F
Francisco Massa
Researcher at Facebook
Publications - 18
Citations - 30177
Francisco Massa is an academic researcher from Facebook. The author has contributed to research in topics: Convolutional neural network & Object detection. The author has an hindex of 14, co-authored 16 publications receiving 11979 citations. Previous affiliations of Francisco Massa include École des ponts ParisTech & University of Paris.
Papers
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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.
Posted Content
End-to-End Object Detection with Transformers
Nicolas Carion,Francisco Massa,Gabriel Synnaeve,Nicolas Usunier,Alexander Kirillov,Sergey Zagoruyko +5 more
TL;DR: This work presents a new method that views object detection as a direct set prediction problem, and demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset.
Book ChapterDOI
End-to-End Object Detection with Transformers
Nicolas Carion,Francisco Massa,Gabriel Synnaeve,Nicolas Usunier,Alexander Kirillov,Sergey Zagoruyko +5 more
TL;DR: DetR as mentioned in this paper proposes a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture to directly output the final set of predictions in parallel.
Posted Content
Training data-efficient image transformers & distillation through attention
Hugo Touvron,Matthieu Cord,Matthijs Douze,Francisco Massa,Alexandre Sablayrolles,Hervé Jégou +5 more
TL;DR: In this article, a teacher-student strategy was proposed to train a convolution-free transformer on Imagenet only, achieving state-of-the-art performance on ImageNet.