电脑怎么访问国外外网 Research Scientist Google Research, Brain Team 1600 Amphitheatre Parkway 94043 Mountain View California, USA Email: bengio [at] google.com

Publications and Vitae

• List of my publications.
• My Google Scholar profile.
• My curriculum vitae in postscript, pdf (acrobat) or html.
• The Statistical Machine Learning from Data lectures that I gave at EPFL.

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• Deep architectures for sequences and other structured objects
  Research in better modeling sequences (speech, text, etc) using deep recurrent architectures. Consider in particular the long tail issues (rare words, rare events, etc).
• Machine Learning Libraries
  I was involved in the initial development of Torch, a machine learning library in lua/C++/GPL, including many building blocks for (deep) neural networks, mixture models, adaboost and bagging, etc. This library is a successor of the well-known SVMTorch software for support vector machines. I am now involved in the team that developed TensorFlow, a modern and flexible library for deep architectures.
• Understanding training and generalization of deep architectures. See for instance pp加速器安卓 or this .
• Adversarial Training. See for instance this or this.
• Image Captioning.

电脑怎么访问国外外网

• World AI Summit Americas 2024
  Keynote talk on "Towards Understanding Why Deep Learning Works", Montreal, Canada, March 2024.
• ASRU 2024
  Eyeriss系列论文总结 - 知乎:2021-11-4 · 新版加速器Eyeriss v2，跑MobileNet比Eyeriss快12.6倍，功耗低2.5 倍 为了少花一些cycle在“跳过0”这个过程上，采用CSC编码，如图4，先判断输入是否为0，输入为0直接跳过；如果输入不为0再判断权重是否为0，权重为0直接跳过，有不为0的权重时会继续在 ...
• Boston University
  Invited talk on "Some empirical observations linking representation and generalization", Boston, USA, February 2024.
• Qualcomm Machine Learning Summit
  Keynote speaker on "Some empirical observations linking representation and generalization", San Diego, USA, October 2018.
• Uber Machine Learning Summit
  Keynote speaker on "A Sample of Recent Deep Learning Research at Google Brain", San Francisco, USA, September 2018.
• Deep Learning: Theory, Algorithms, and Applications
  Invited talk on "Neural Combinatorial Optimization with Reinforcement Learning with application to device placement". Berlin, Germany, June 2017.
• Yandex
  Guest Lecture on "Deep Architectures for Structured Data". Moscow, Russia, December 2016.
• Berkeley University
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• Deep Learning: Theory, Algorithms, and Applications
  Invited talk on "Order Matters: Sequence to Sequence for Sets". MIT, Cambridge, USA, June 2016.
• DALI 2016
  Invited talk on "Order Matters: Sequence to Sequence for Sets". Sestri Levante, Italy, April 2016.
• College de France
  Invited talk on "Neural Image Captioning and other Structured Output Problems". Paris, France, March 2016.
• eBay Machine Learning and Data Science Conference
  PP加速器下载|PP加速器下载 - pk游戏网:2021-6-14 · PP加速器电脑版是款适用于网络上视频所打造的加速工具。PP加速器正式版采用目前许多先进和流行的互联网技术，如优化后的点对点传输(P2SP)、多任务下载、Web感知下载、智能Cache等技术。PP加速器为视频网站提供视频加速服务，可众有效的 ...
• Princeton Computer Science Department Seminar
  Invited talk on "Neural Image Captioning". Princeton University, USA, February 2016.
• NIPS 2015 Extreme Classification Workshop
  Invited talk on "Sharing is Caring in the Land of the Long Tail". Montreal, Canada, December 2015.
• ICMI 2015
  Invited talk on "Sharing Representations for Long Tail Computer Vision Problems ". Seattle, USA, November 2015.
• ICIAP 2015
  Invited talk on "The Battle Against the Long Tail in Computer Vision". Genoa, Italy, September 2015.
• AFEKA Conference for unsupervised learning
  Invited talk on "The power of embeddings for text, images, music, voice and image caption generation". Tel Aviv, Israel, April 2015.
• Fields Institute for Research in Mathematical Sciences
  Invited talk on "The Battle Against the Long Tail". Toronto, Canada, January 2015.
• Speaker Odyssey 2014
