputation of Temporally-occluded Multiagent Trajectories

In multiagent environments, several decision-making individuals interact...

16 Shayegan Omidshafiei, et al. ∙

∙ 11/18/2020

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning ...

11 Karl Tuyls, et al. ∙

∙ 07/17/2020

AlignNet: Unsupervised Entity Alignment

Recently developed deep learning models are able to learn to segment sce...

35 Antonia Creswell, et al. ∙

∙ 02/14/2020

Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets

Adversarial training, a special case of multi-objective optimization, is...

0 Gauthier Gidel, et al. ∙

∙ 07/11/2019

A Neural Architecture for Designing Truthful and Efficient Auctions

Auctions are protocols to allocate goods to buyers who have preferences ...

0 Andrea Tacchetti, et al. ∙

∙ 05/24/2019

An Explicitly Relational Neural Network Architecture

With a view to bridging the gap between deep learning and symbolic AI, w...

13 Murray Shanahan, et al. ∙

∙ 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. ∙

∙ 02/07/2019

Adaptive Posterior Learning: few-shot learning with a surprise-based memory module

The ability to generalize quickly from few observations is crucial for i...

0 Tiago Ramalho, et al. ∙

∙ 01/23/2019

Open-ended Learning in Symmetric Zero-sum Games

Zero-sum games such as chess and poker are, abstractly, functions that e...

16 David Balduzzi, et al. ∙

∙ 01/17/2019

Attentive Neural Processes

Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by le...

10 Hyunjik Kim, et al. ∙

∙ 12/06/2018

Verification of deep probabilistic models

Probabilistic models are a critical part of the modern deep learning too...

0 Krishnamurthy Dvijotham, et al. ∙

∙ 07/05/2018

Consistent Generative Query Networks

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

6 Ananya Kumar, et al. ∙

∙ 07/04/2018

Neural Processes

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

6 Marta Garnelo, et al. ∙

∙ 07/04/2018

Conditional Neural Processes

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

2 Marta Garnelo, et al. ∙

∙ 11/08/2016

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

We study a variant of the variational autoencoder model (VAE) with a Gau...

0 Nat Dilokthanakul, et al. ∙

∙ 09/18/2016

Towards Deep Symbolic Reinforcement Learning

Deep reinforcement learning (DRL) brings the power of deep neural networ...

0 Marta Garnelo, et al. ∙

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