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.

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.

Jason Weston, Sumit Chopra, Keith Adams
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…

Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
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…

Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev
September 8, 2017

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Conference on Empirical Methods on Natural Language Processing (EMNLP)

Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states.

Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, Dhruv Batra
February 24, 2018

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

International Symposium on High-Performance Computer Architecture (HPCA)

Facebook’s machine learning workloads are extremely diverse: services require many different types of models in practice. This paper describes the hardware and software infrastructure that supports machine learning at global scale.

Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang