Facebook AI Research (FAIR)

Facebook to Accelerate Global AI Research with  New GPU Program Recipients
by Ari Entin22 hours ago
Facebook AI Research (FAIR)
Blog
Facebook is announcing new recipients to the GPU Partnership Program
Learning to Segment
by Piotr Dollaron Thursday
Facebook AI Research (FAIR)
Blog
New detection technologies will move us toward a more precise understanding of images.
fastText
by Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolovabout 2 weeks ago
Facebook AI Research (FAIR)
Blog
Faster, better text classification! New open-source fastText library quickly builds state-of-the-art text classifiers that scale to billions of words.
Facebook Researchers Focus on the Most Challenging Machine Learning Questions at ICML 2016
by Jason Weston, Leon Bottou, Joaquin Quinonero Candela, Hussein Mehanna, Pierre Andrews, Aditya Kalro, Alexander Sidorov, Ronan Collobert, Armand Joulin, Laurens van der Maaten, David Grangier, Tomas Mikolov, Antoine Bordes, Rob Fergus, Lars Backstrom, Ross Girshickabout 2 months ago
Facebook AI Research (FAIR)
Blog
Facebook researchers are actively engaged at the International Conference on Machine Learning (ICML) 2016 being held in New York City this week. Widely known as the leading Machine Learning...

Highlights

Learning to Refine Object Segments
by Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr DollarOctober 10, 2016
Publication
A MultiPath Network for Object Detection
by Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr DollarSeptember 18, 2016
Publication
Learning to Segment
by Piotr DollarAugust 25, 2016
Blog post

About Facebook AI Research (FAIR)

We’re committed to advancing the field of machine intelligence and developing technologies that give people better ways to communicate. In the long term, we seek to understand intelligence and make intelligent machines. How will we accomplish all this? By building the best AI lab in the world.

Research at the lab covers the full spectrum of topics related to AI, and to deriving knowledge from data: theory, algorithms, applications, software infrastructure and hardware infrastructure.

Our long-term objectives of understanding intelligence and building intelligent machines are bold and ambitious. But making significant progress towards AI can't be done in isolation, and will require the full engagement of the international research community. Everyone at Facebook strongly believes that scientific and technological progress comes from open interactions within the research community. In that spirit, Facebook AI researchers are expected to contribute to the research community through publications, open source software, participation in technical conferences and workshops, and through collaborations with colleagues in academia.

Publications

Learning to Refine Object Segments
Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollar
ECCV
Oct 10, 2016
In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.
A MultiPath Network for Object Detection
Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollar
BMVC
Sep 18, 2016
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...
Synergy of Monotonic Rules
Vladimir Vapnik, Rauf Izmailov
JMLR
Aug 16, 2016
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...

Blog

Learning to Segment
by Piotr DollarAug 25, 2016
fastText
by Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas MikolovAug 18, 2016
Facebook Researchers Focus on the Most Challenging Machine Learning Questions at ICML 2016
by Jason Weston, Leon Bottou, Joaquin Quinonero Candela, Hussein Mehanna, Pierre Andrews, Aditya Kalro, Alexander Sidorov, Ronan Collobert, Armand Joulin, Laurens van der Maaten, David Grangier, Tomas Mikolov, Antoine Bordes, Rob Fergus, Lars Backstrom, Ross GirshickJun 19, 2016

Topics and Resources

  • Mathematics of data representation and analysis
  • Learning theory
  • Optimization
  • Learning principles
  • Learning architectures and algorithms
  • Knowledge representation
  • Reasoning and inference
  • Image, text, speech, audio, and video analysis and understanding
  • Distributed systems and software environments for AI
  • …and many related domains



"If your dream is to solve AI, then Facebook— with its incredible infrastructure, rich data and top talent—is simply the most exciting place to be."
Director, Facebook AI Research

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