Search Results for author: Alban Desmaison

Found 13 papers, 6 papers with code

Lagrangian Decomposition for Neural Network Verification

2 code implementations24 Feb 2020 Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

Both the algorithms offer three advantages: (i) they yield bounds that are provably at least as tight as previous dual algorithms relying on Lagrangian relaxations; (ii) they are based on operations analogous to forward/backward pass of neural networks layers and are therefore easily parallelizable, amenable to GPU implementation and able to take advantage of the convolutional structure of problems; and (iii) they allow for anytime stopping while still providing valid bounds.

Automatic Differentiation in PyTorch

1 code implementation NIPS 2017 2017 Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.

Dimensionality Reduction General Classification

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

1 code implementation NeurIPS 2017 N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank Wood, Philip H. S. Torr

We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder.

Representation Learning

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

1 code implementation1 Dec 2016 Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr

A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions.

Object Detection Q-Learning +1

Efficient Linear Programming for Dense CRFs

no code implementations CVPR 2017 Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar

To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent.

Semantic Segmentation

Inducing Interpretable Representations with Variational Autoencoders

no code implementations22 Nov 2016 N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr

We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference.

General Classification Variational Inference

Learning to superoptimize programs

no code implementations6 Nov 2016 Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli

This approach involves repeated sampling of modifications to the program from a proposal distribution, which are accepted or rejected based on whether they preserve correctness, and the improvement they achieve.

Efficient Continuous Relaxations for Dense CRF

no code implementations22 Aug 2016 Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

In contrast to the continuous relaxation-based energy minimisation algorithms used for sparse CRFs, the mean-field algorithm fails to provide strong theoretical guarantees on the quality of its solutions.

Semantic Segmentation Variational Inference

Adaptive Neural Compilation

1 code implementation NeurIPS 2016 Rudy Bunel, Alban Desmaison, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

We show that it is possible to compile programs written in a low-level language to a differentiable representation.

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