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Year Published

69 Results

April 3, 2017

Detecting Large Reshare Cascades in Social Networks

International Conference on World Wide Web

In this paper, we propose SansNet, a network agnostic approach towards detecting large reshare cascades in online social networks.

By: Karthik Subbian, B. Aditya Prakash, Lada Adamic
November 1, 2016

Neural Text Generation from Structured Data with Application to the Biography Domain

Empirical Methods in Natural Language Processing (EMNLP)

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.

By: Remi Lebret, David Grangier, Michael Auli
October 28, 2016

Bilingual Methods for Adaptive Training Data Selection for Machine Translation

Association for the Machine Translation in Americas

We propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual

By: Boxing Chen, Roland Kuhn, George Foster, Colin Cherry, Fei Huang
October 10, 2016

Learning to Refine Object Segments

European Conference on Computer Vision

In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.

By: Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollar
September 18, 2016

A MultiPath Network for Object Detection


We test three modifications to the standard Fast R-CNN object detector to determine if they can overcome the object detection challenges in a COCO object detection dataset.

By: Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollar
August 16, 2016

Synergy of Monotonic Rules


This article describes a method for constructing a special rule (we call it synergy rule) that uses as its input information the outputs (scores) of several monotonic rules which solve the same pattern recognition problem.

By: Vladimir Vapnik, Rauf Izmailov
August 11, 2016

Semi-Supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data

Conference on Natural Language Learning

We propose a method which uses semi-supervised convolutional neural networks (CNNs) to select in-domain training data for statistical machine translation.

By: Boxing Chen, Fei Huang
June 26, 2016

End-to-End Voxel-to-Voxel Prediction

Conference on Computer Vision and Pattern Recognition (CVPR)

Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis with most successful applications having been in the area of video classification and detection. In this paper we challenge these views by presenting a deep 3D convolutional architecture trained end to end to perform voxel-level prediction, i.e., to output a variable at every voxel of the video.

By: Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri
June 8, 2016

Key-Value Memory Networks for Directly Reading Documents

EMNLP 2016

This paper introduces a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation.

By: Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, Jason Weston
May 2, 2016

Deep Multi-Scale Video Prediction Beyond Mean Square Error

ICLR 2016

The paper is about predicting future frames in video sequences given the previous frames.

By: Michael Mathieu, Camille Couprie, Yann LeCun