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I work on machine learning, in particular Deep Learning methods as applied to representation learning and generative models. Application areas include computer vision and generative biology. \\
I work on machine learning, in particular Deep Learning methods as applied to representation learning and generative models. \\
(:if false:)
(:ifend:)
Research Director
Facebook AI Research.\\
Research Scientist
DeepMind New York\\
http://cs.nyu.edu/~fergus/email2.png\\
http://cs.nyu.edu/~fergus/email2.png\\
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy.
\\
I work on machine learning, in particular Deep Learning methods as applied to representation learning and generative models. Application areas include computer vision and generative biology. \\
Research Director,
Facebook AI Research.\\
Research Director
Facebook AI Research.
I am also a Research Director at Facebook AI Research.\\
Research Director,
Facebook AI Research.\\
Professor\\
Professor of Computer Science\\
Dept. of Computer Science,\\
Research papers
!Research Papers
arXiv
arXiv and
I am also a Research Scientist at Facebook AI Research.\\
I am also a Research Director at Facebook AI Research.\\
Deep Learning for Computer Vision
NIPS 2013 Tutorial [Slides]
Online Recognition Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Krizhevsky et al. (NIPS 2012).
Latest Work
Pre-prints of recent research can be found on arXiv: Link
Selected Projects
http://cs.nyu.edu/~fergus/thumb/vis_thumb.png%%
Visualizing and Understanding Convolutional Networks
Matt Zeiler and Rob Fergus,
ECCV 2014, PDF
http://cs.nyu.edu/~fergus/thumb/exoplanet2.png%%
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy
B. R. Oppenheimer et al., Astrophysical Journal, March 2013
PDF | Project page
http://cs.nyu.edu/~fergus/thumb/depth_indoor.png%%
Indoor Segmentation and Support Inference from RGBD Images
Nathan Silberman, Derek Hoiem, Pushmeet Kolhi and Rob Fergus, ECCV 2012
PDF | NYU Depth Dataset v2.0
http://cs.nyu.edu/~fergus/thumb/deconv2.png%%
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
Matt Zeiler, Graham Taylor and Rob Fergus, ICCV 2011
PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/imitate.png%%
Learning Invariance through Imitation
Graham Taylor, Ian Spiro, Christoph Bregler and Rob Fergus
CVPR 2011. PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/norm_sparse.png%%
Blind Deconvolution using a Normalized Sparsity Measure
Dilip Krishnan, Terence Tay and Rob Fergus, CVPR 2011
PDF |
Project Page
http://cs.nyu.edu/~fergus/thumb/dark2.jpg%%
Dark Flash Photography
Dilip Krishnan and Rob Fergus,
ACM Trans. on Graphics (Proc. SIGGRAPH 2009).
High res PDF (73Mb) |
Low res PDF (5.1Mb) |
Project page
http://cs.nyu.edu/~fergus/thumb/tinyimages.jpg%%
80 million tiny images: a large dataset for non-parametric object and scene recognition
Antonio Torralba, Rob Fergus and William T. Freeman
PAMI, November 2008. PDF | Bibtex | Project page
http://cs.nyu.edu/~fergus/thumb/deblur-tn.png%%
Removing Camera Shake From A Single Photograph
Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis and William T. Freeman,
ACM Trans. on Graphics (Proc. SIGGRAPH 2006).
PDF | PPT | Code | Project page
Research papers
My research papers can be found at: arXiv
Google Scholar
Associate Professor
Vision, Learning and Graphics group,\\
Professor
CILVR lab,\\
Room 1226, 715 Broadway,
New York, NY 10003, USA.\\
Room 514, 60 5th Ave.,
New York, NY 10011, USA.\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research.\\
I am also a Research Scientist at Facebook AI Research.\\
Hello!<a href="http://www.cia9online.com/#1.html">cialis online pharmacy</a>
http://cs.nyu.edu/~fergus/rob4.png%%
Associate Professor
Vision, Learning and Graphics group,
Dept. of Computer Science,
Courant Institute of Mathematical Sciences,
New York University
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research.
Address:
Room 1226, 715 Broadway,
New York, NY 10003, USA.
