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Year Published

56 Results

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

BMVC

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

JMLR

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
May 2, 2016

Metric Learning with Adaptive Density Discrimination

ICLR

Distance metric learning approaches learn a transformation to a representation space in which distance is in correspondence with a predefined notion of similarity.

By: Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
September 17, 2015

Improved Arabic Dialect Classification with Social Media Data

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their combination yields significant and consistent improvement on two different test sets.

By: Fei Huang
June 12, 2015

Web-Scale Training for Face Identification

The IEEE Conference on Computer Vision and Pattern Recognition

We study face recognition and show that three distinct properties have surprising effects on the transferability of deep convolutional networks (CNN)

By: Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf