A
Alban Desmaison
Researcher at University of Oxford
Publications - 28
Citations - 36824
Alban Desmaison is an academic researcher from University of Oxford. The author has contributed to research in topics: Conditional random field & Graphical model. The author has an hindex of 12, co-authored 26 publications receiving 21773 citations. Previous affiliations of Alban Desmaison include École Centrale Paris.
Papers
More filters
Automatic differentiation in PyTorch
Adam Paszke,Sam Gross,Soumith Chintala,Gregory Chanan,Edward Z. Yang,Zachary DeVito,Zeming Lin,Alban Desmaison,Luca Antiga,Adam Lerer +9 more
TL;DR: An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead.
Posted Content
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke,Sam Gross,Francisco Massa,Adam Lerer,James Bradbury,Gregory Chanan,Trevor Killeen,Zeming Lin,Natalia Gimelshein,Luca Antiga,Alban Desmaison,Andreas Kopf,Edward Z. Yang,Zachary DeVito,Martin Raison,Alykhan Tejani,Sasank Chilamkurthy,Benoit Steiner,Lu Fang,Junjie Bai,Soumith Chintala +20 more
TL;DR: PyTorch as discussed by the authors is a machine learning library that provides an imperative and Pythonic programming style that makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs.
Proceedings Article
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke,Sam Gross,Francisco Massa,Adam Lerer,James Bradbury,Gregory Chanan,Trevor Killeen,Zeming Lin,Natalia Gimelshein,Luca Antiga,Alban Desmaison,Andreas Kopf,Edward Z. Yang,Zachary DeVito,Martin Raison,Alykhan Tejani,Sasank Chilamkurthy,Benoit Steiner,Lu Fang,Junjie Bai,Soumith Chintala +20 more
TL;DR: This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.
Proceedings ArticleDOI
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth,Brooks Paige,Brooks Paige,Jan-Willem van de Meent,Alban Desmaison,Noah D. Goodman,Pushmeet Kohli,Frank Wood,Philip H. S. Torr +8 more
TL;DR: The authors propose to learn disentangled representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder, which allows to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks for the remaining variables.
Posted Content
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth,Brooks Paige,Brooks Paige,Jan-Willem van de Meent,Alban Desmaison,Noah D. Goodman,Pushmeet Kohli,Frank Wood,Philip H. S. Torr +8 more
TL;DR: The authors propose to learn disentangled representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder, which allows to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks for the remaining variables.