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• Trends in Machine Learning
  Invited talk on "Embeddings for images: from large scale image labeling to zero-shot classification". Kyoto, Japan, March 2014.
• ERMITES 2013
  Invited talks on "Large Scale Semantic Extraction Through Embeddings: From Images to Music" and "DeViSE". Ile de Porquerolles, France, September 2013.
• Microsoft Machine Learning Summit 2013
  Invited talk on "Laconic: Label Consistency for Image Categorization". Paris, April 2013.
• WebVision 2012
  pp视频加速器官方下载最新版下载 - 量产资源网:2021-6-10 · 视频加速速度提升300%，完全免费不用注册！pp视频加速器支持用户在线观看PPTV网站视频。能在您观看短视频网站的时候进行加速。帮您解决观看短视频时的“卡”“顿”的现象。目前支持Youtube、土豆优酷、56酷6新浪播客等短
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  Invited talk on "Learning A Semantic Space: From Image Annotation to Music Similarity". Toronto, Canada. May 2012.
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  Conference on Document Analysis Systems. Invited talk on "Learning A Semantic Space: From Image Annotation to Music Similarity". Gold Coast, Queensland. March 2012.
• 云豹加速器-小米应用商店 - Xiaomi:2021-5-7 · 小米应用商店提供云豹加速器免费下载,云豹是支持外网、外服游戏加速的全能加速器。采用国际光纤专线，安全私密一键跨境，不限流量畅游全球网站，超值赠送游戏加速，注册享免费体验。
  Invited talk on "Learning A Semantic Space: From Image Annotation to Music Similarity". UCLA, January 2011.
• NIPS Similarity Workshop 2011
  Invited talk on "Online Similarity Learning: From Images to Texts". Sierra Nevada, Andalucia, Spain, December 2011.
• Toyota Technical Institute in Chicago
  Invited talk on "Learning A Semantic Space: From Image Annotation to Music Similarity". Chicago, November 2011.
• National Academy of Engineering's Frontier of Engineering.
  Invited talk on "Large Scale Visual Semantic Extraction". Mountain View, California, September 2011.
• Symposium on distributed machine learning and sparse representation with massive data sets, organized by CSIRO.
  PP加速器0.6.5.0007 中文官方安装版 PP加速器绿色版 ...:2021-10-11 · PP加速器亲，如果你是一个电视剧爱好者，一定对加速器不莫生吧，他能让我伔的看电视剧的时候不用等待，不会卡住，但是大多数加速器只对视频本身的网站有效，对其它的视频网站就无能为力了，如果我伔看多个网站的视频就要装N个加速器，这样不但加不了速，还能让网络卡死，更PP加速器
• NIPS 2010 Workshop on Machine Learning for Next Generation Computer Vision Challenges.
  Invited talk on "Large scale image annotation: Learning to rank with joint word-image embeddings". Whistler, Canada, December 2010.
• IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010.
  Invited talk on "Large scale image annotation: Learning to rank with joint word-image embeddings". Arlington, Virginia, USA, September 2010.
• Iberian Conference on Pattern Recognition and Image Analysis, IbPRAI.
  Invited talk on "OASIS", Porto, Portugal, June 2009.
• Colloque international Francophone sur l'Ecrit et le Document, 手机pp加速器.
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• pp加速器官网网页: Ecole Recherche Multimedia d'Information, Techniques et Sciences.
  Lectures on "Statistical Machine Learning for Multimodal Sequence Processing and Extraction". Presqu'ile de Giens, September 2006.
• International Conference on Accoustic, Speech and Signal Processing, ICASSP.
  Invited talk at special session on Multimodal Biometric Authentication. Title: "Chimeric users to construct fusion classifiers in biometric authentication tasks: An investigation". Toulouse, May 2006.
• 15eme congres francophone en reconnaissance des formes et intelligence artificielle, RFIA.
  Invited talk on "Challenges in Multi Channel Sequence Processing". Tours, January 2006.
• International Conference on Multimodal Interactions, ICMI.
  Tutorial on "Statistical Machine Learning, with application to Multimodal Interactions". Trento, Italy. October 2005.
• Fourteenth Dutch-Belgian Conference on Machine Learning, pp加速器国际版下载.
  Invited talk on "Challenges in Multi-Channel Sequence Processing". University of Twente. Enschede, Holland. February, 2005.
• Summer School on Statistics and Environmental Sciences.
  Lecture on "Introduction to Statistical Machine Learning". Cargèse, Corsica, France. Novembre, 2004.

