This page shows the most relevant public items for Machine Learning, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • May 22, 2014 • arxiv.org • Quoc V. Le, Tomas Mikolov
Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite ...
Paper • Sep 3, 2014 • arxiv.org • Yoon Kim
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with litt...
Paper • Apr 8, 2014 • arxiv.org • Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for...
Paper • Sep 3, 2014 • arxiv.org • Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vec...
Paper • May 19, 2016 • arxiv.org • Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single n...
Paper • Nov 29, 2015 • arxiv.org • Jason Weston, Sumit Chopra, Antoine Bordes
We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. ...
Paper • Sep 20, 2015 • arxiv.org • Minh-Thang Luong, Hieu Pham, Christopher D. Manning
An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little ...
Paper • Feb 11, 2016 • arxiv.org • Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu
In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding. We extend current models to deal with two key chall...
Paper • Apr 19, 2016 • arxiv.org • Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train ...
Paper • Sep 2, 2015 • arxiv.org • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the al...
Paper • Jul 31, 2015 • arxiv.org • Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a de...
Paper • Jun 7, 2016 • arxiv.org • Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together ...
Paper • Apr 23, 2015 • arxiv.org • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224x224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images ...
Paper • Mar 4, 2014 • arxiv.org • Min Lin, Qiang Chen, Shuicheng Yan
We propose a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional la...
Paper • Sep 20, 2013 • arxiv.org • Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio
We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its ...
Paper • Feb 24, 2014 • arxiv.org • Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun
We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implement...
Paper • Nov 5, 2014 • arxiv.org • Ken Chatfield, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
The latest generation of Convolutional Neural Networks (CNN) have achieved impressive results in challenging benchmarks on image recognition and object detection, significantly raising the interest...
Paper • Apr 10, 2015 • arxiv.org • Karen Simonyan, Andrew Zisserman
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of...
Paper • Dec 10, 2015 • arxiv.org • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly...
Paper • Apr 12, 2016 • arxiv.org • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations be...
It ranks public content for Machine Learning using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to Machine Learning topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to Machine Learning topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use the linked entities section to jump to labs, companies, and experts connected to this topic and explore their timelines. This guidance is specific to Machine Learning topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Yes. Use the follow button on this page to subscribe and track new high-signal activity. This guidance is specific to Machine Learning topic page on Attendemia and is written so it still makes sense without reading other sections on the page.