samy bengio cv

45. 2005, ICML 2010, IEEE Workshop on Neural Networks for Signal Processing, NNSP, I. Goodfellow, and. Why is the name "neural" praised so much? Symposium on Applied Computing - Special Track on 2006, IEEE Workshop on Machine Learning for Signal Processing, MLSP, Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio. J. Deng, N. Ding, Y. Jia, A. Frome, K. Murphy, D. Erhan, Y. Bengio, A. Courville, P.-A. I. Huerga, A. Grigorenko, L. Thorbergsson, A. D. Nemitz, J. Sandker, S. King, S. Marcel. MULTI - Multimodal Interaction and Multimedia Data Mining, several PhDs. BayLearn: a new Workshop in Machine Learning in the Bay Area (BayLearn'2012-2016). Google Inc SCRIPT - Cursive Handwriting Recognition, 1 PhD thesis finished. ... Edgar Dobriban: Curriculum Vitae nized by Samy Bengio, Alexander Madry, Elchanan Mossel, Matus Telgarsky. Created by W.Langdon from gp-bibliography.bib Revision:1.5454 @InProceedings{Bengio:1994:GPslrNN, author = "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier", title = "Use of genetic programming for the search of a new learning rule for neutral networks", K. Weber, F. de Wet, B. Cranen, L. Boves. 2008, 2009, Multimodal User Authentication Workshop, MMUA, 2006, NIPS Workshop on 2013. International Conference on Machine Learning, ICML, IM2.ACP, Song. ... Uri Shalit, and Samy Bengio. S. Sonnenburg, M. L. Braun, C. Soon Ong, S Bengio, L. Bottou, G. Holmes, BigVision 2014: a CVPR Workshop on Big Data for Computer Vision (CVPR'2014). For up-to-date information: my Google scholar page. He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). [5] Chiyuan Zhang, Samy Bengio, Moritz Hardt, et al. Member of the steering committee, BANCA - Biometric Access Control for Networked and e-Commerce Applications, 5th Framework Programme, Information Society Technology, 2 researchers, EDAM - Environmental data mining: machine Learning algorithms and statistical tools for monitoring and forecasting, INTAS foundation, 1 invited researcher, LAVA - Learning for Adaptable Visual Assistants, 1 postdoc and 2 PhD, COST-275 - Biometric-Based Recognition of People over the Internet, 1 PhD, Journal of Machine Learning Research, 2009-2012, Journal of Computational Statistics, 2002-2011, Journal of Selected Topics in Signal Processing, 2009, ICLR: International Conference on Learning Representations, 2018-2020. 2006, NIPS Workshop on Efficient Machine Learning, Research Scientist, Google Brain. The idea of learning to learn (in particular by back-propagating through the whole process) has now become very popular (now called Dec 23, 2017 Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks. Webvision: ECCV Workshop on Computer Vision for the Web (ECCV'2012), Workshop on Multimodal Interaction and Related Machine Learning Algorithms, MLMI, Machine Learning Deep Learning Representation Learning. Google Scholar; Canhui Wang, Min Zhang, Shaoping Ma, and Liyun Ru. NIPS 2004, International Conference on Machine Learning (ICML) CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Human actions capture a wide variety of interactions between people and objects. … 2004, F. de Wet, K. Weber, L. Boves, B. Cranen. The Deep Learning Tutorials are a walk-through with code for several important Deep Architectures (in progress; teaching material for Yoshua Bengio’s IFT6266 course). Edgar Dobriban 6 Participant in Random Matrix Theory Summer School, Park City Mathematics Institute, Institute for Advanced Studies, June 2017. 2005, 2011, Senior Program Committee, International Joint Conference on Artificial Intelligence (IJCAI) Learning semantic relationships for better action retrieval in images. EDAM - Environmental data mining: … PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning, 6th Framework Programme, Information Society Technology, Network of Excellence. 2019, International Conference on Machine Learning (ICML) A. Mirhoseini, H. Pham, Q. V. Le, B. Steiner, R. Larsen, Y. Zhou, N. Kumar, IM2.MI, Google Inc Song Han, Huizi Mao, and William J. Dally. Simons Institute for the Theory of Computing, 2018-. Deep Reinforcement Learning Workshop in Neural Information Processing Systems Conference, 2019. Machine Learning for Implicit Feedback and User Modeling (NIPS'2005), SIGIR 2007 Workshop on [B2] Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Hamm, and Daniel D. ... Fei Sha Curriculum Vitae,, 2015. Paper-Spray. NIPS, AAAI Spring Symposium on Dataset Augmentation in Feature Space. 