Directions to lab
http://cs.nyu.edu/~fergus/email2.png
Research Overview
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy.
Deep Learning for Computer Vision
NIPS 2013 Tutorial [Slides]
Online Recognition Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Krizhevsky et al. (NIPS 2012).
Latest Work
Pre-prints of recent research can be found on arXiv: Link
Selected Projects
http://cs.nyu.edu/~fergus/thumb/vis_thumb.png%%
Visualizing and Understanding Convolutional Networks
Matt Zeiler and Rob Fergus,
ECCV 2014, PDF
http://cs.nyu.edu/~fergus/thumb/exoplanet2.png%%
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy
B. R. Oppenheimer et al., Astrophysical Journal, March 2013
PDF | Project page
http://cs.nyu.edu/~fergus/thumb/depth_indoor.png%%
Indoor Segmentation and Support Inference from RGBD Images
Nathan Silberman, Derek Hoiem, Pushmeet Kolhi and Rob Fergus, ECCV 2012
PDF | NYU Depth Dataset v2.0
http://cs.nyu.edu/~fergus/thumb/deconv2.png%%
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
Matt Zeiler, Graham Taylor and Rob Fergus, ICCV 2011
PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/imitate.png%%
Learning Invariance through Imitation
Graham Taylor, Ian Spiro, Christoph Bregler and Rob Fergus
CVPR 2011. PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/norm_sparse.png%%
Blind Deconvolution using a Normalized Sparsity Measure
Dilip Krishnan, Terence Tay and Rob Fergus, CVPR 2011
PDF |
Project Page
http://cs.nyu.edu/~fergus/thumb/dark2.jpg%%
Dark Flash Photography
Dilip Krishnan and Rob Fergus,
ACM Trans. on Graphics (Proc. SIGGRAPH 2009).
High res PDF (73Mb) |
Low res PDF (5.1Mb) |
Project page
http://cs.nyu.edu/~fergus/thumb/tinyimages.jpg%%
80 million tiny images: a large dataset for non-parametric object and scene recognition
Antonio Torralba, Rob Fergus and William T. Freeman
PAMI, November 2008. PDF | Bibtex | Project page
http://cs.nyu.edu/~fergus/thumb/deblur-tn.png%%
Removing Camera Shake From A Single Photograph
Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis and William T. Freeman,
ACM Trans. on Graphics (Proc. SIGGRAPH 2006).
PDF | PPT | Code | Project page
I do believe all of the ideas you've offered for your post. They are really convincing and will definitely work. Still, the posts are very quick for newbies. May just you please extend them a little from next time? Thank you for the post. bkgekeeecgda
Hello!<a href="http://www.cia9online.com/#1.html">cialis online pharmacy</a>
http://cs.nyu.edu/~fergus/rob4.png%%
Associate Professor
Vision, Learning and Graphics group,
Dept. of Computer Science,
Courant Institute of Mathematical Sciences,
New York University
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research.
Address:
Room 1226, 715 Broadway,
New York, NY 10003, USA.
Directions to lab
http://cs.nyu.edu/~fergus/email2.png
Research Overview
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy.
Deep Learning for Computer Vision
NIPS 2013 Tutorial [Slides]
Online Recognition Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Krizhevsky et al. (NIPS 2012).
Latest Work
Pre-prints of recent research can be found on arXiv: Link
Selected Projects
http://cs.nyu.edu/~fergus/thumb/vis_thumb.png%%
Visualizing and Understanding Convolutional Networks
Matt Zeiler and Rob Fergus,
ECCV 2014, PDF
http://cs.nyu.edu/~fergus/thumb/exoplanet2.png%%
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy
B. R. Oppenheimer et al., Astrophysical Journal, March 2013
PDF | Project page
http://cs.nyu.edu/~fergus/thumb/depth_indoor.png%%
Indoor Segmentation and Support Inference from RGBD Images
Nathan Silberman, Derek Hoiem, Pushmeet Kolhi and Rob Fergus, ECCV 2012
PDF | NYU Depth Dataset v2.0
http://cs.nyu.edu/~fergus/thumb/deconv2.png%%
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
Matt Zeiler, Graham Taylor and Rob Fergus, ICCV 2011
PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/imitate.png%%
Learning Invariance through Imitation
Graham Taylor, Ian Spiro, Christoph Bregler and Rob Fergus
CVPR 2011. PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/norm_sparse.png%%
Blind Deconvolution using a Normalized Sparsity Measure
Dilip Krishnan, Terence Tay and Rob Fergus, CVPR 2011
PDF |
Project Page
http://cs.nyu.edu/~fergus/thumb/dark2.jpg%%
Dark Flash Photography
Dilip Krishnan and Rob Fergus,
ACM Trans. on Graphics (Proc. SIGGRAPH 2009).
