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Year Published

85 Results

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.

By: Matthias Eck, Yury Zemlyanskiy, Joy Zhang, Alex Waibel
September 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.

By: Antoine Bordes, Jason Weston, Sumit Chopra
September 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.

By: Jason Weston, Sumit Chopra, Keith Adams
September 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…

By: Aditya Parameswaran, Stephen Boyd, Hector Garcia-Molina, Ashish Gupta, Neoklis Polyzotis, Jennifer Widom
August 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…

By: Daniel Ting
August 24, 2014

Practical Lessons from Predicting Clicks on Ads at Facebook

International Workshop on Data Mining for Online Advertising (ADKDD)

Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising system…

By: Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Stuart Bowers, Joaquin QuiƱonero Candela
August 7, 2014

Perceiving, Learning, and Exploiting Object Affordances for Autonomous Pile Manipulation

Autonomous Robots

Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the…

By: Anthony Stentz, Arun Venkatraman, Dubi Katz, J. Andrew Bagnell, Moslem Kazemi
June 24, 2014

PANDA: Pose Aligned Networks for Deep Attribute Modeling

Conference on Computer Vision and Pattern Recognition (CVPR)

We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulat…

By: Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev
June 24, 2014

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Conference on Computer Vision and Pattern Recognition (CVPR)

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing exp…

By: Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
June 24, 2014

Collaborative Hashing

Conference on Computer Vision and Pattern Recognition (CVPR)

Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities….

By: Xianglong Liu, Junfeng He, Cheng Deng, Bo Lang