1993-1993, Part Time System Administrator and Research Assistant Moreover, NeurIPS 2020 had twice as many submissions as ICML, even though both are top-tier ML conferences. arxiv: 1512.03385 [cs.CV] Google Scholar 2007-Present, Senior Researcher in Machine Learning Samy Bengio Google Brain bengio@google.com ABSTRACT Adversarial examples are malicious inputs designed to fool machine learning models. In general, I follow the paper "Variational Recurrent Auto-encoders" and "Generating Sentences from a Continuous Space".Most of the implementations about the variational layer are adapted from "y0ast/VAE-torch". 2007, 2004, I also interned at Google Research Mountain View, under the thoughtful guidance of Samy Bengio. 2006, 2009, International Conference on Biometrics Authentication, ICBA, International Conference on Computer Vision, CVPR. What many people don't know is how intertwined Yoshua’s career has been with that of his brother, Samy, a machine learning scientist at … ∙ University of Guelph ∙ 0 ∙ share . Y. Jiang, B. Neyshabur, H. Mobahi, D. Krishnan, and, D. Duckworth, A. Neelakantan, B. Goodrich, L. Kaiser, and, J. Chorowski, R. J. Weiss, R. A. Saurous, and, G. F. Elsayed, D. Krishnan, H. Mobahi, K. Regan, and, S. Escalera, M. Weimer, M. Burtsev, V. Malykh, V. Logacheva, R. Lowe, I. V. URL: Andrew Ng. IM2.BMI and IM2.MPR - Interactive Multimodal Information Management, 4 PhD, 2 postdocs. Decision Trees are the foundation for many classical machine learning algorithms like Random Forests, Bagging, and Boosted Decision Trees.They were first proposed by Leo Breiman, a statistician at the University of California, Berkeley. Y. Wang, R.J. Skerry-Ryan, D. Stanton, Y. Wu, R.J. Weiss, N. Jaitly, Z. Yang, “Understanding deep learning requires rethinking generalization”. Applied Biometrics (2010), CVPR Workshop on S. P. Mohanty, C. F. Ong, J. L. Hicks, S. Levine, M. Salathé, S. Delp, 2015. GLAD - Use of Boolean Methods for Classification, 1 PhD thesis finished. Swiss National Science Foundation Projects: Machine Learning for Implicit Feedback and User Modeling, Searching Spontaneous Conversational Speech, http://bengio.abracadoudou.com/lectures/old. 1991-1995 Learningtolearnpapers with Samy Bengio, starting with IJCNN 1991, “Learning a synaptic learning rule”. Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. KERNEL - Kernel Methods for Sequence Processing, 1 PhD. Serban, Y. Bengio, A. Rudnicky, A. W. Black, S. Prabhumoye, ¿. 2. Knowledge Representation and Reasoning (2015), AAAI Spring Symposium on PDF. K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, A. Kostin, S. Marcel, M. Magimai Doss, T. A. Stephenson, H. Bourlard, and. 2015. Next, Bengio, Hinton, and LeCun are truly deep learning pioneers but calling them the "godfathers" of AI is insane. Microcell Labs Deep Learning Code Tutorials. NIPS 2007, NIPS Workshop on Learning to Compare Examples, A. Smola, P. Vincent, J. Weston, and R. Williamson. Yoshua Bengio, Aaron Courville, Pascal Vincent, Representation Learning: A Review and New Perspectives, Arxiv, 2012. There's multiple things in the middle. Yoshua Bengio. “Practical recommendations for gradient-based training of deep architectures”. http://bengio.abracadoudou.com/, Research Scientist in Machine Learning 1997-1999, Researcher 3156-3164 Abstract Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. Yu-Wei Chao, Zhan Wang, Rada Mihalcea, Jia Deng. There's self-supervised, there's reinforcement learning. Centre National d'Etudes des Télécommunications, France Télécom NIPS 2006, NIPS Workshop on Multimodal Signal Processing, ... Yoshua Bengio interview 25:48. “Practical recommendations for gradient-based training of deep architectures”. Yoshua Bengio FRS OC FRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. Centre Interuniversitaire de Recherche en ANalyse des Organisations, 437–478. KerSpeech - Kernel Methods for Speech and Video Sequence Analysis, 1 PhD. 1994-1995, Research Assistant Y. LeCun, K.-R. Müller, F. Pereira, C. E. Rasmussen, G. Rätsch, B. Schölkopf, Instructor. Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. This is a list of interesting research papers started by Kumar and Biswa (currently being maintained only by Kumar), mainly in Machine Learning, but definitely not limited to it. In: … By Vignesh Ramanathan, Congcong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang Song, Samy Bengio, Chuck Rossenberg and Li Fei-Fei Beyond Patches (CVPR'2006), International Workshop on Biometric Recognition Systems (IWBRS) Y. Xiao, Z. Chen, S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and, N. Jaitly, D. Sussillo, Q. V. Le, O. Vinyals, I. Sutskever, and, J. Lee, S. Kim, G. Lebanon, Y. Institut National de la Recherche Scientifique - Télécommunications 2006, International Workshop on Multiple Classifier Systems, MCS, International Joint Conference on Neural Networks, IJCNN, International Conference on Pattern Recognition, ICPR, Neural Information Processing Systems, ADASEQ - Ensemble Methods for Sequence Processing, 1 PhD. ESANN, 2004, 2005, Extraction et Gestion des Connaissances (EGC) “Understanding deep learning requires rethinking generalization”. Manzagol, P. Vincent, and. December 2019 Oral. 2008. Yoshua Bengio. 2016 2015 2014. 1995-1996, Postdoctoral Fellow Large scale online learning of image similarity through rank-ing. BigVision 2012: a NIPS Workshop on Big Data for Computer Vision (NIPS'2012). Samy Bengio. M. Norouzi. 1999-2007, Research Director In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). Representational Similarity - From Neuroscience to Deep Learning… and back again 11 minute read Published: June 16, 2019 In today’s blog post we discuss Representational Similarity Analysis (RSA), how it might improve our understanding of the brain as well as recent efforts by Samy Bengio’s and Geoffrey Hinton’s group to systematically study representations in Deep Learning … Yijie Guo, Jongwook Choi, Marcin Moczulski, Samy Bengio, Mohammad Norouzi, Honglak Lee. Department of Computer Science, Université de Montréal By Year: 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 before 2000: V. Ramanathan, J. Deng, C. Li, W. Han, Z. Li, K. Gu, Y. [5] Chiyuan Zhang, Samy Bengio, Moritz Hardt, et al. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers. Show and tell: A neural image caption generator. In: Neural networks: Tricks of the trade. Centre de Recherche sur les Transports, Université de Montréal 2013 2012. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. lectures, Many years experience in system administration, Institut National de la Recherche Scientifique - Télécommunications, Centre National d'Etudes des Télécommunications, France Télécom. ... GS Corrado, J Shlens, S Bengio, J Dean, MA Ranzato, ... Advances in neural information processing systems, 2121-2129, 2013. Mohammad Norouzi, “Compact Discrete Representations for Scalable Similarity Search”,PhD thesis 2016. Joonseok Lee, Hanggjun Cho, Robert Ian (Bob) McKay. 02/17/2017 ∙ by Terrance DeVries, et al. Springer, 2012, pp. BigVision 2015: a CVPR Workshop on Big Data for Computer Vision (CVPR'2015). In a previous life, I was an undergrad in ECE at IIIT-Hyderabad where I worked with K. Madhava Krishna in … SAMY BENGIO: It's not just supervised and unsupervised. 2020, European Conference on Machine Learning (ECML-PKDD) I have been fortunate to work with some great mentors and collaborators during grad school, including Larry Zitnick, Dhruv Batra, Kevin Murphy, Gal Chechik, and Samy Bengio. Research Intern at Google Brain Advisor: Honglak Lee, Samy Bengio MTV, California (Jun.2018 – Aug.2018) • Build a model to learn representation about controllable and uncontrollable dynamics in RL; Capture the location information of multiple moving entities in the 2D video games to improve count-based exploration BANCA - Biometric Access Control for Networked and e-Commerce Applications, 5th Framework Programme, Information Society Technology, 2 researchers. PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning, 6th Framework Programme, Information Society Technology, Network of Excellence. 2019, Neural Information Processing Systems (NeurIPS) Singer, and. California 2005, 2006, IEEE International Conference on Acoustic, Speech and Signal Processing, ICASSP, IEEE International Conference on Robotics and Automation, ICRA, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, International Conference on Artificial Intelligence and Statistics, AISTATS, Yoshua Bengio just won the Turing Award, the highest distinction in computer science and artificial intelligence, with Geoffrey Hinton and Yann Lecun. 