6 citations · 11 across the 3 of their papers we have counts for
7 papers
Continuous Mixtures of Tractable Probabilistic Models
Alvaro H. C. Correia, Gennaro Gala, Erik Quaeghebeur +2
Probabilistic models based on continuous latent spaces, such as variational autoencoders, can be understood as uncountable mixture models where components depend continuously on th…
Neural Simulated Annealing
Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan
Simulated annealing (SA) is a stochastic global optimisation technique applicable to a wide range of discrete and continuous variable problems. Despite its simplicity, the developm…
Towards Robust Classification with Deep Generative Forests
Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonet…
Joints in Random Forests
Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist.…
On Pruning for Score-Based Bayesian Network Structure Learning
Alvaro H. C. Correia, James Cussens, Cassio de Campos
Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variabl…
A Fully Attention-Based Information Retriever
Alvaro Henrique Chaim Correia, Jorge Luiz Moreira Silva, Thiago de Castro Martins +1
Recurrent neural networks are now the state-of-the-art in natural language processing because they can build rich contextual representations and process texts of arbitrary length.…