José Miguel Hernández-Lobato

University of Cambridge
0000-0001-7610-949X
https://jmhl.org
Publications145
h-index37
Citations7,583
Highly Influential Citations741
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration…
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
TLDR
This work presents a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP), which works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients.
View PDF on arXiv
Grammar Variational Autoencoder
TLDR
Surprisingly, it is shown that not only does the model more often generate valid outputs, it also learns a more coherent latent space in which nearby points decode to similar discrete outputs.
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Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
TLDR
This work proposes a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES), which codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution.
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Predictive Entropy Search for Multi-objective Bayesian Optimization
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Deep Gaussian Processes for Regression using Approximate Expectation Propagation
TLDR
A new approximate Bayesian learning scheme is developed that enables DGPs to be applied to a range of medium to large scale regression problems for the first time and is almost always better than state-of-the-art deterministic and sampling-based approximate inference methods for Bayesian neural networks.
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Probabilistic Matrix Factorization with Non-random Missing Data
TLDR
A probabilistic matrix factorization model for collaborative filtering that learns from data that is missing not at random (MNAR) to obtain improved performance over state-of-the-art methods when predicting the ratings and when modeling the data observation process.
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Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators
The continued success of Deep Neural Networks (DNNs) in classification tasks has sparked a trend of accelerating their execution with specialized hardware. While published designs easily give an…
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EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
TLDR
In EDDI, a novel partial variational autoencoder to predict missing data entries problematically given any subset of the observed ones, and combine it with an acquisition function that maximizes expected information gain on a set of target variables is proposed.
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GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
TLDR
This work evaluates the performance of GANs based on recurrent neural networks with Gumbel-softmax output distributions in the task of generating sequences of discrete elements with a continuous approximation to a multinomial distribution parameterized in terms of the softmax function.
View PDF on arXiv
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