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July 21, 2017
Relationship Proposal Networks
Conference on Computer Vision and Pattern Recognition 2017
Image scene understanding requires learning the relationships between objects in the scene. A scene with many objects may have only a few individual interacting objects (e.g., in a party image with many people, only a handful of people might be speaking with each other). To detect all relationships, it would be inefficient to first detect all individual objects and then classify all pairs; not only is the number of all pairs quadratic, but classification requires limited object categories, which is not scalable for real-world images. In this paper we address these challenges by using pairs of related regions in images to train a relationship proposer that at test time produces a manageable number of related regions.
Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang, Ahmed Elgammal
July 21, 2017
Link the head to the “beak”: Zero Shot Learning from Noisy Text Description at Part Precision
CVPR 2017
In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. F
Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, Ahmed ElgammalApril 19, 2017
Joint User-Entity Representation Learning for Event Recommendation in Social Network
2017 IEEE 33rd International Conference on Data Engineering (ICDE)
In this work, we consider the heavy sparseness in both user and event feedback history caused by short lifespans (transiency) of events and user participation patterns in a production event system. We propose to solve the resulting cold-start problems by introducing a joint representation model to project users and events into the same latent space.
Lijun Tang, Eric Yi Liu
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.
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 corpus.us.
Boxing Chen, Roland Kuhn, George Foster, Colin Cherry, Fei HuangOctober 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.
Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr DollarSeptember 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.
Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr DollarAugust 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.
Vladimir Vapnik, Rauf IzmailovAugust 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.
Boxing Che, Fei HuangJune 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.
Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, Jason WestonMay 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.
Michael Mathieu, Camille Couprie, Yann LeCunMay 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.
Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir BourdevSeptember 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.
Fei HuangJune 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)
Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior WolfDecember 19, 2014
Predicting the quality of new contributors to the Facebook crowdsourcing system
Neural Information Processing Systems: Crowdsourcing and Machine Learning Workshop
We are interested in improving the quality and coverage of a knowledge graph through crowdsourcing features built into a social networking service. This work presents an approach to model user trust when prior history is lacking.
Julian Eisenschlos
December 4, 2014
Extracting Translation Pairs from Social Network Content
International Workshop on Spoken Language Translation
We describe two methods to collect translation pairs from public Facebook content. We use the extracted translation pairs as additional training data for machine translation systems and we can show significant improvements.
Matthias Eck, Yury Zemlyanskiy, Joy Zhang, Alex WaibelSeptember 4, 2014
Question Answering with Subgraph Embeddings
Empirical Methods in Natural Language Processing
This paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few handcrafted features. Our model learns low-dimensional embeddings of words and knowledge base constituents; these representations are used to score natural language questions against candidate answers.
Antoine Bordes, Jason Weston, Sumit ChopraSeptember 4, 2014
#TagSpace: Semantic Embeddings from Hashtags
Empirical Methods in Natural Language Processing
We describe a convolutional neural network that learns feature representations for short textual posts using hashtags as a supervised signal. The proposed approach is trained on up to 5.5 billion words predicting 100,000 possible hashtags.
Jason Weston, Sumit Chopra, Keith AdamsSeptember 1, 2014
Optimal Crowd-Powered Rating and Filtering Algorithms
VLDB 2014
We focus on crowd-powered filtering, i.e., filtering a large set of items using humans. Filtering is one of the most commonly used building blocks in crowdsourcing applications and systems. While solu…
Aditya Parameswaran, Stephen Boyd, Hector Garcia-Molina, Ashish Gupta, Neoklis Polyzotis, Jennifer WidomAugust 24, 2014
Streamed Approximate Counting of Distinct Elements
ACM Conference on Knowledge Discovery and Data Mining (KDD)
Counting the number of distinct elements in a large dataset is a common task in web applications and databases. This problem is difficult in limited memory settings where storing a large hash table ta…
Daniel Ting