High res PDF (73Mb) |
Low res PDF (5.1Mb) |
Project page
http://cs.nyu.edu/~fergus/thumb/tinyimages.jpg%%
80 million tiny images: a large dataset for non-parametric object and scene recognition
Antonio Torralba, Rob Fergus and William T. Freeman
PAMI, November 2008. PDF | Bibtex | Project page
http://cs.nyu.edu/~fergus/thumb/deblur-tn.png%%
Removing Camera Shake From A Single Photograph
Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis and William T. Freeman,
ACM Trans. on Graphics (Proc. SIGGRAPH 2006).
PDF | PPT | Code | Project page
I do believe all of the ideas you've offered for your post. They are really convincing and will definitely work. Still, the posts are very quick for newbies. May just you please extend them a little from next time? Thank you for the post. bkgekeeecgda
However, I am looking to recruit PhD students this year (i.e. to start in Fall 2016).\\
However, I am looking to recruit PhD students this year (i.e. for admission in Fall 2016).\\
However, I am looking to recruit PhD students this year (i.e. to start in Fall 2016).\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research. However, I am looking to recruit PhD students this year (i.e. for admission in Fall 2016)\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research.
However, I am looking to recruit PhD students this year (i.e. for admission in Fall 2016).\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research
However, I am looking to recruit PhD students this year (i.e. for admission in Fall 2016)\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research. However, I am looking to recruit PhD students this year (i.e. for admission in Fall 2016)\\
However, I am looking to recruit PhD students this year (i.e. for admission in Fall 2016)
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research\\
International Conference on Learning Representations 2014
I am one of the Program Chairs for a new conference on feature learning and representation learning. The submission deadline is December 20th. Check out the website.
arXiv pre-print, Nov 2013, PDF
ECCV 2014, PDF
Learning Invarance through Imitation
Learning Invariance through Imitation
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research\\
I am currently on leave from NYU, working as a Research Scientist
at Facebook AI Research\\
I am currently on leave from NYU, working as a Research Scientist at Facebook AI Research\\
Research Scientist
Facebook AI Lab\\
I am currently on leave from NYU, working as a Research Scientist
at Facebook AI Research\\
Director of Graduate Studies for
Master of Science in Data Science
I'm joining Facebook!
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group (Link). Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I will be working part time at Facebook until May, when my sabbatical starts and then I will be based full time at Facebook's new office at Astor Place, one block away from NYU. After my sabbatical, I will continue to work both at Facebook and NYU.
Facebook is building the AI Group here in New York, in Menlo Park and in London. This is an exciting time for the field, and I'm looking forward to getting started.
Applicants for Fall 2014 are welcome.
Please see the admissions page\\
http://chicagorehab.net/userinfo.php?uid=465922 15 min payday loans no credit check
http://cs.nyu.edu/~fergus/rob4.png%%
Associate Professor
Vision, Learning and Graphics group,
Dept. of Computer Science,
Courant Institute of Mathematical Sciences,
New York University
Research Scientist
Facebook AI Lab
Director of Graduate Studies for
Master of Science in Data Science
Applicants for Fall 2014 are welcome.
Please see the admissions page
MSDS Office Hours: 10.30-11.30am Wednesdays
Address:
Room 1226, 715 Broadway,
New York, NY 10003, USA.
Directions to lab
http://cs.nyu.edu/~fergus/email2.png
I'm joining Facebook!
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group (Link). Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I will be working part time at Facebook until May, when my sabbatical starts and then I will be based full time at Facebook's new office at Astor Place, one block away from NYU. After my sabbatical, I will continue to work both at Facebook and NYU.
