
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment sce...
read it

Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about MixedNash and Love Neural Nets
Adversarial training, a special case of multiobjective optimization, is...
read it

A Neural Architecture for Designing Truthful and Efficient Auctions
Auctions are protocols to allocate goods to buyers who have preferences ...
read it

An Explicitly Relational Neural Network Architecture
With a view to bridging the gap between deep learning and symbolic AI, w...
read it

MetaLearning surrogate models for sequential decision making
Metalearning methods leverage past experience to learn datadriven indu...
read it

Adaptive Posterior Learning: fewshot learning with a surprisebased memory module
The ability to generalize quickly from few observations is crucial for i...
read it

Openended Learning in Symmetric Zerosum Games
Zerosum games such as chess and poker are, abstractly, functions that e...
read it

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

Verification of deep probabilistic models
Probabilistic models are a critical part of the modern deep learning too...
read it

Consistent Generative Query Networks
Stochastic video prediction is usually framed as an extrapolation proble...
read it

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

Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typic...
read it

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
We study a variant of the variational autoencoder model (VAE) with a Gau...
read it

Towards Deep Symbolic Reinforcement Learning
Deep reinforcement learning (DRL) brings the power of deep neural networ...
read it
Marta Garnelo
is this you? claim profile