This page shows the most relevant public items for Awesome List: deep-learning-foundation, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Aug 1, 2015 • arxiv.org • Jan Hosang, Rodrigo Benenson, Piotr Dollár, Bernt Schiele
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and wides...
Paper • Dec 4, 2014 • arxiv.org • Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
In this work we present an end-to-end system for text spotting -- localising and recognising text in natural scene images -- and text based image retrieval. This system is based on a region proposa...
Paper • Mar 27, 2016 • arxiv.org • Justin Johnson, Alexandre Alahi, Li Fei-Fei
We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks usin...
Paper • Jun 7, 2016 • arxiv.org • Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein
We describe a question answering model that applies to both images and structured knowledge bases. The model uses natural language strings to automatically assemble neural networks from a collectio...
Paper • Jan 27, 2017 • arxiv.org • Alexis Conneau, Holger Schwenk, Loïc Barrault, Yann Lecun
The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the ...
Paper • Jun 23, 2016 • arxiv.org • Minh-Thang Luong, Christopher D. Manning
Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words. This paper presents a novel word-cha...
Paper • Aug 8, 2016 • arxiv.org • Danqi Chen, Jason Bolton, Christopher D. Manning
Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is t...
Paper • Aug 25, 2016 • arxiv.org • Gao Huang, Zhuang Liu, Kilian Q. Weinberger
Recent work has shown that convolutional networks can be substantially deeper, more accurate and efficient to train if they contain shorter connections between layers close to the input and those c...
Paper • Feb 21, 2017 • arxiv.org • Alex Graves
This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an ou...
Paper • May 30, 2016 • arxiv.org • Jayanth Koushik
Convoulutional Neural Networks (CNNs) exhibit extraordinary performance on a variety of machine learning tasks. However, their mathematical properties and behavior are quite poorly understood. Ther...
Paper • Jun 2, 2016 • arxiv.org • Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples fr...
Paper • Oct 27, 2016 • arxiv.org • Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio
The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do multi-step sampl...
Paper • May 20, 2016 • arxiv.org • Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictu...
Paper • Feb 22, 2016 • arxiv.org • Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid
Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process ...
Paper • Jan 25, 2017 • www.nature.com • Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, Sebastian Thrun
An artificial intelligence trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists.
Paper • Jun 21, 2016 • arxiv.org • Junyoung Chung, Kyunghyun Cho, Yoshua Bengio
The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation. In this paper, we ask a fundamental ques...
Paper • Apr 23, 2014 • arxiv.org • Yoshua Bengio, Aaron Courville, Pascal Vincent
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the differe...
Paper • Oct 8, 2014 • arxiv.org • Juergen Schmidhuber
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relev...
Paper • Jan 3, 2021 • arxiv.org • Carl Doersch
In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are bu...
Paper • Oct 4, 2017 • arxiv.org • Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the stat...
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