Facebook is building the AI Group here in New York, in Menlo Park and in London. This is an exciting time for the field, and I'm looking forward to getting started.
Research Overview
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy.
Deep Learning for Computer Vision
NIPS 2013 Tutorial [Slides]
Online Recognition Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Krizhevsky et al. (NIPS 2012).
International Conference on Learning Representations 2014
I am one of the Program Chairs for a new conference on feature learning and representation learning. The submission deadline is December 20th. Check out the website.
Latest Work
Pre-prints of recent research can be found on arXiv: Link
Selected Projects
http://cs.nyu.edu/~fergus/thumb/vis_thumb.png%%
Visualizing and Understanding Convolutional Networks
Matt Zeiler and Rob Fergus,
arXiv pre-print, Nov 2013, PDF
http://cs.nyu.edu/~fergus/thumb/exoplanet2.png%%
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy
B. R. Oppenheimer et al., Astrophysical Journal, March 2013
PDF | Project page
http://cs.nyu.edu/~fergus/thumb/depth_indoor.png%%
Indoor Segmentation and Support Inference from RGBD Images
Nathan Silberman, Derek Hoiem, Pushmeet Kolhi and Rob Fergus, ECCV 2012
PDF | NYU Depth Dataset v2.0
http://cs.nyu.edu/~fergus/thumb/deconv2.png%%
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
Matt Zeiler, Graham Taylor and Rob Fergus, ICCV 2011
PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/imitate.png%%
Learning Invarance through Imitation
Graham Taylor, Ian Spiro, Christoph Bregler and Rob Fergus
CVPR 2011. PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/norm_sparse.png%%
Blind Deconvolution using a Normalized Sparsity Measure
Dilip Krishnan, Terence Tay and Rob Fergus, CVPR 2011
PDF |
Project Page
http://cs.nyu.edu/~fergus/thumb/dark2.jpg%%
Dark Flash Photography
Dilip Krishnan and Rob Fergus,
ACM Trans. on Graphics (Proc. SIGGRAPH 2009).
High res PDF (73Mb) |
Low res PDF (5.1Mb) |
Project page
http://cs.nyu.edu/~fergus/thumb/tinyimages.jpg%%
80 million tiny images: a large dataset for non-parametric object and scene recognition
Antonio Torralba, Rob Fergus and William T. Freeman
PAMI, November 2008. PDF | Bibtex | Project page
http://cs.nyu.edu/~fergus/thumb/deblur-tn.png%%
Removing Camera Shake From A Single Photograph
Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis and William T. Freeman,
ACM Trans. on Graphics (Proc. SIGGRAPH 2006).
PDF | PPT | Code | Project page
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http://cs.nyu.edu/~fergus/rob4.png%%
Associate Professor
Vision, Learning and Graphics group,
Dept. of Computer Science,
Courant Institute of Mathematical Sciences,
New York University
Research Scientist
Facebook AI Lab
Director of Graduate Studies for
Master of Science in Data Science
MSDS Office Hours: 10.30-11.30am Wednesdays
Address:
Room 1226, 715 Broadway,
New York, NY 10003, USA.
Directions to lab
http://cs.nyu.edu/~fergus/email2.png
I'm joining Facebook!
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group (Link). Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I will be working part time at Facebook until May, when my sabbatical starts and then I will be based full time at Facebook's new office at Astor Place, one block away from NYU. After my sabbatical, I will continue to work both at Facebook and NYU.
Facebook is building the AI Group here in New York, in Menlo Park and in London. This is an exciting time for the field, and I'm looking forward to getting started.
Research Overview
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy.
Deep Learning for Computer Vision
NIPS 2013 Tutorial [Slides]
Online Recognition Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Krizhevsky et al. (NIPS 2012).
International Conference on Learning Representations 2014
I am one of the Program Chairs for a new conference on feature learning and representation learning. The submission deadline is December 20th. Check out the website.