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• Mark Barnard (PhD 2005)
• Fabien Cardinaux (PhD 2005)
• Silvia Chiappa (PhD 2006)
• Ronan Collobert (PhD 2004)
• Christos Dimitrakakis (PhD 2006)
• Nicolas Gilardi (PhD 2002)
• David Grangier (PhD 2008)
• 绿叶加速器最新版下载 (PhD 2006)
• Johnny Mariéthoz (PhD 2006)
• 蚂蚁app加速器 (PhD 2001)
• Jean-Francois Paiement (PhD 2008)
• Norman Poh (PhD 2006)
• Alexei Pozdnoukhov (PhD 2006)
• Yann Rodriguez (PhD 2006)
• Conrad Sanderson (postdoc 2004)
• 手机pp加速器 (PhD 2003)
• 海外app加速器

Some Pointers

• I am co-organizing the Foundation of Deep Learning program at the Simons Institute for the Theory of Computing
• I am a member of the scientific advisory board of the Simons Institute for the Theory of Computing
• I was the General Chair of NeurIPS 2018 and the Programme Chair of NIPS 2017
• The Simons Institute's Foundation of Deep Learning Programme
• The ICML 2024 Workshop on Deep Phenomena
• The 2018 Dagstuhl seminar on Extreme Classification
• The ICML 2017 Reproducibility in Machine Learning Workshop
• The NIPS 2016 Extreme Classification Workshop
• We're organizing BayLearn, the long standing workshop in machine learning for researchers in the Bay Area.
• The 2016 Bay Area Deep Learning School
• The ICML Extreme Classification Workshop, 2015.
• The AAAI Spring Symposium on Knowledge Representation and Reasoning, 2015.
• We're organizing BigVision2012, 旋风app加速器, 2015, a series of workshops on Big Data for Computer Vision.
• We're organizing pp加速器国际版, an ECCV 2012 workshop on Computer Vision for the Web.
• A call for paper on Learning Deep Architectures organized by TPAMI in which I am a guest editor.
• A new book: Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods, co-edited by Joseph Keshet and myself. Published by Wiley, 2009.
• The International Conference on Machine Learning (ICML), Montreal, QC, Canada, June 14-18, 2009.
• The NIPS Workshop on Efficient Machine Learning, Whistler, BC, Canada, December 7-8, 2007.
• The NIPS Workshop on Learning to Compare Examples, Whistler, BC, Canada, December 8-9, 2006.
• The MLMI'06 workshop, Washington DC, USA, May 2006.
• The MLMI'05 workshop, Edinburgh, UK, July 2005.
• The PASCAL network of excellence.
• The NIPS 2004 Workshop on Multimodal Signal Processing .
• The MLMI'04 workshop, Martigny, Switzerland, June 2004, and the corresponding MLMI'04 recordings of the talks.
• The 2002 IEEE Workshop on Neural Networks for Signal Processing (NNSP), Martigny, Switzerland, September 2002
• Description of the LISA laboratory (UdeM) .
• Description of the CIRANO research center.
• Description of the IDIAP Research Institute, where I worked for many years.

Varia

• My best Machine Learning experiments: Alice and Jeremy