229 citations · 239 across the 5 of their papers we have counts for
7 papers
Neural Program Synthesis with a Differentiable Fixer
Matej Balog, Rishabh Singh, Petros Maniatis +1
We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer. Our approach is inspired from the fac…
Fast Training of Sparse Graph Neural Networks on Dense Hardware
Matej Balog, Bart van Merriënboer, Subhodeep Moitra +2
Graph neural networks have become increasingly popular in recent years due to their ability to naturally encode relational input data and their ability to scale to large graphs by…
Differentially Private Database Release via Kernel Mean Embeddings
Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf
We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the pri…
Lost Relatives of the Gumbel Trick
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani +1
The Gumbel trick is a method to sample from a discrete probability distribution, or to estimate its normalizing partition function. The method relies on repeatedly applying a rando…
DeepCoder: Learning to Write Programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt +2
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to pr…
The Mondrian Kernel
Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani +2
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selec…