Latest Work
Pre-prints of recent research can be found on arXiv: Link
Selected Projects
http://cs.nyu.edu/~fergus/thumb/vis_thumb.png%%
Visualizing and Understanding Convolutional Networks
Matt Zeiler and Rob Fergus,
arXiv pre-print, Nov 2013, PDF
http://cs.nyu.edu/~fergus/thumb/exoplanet2.png%%
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy
B. R. Oppenheimer et al., Astrophysical Journal, March 2013
PDF | Project page
http://cs.nyu.edu/~fergus/thumb/depth_indoor.png%%
Indoor Segmentation and Support Inference from RGBD Images
Nathan Silberman, Derek Hoiem, Pushmeet Kolhi and Rob Fergus, ECCV 2012
PDF | NYU Depth Dataset v2.0
http://cs.nyu.edu/~fergus/thumb/deconv2.png%%
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
Matt Zeiler, Graham Taylor and Rob Fergus, ICCV 2011
PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/imitate.png%%
Learning Invarance through Imitation
Graham Taylor, Ian Spiro, Christoph Bregler and Rob Fergus
CVPR 2011. PDF | Project Page
http://cs.nyu.edu/~fergus/thumb/norm_sparse.png%%
Blind Deconvolution using a Normalized Sparsity Measure
Dilip Krishnan, Terence Tay and Rob Fergus, CVPR 2011
PDF |
Project Page
http://cs.nyu.edu/~fergus/thumb/dark2.jpg%%
Dark Flash Photography
Dilip Krishnan and Rob Fergus,
ACM Trans. on Graphics (Proc. SIGGRAPH 2009).
High res PDF (73Mb) |
Low res PDF (5.1Mb) |
Project page
http://cs.nyu.edu/~fergus/thumb/tinyimages.jpg%%
80 million tiny images: a large dataset for non-parametric object and scene recognition
Antonio Torralba, Rob Fergus and William T. Freeman
PAMI, November 2008. PDF | Bibtex | Project page
http://cs.nyu.edu/~fergus/thumb/deblur-tn.png%%
Removing Camera Shake From A Single Photograph
Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis and William T. Freeman,
ACM Trans. on Graphics (Proc. SIGGRAPH 2006).
PDF | PPT | Code | Project page
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Applicants for Fall 2014 are welcome.
Please see the admissions page\\
Pre-prints of recent research can be found on arXiv: Link
Facebook is building the AI Group here in New York, in Mountain View and in London. This is an exciting time for the field, and I'm looking forward to getting started.
Facebook is building the AI Group here in New York, in Menlo Park and in London. This is an exciting time for the field, and I'm looking forward to getting started.
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy. \\
My research is in the areas of Machine Learning and Computer Vision. I am particularly interested in applying Deep Learning methods to object recognition. I also work on low-level vision problems, with applications to computational photography and astronomy.
\\
NIPS 2013 Tutorial
[Slides]
NIPS 2013 Tutorial [Slides]
Research Scientist
Facebook AI Lab
\\
Research Scientist
Facebook AI Lab
\\
[[http://www.facebook.com|Facebook] AI Lab
Facebook AI Lab
Research Scientist
[[http://www.facebook.com|Facebook] AI Lab
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group <Link to Yann's post on Facebook. Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group (Link). Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I'm joining Facebook!
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group <Link to Yann's post on Facebook. Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I will be working part time at Facebook until May, when my sabbatical starts and then I will be based full time at Facebook's new office at Astor Place, one block away from NYU. After my sabbatical, I will continue to work both at Facebook and NYU.
Facebook is building the AI Group here in New York, in Mountain View and in London. This is an exciting time for the field, and I'm looking forward to getting started.
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group <Link to Yann's post on Facebook. Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I will be working part time at Facebook until May, when my sabbatical starts and then I will be based full time at Facebook's new office at Astor Place, one block away from NYU. After my sabbatical, I will continue to work both at Facebook and NYU.
Facebook is building the AI Group here in New York, in Mountain View and in London. This is an exciting time for the field, and I'm looking forward to getting started.
I'm happy to announce that I am joining Facebook's new AI Group, a research laboratory with the long term goal of making major advances in the field. I'll be working alongside Yann LeCun, who will be leading the Group <Link to Yann's post on Facebook. Facebook first announced the AI Group in September to work on problems in deep learning, machine learning and computer vision.
I will be working part time at Facebook until May, when my sabbatical starts and then I will be based full time at Facebook's new office at Astor Place, one block away from NYU. After my sabbatical, I will continue to work both at Facebook and NYU.
Facebook is building the AI Group here in New York, in Mountain View and in London. This is an exciting time for the field, and I'm looking forward to getting started.
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy,
Reconnaissance of the HR 8799 Exosolar System I: Near IR Spectroscopy
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Kirzhevsky et al. (NIPS 2012).
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Krizhevsky et al. (NIPS 2012).
Results of ImageNet 2013 Competition
The NYU lab entries did very well in all three competitions. There results are here: link.
Deep Learning Online Demo
Online Recognition Demo
Results of ImageNet 2013 Competition
The NYU lab entries did very well in all three competitions. There results are here: link.
arXiv pre-print, Nov 2013 PDF
arXiv pre-print, Nov 2013, PDF
Visualizing and Understanding Convolutional Networks,
Visualizing and Understanding Convolutional Networks
arXiv pre-print, Nov 2013
PDF
arXiv pre-print, Nov 2013 PDF
arXiv pre-print PDF
, Nov 2013
arXiv pre-print, Nov 2013
PDF
http://cs.nyu.edu/~fergus/thumb/digit_2.png%%\\
http://cs.nyu.edu/~fergus/thumb/vis_thumb.png%%\\
arXiv pre-print PDF
arXiv pre-print PDF
, Nov 2013
Deep Learning for Computer Vision
NIPS 2013 Tutorial
Slides Coming soon....
I am one of the Program Chairs for a new conference on feature learning and representation learning. The submission deadline is December 20th. Check out the website.
Deep Learning Methods for Vision
CVPR 2012 Tutorial
Speakers: Rob Fergus (NYU), Honglak Lee (Michigan), Marc'Aurelio Ranzato (Google) Ruslan Salakhutdinov (Toronto), Graham Taylor (Guelph), Kai Yu (Baidu)
Course Webpage
I am one of the Program Chairs for a new conference on feature learning and representation learning. The submission deadline is December 20th. Check out the website.
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Kirzhevsky et al. (NIPS 2012).
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Kirzhevsky et al. (NIPS 2012).
PDF
arXiv pre-print PDF
http://cs.nyu.edu/~fergus/thumb/digit_2.png%%
Visualizing and Understanding Convolutional Networks,
Matt Zeiler and Rob Fergus,
PDF
http://cs.nyu.edu/~fergus/thumb/digit_2.png%%
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks,
Matt Zeiler and Rob Fergus, ICLR 2013
PDF
Deep Learning Online Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Kirzhevsky et al. (NIPS 2012).
Deep Learning Online Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Kirzhevsky et al. (NIPS 2012).
Deep Learning Online Demo
See our deep convolutional network demo here. This network achieves 16.5% top-5 error on the Imagenet 2012 classification challenge, around 2% better than the network of Kirzhevsky et al. (NIPS 2012).
Director of Graduate Studies for Master of Science in Data Science\\
Director of Graduate Studies for
Master of Science in Data Science\\
Please see the admissions page \\
Please see the admissions page
Applicants for Fall 2014 are welcome. Please see the admissions page
Applicants for Fall 2014 are welcome.
Please see the admissions page
Director of Graduate Studies for Master of Science in Data Science
Director of Graduate Studies for Master of Science in Data Science
Applicants for Fall 2014 are welcome. Please see the admissions page
\\
Applicants for Fall 2014 are welcome. Please see the admissions page
Applicants for Fall 2014 are welcome. Please see the admissions page \\
International Conference on Learning Representations 2013
I am one of the Program Chairs for a new conference on feature learning and representation learning. Check out the website.
International Conference on Learning Representations 2014
I am one of the Program Chairs for a new conference on feature learning and representation learning. The submission deadline is December 20th. Check out the website.
MSDS Office Hours: 10.30-11.30am Wednesdays
MSDS Office Hours: 10.30-11.30am Wednesdays\\
Assistant Professor\\
Associate Professor\\
MSDS Office Hours: 10.30-11.30am Wednesdays