1681: 2013: Generating sentences from a continuous space. arxiv: 1510.00149 [cs.CV] Google Scholar; Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model’s parameters. ICLR: International Conference on Learning Representations (2015, 2016). Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans, “Reward Augmented Maximum Likelihood for Neural Structured Prediction”,NIPS 2016. Variational LSTM-Autoencoder. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. So, it has become a much more complex space. Organized by Alexei Borodin, Alice Guionnet, and Ivan Corwin. D. Gatica-Perez, I. McCowan D. Zhang, and. Springer, 2012, pp. K. Messer, J. Kittler, M. Sadeghi, S. Marcel, C. Marcel. This project implements the Variational LSTM sequence to sequence architecture for a sentence auto-encoding task. K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, A. Kostin, F. Cardinaux, CIRANO L. Kaiser, A. Roy, A. Vaswani, N. Parmar, I. Bello, H. Pham, Q. V. Le, M. Norouzi, and. Preprint. Centre Interuniversitaire de Recherche en ANalyse des Organisations, Part Time System Administrator and Research Assistant, Chair of International Conferences and Workshops, Programme Committee Chair - Senior Area Chair, Reviewer - Programme Committee Member - International Conferences, Reviewer - Programme Committee Member - International Workshops, Course IC-49 on Statistical Machine Learning from Data, EPFL - Computer, Communication and Information Sciences Doctoral Program, Advanced lectures on Statistical Machine Learning, Teaching replacement for M.Sc./Ph.D. ICLR: International Conference on Learning Representations (2014, 2017). CV; Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks. In Joseph Keshet and Samy Bengio, editors, Large Margin and Kernel Approaches to Speech and Speaker Recognition, chapter 8. There are many ways to get supervision cheap from the data you already have. Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. Publication. arXiv:2007.03200v2 [cs.CV] 8 Jul 2020. 94043 Mountain View 2009, 2012, 2015, 2016, 2020, International Joint Conference on Artificial Intelligence (IJCAI) A. S. Ecker, L. A. Gatys, M. Bethge, J. Boyd-Graber, S. Feng, P. Rodriguez, Deep Residual Learning for Image Recognition. In: Neural networks: Tricks of the trade. 3. 2005, Poster Track, Neural Information Processing Systems, 2006, IEEE Conference on Face and Gesture Recognition (FG) Wiley & Sons, 2008. LLORMA: Local Low-Rank Matrix Approximation, Journal of Machine Learning Research (JMLR), 2016. 2009, International Conference on Audio and Video Based Biometric Person Authentication, AVBPA, IDIAP Research Institute NeurIPS: Neural Information Processing Systems, 2019-. 1986-1993. Human Behavior Modeling (2009), ACM Extreme Classification Workshop, 2015, Workshop on Multimodal Interaction and Related Machine Learning Algorithms, MLMI, Divide and Learn I and II - Mixture models for large datasets, 3 PhD, 1 thesis finished. Member of the steering committee. 2004, 437–478. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable … USA, Email: This is mainly an initiative to inculcate a reading habit among ourselves. Samy Bengio - Publications Some of the files below are copyrighted. 1600 Amphitheatre Parkway Samy Bengio, Charles Rosenberg, Li Fei-Fei. 1991 – 1995 Articles sur l’art d’apprendre à apprendre en collaboration avec Samy Bengio, amorcés au IJCNN 1991 avec « Learning a synaptic learning rule ». Type. His idea was to represent data as a tree where each internal node denotes a test on an attribute (basically a condition), each branch represents an … NeurIPS: Neural Information Processing Systems (2018). Why? NIPS: Neural Information Processing Systems (2017). 2015. 2003, International Conference on Biometrics, ICB Kidzi¿ski, In: … IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 2002, International Conference on Learning Representations (ICLR) 3156--3164. My first but deeply formative research experience was at the Gatsby computational neuroscience unit, working with Peter Dayan on trying to understand how serotonin and dopamine interact. Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Taught By. CARTANN - Cartography by Artificial Neural Networks, 1 PhD thesis finished. Verified email at google.com - Homepage. Preprints [1] Yingwei Li, Song Bai, Cihang Xie, Zhenyu Liao, Xiaohui Shen, Alan Yuille. 1996-1997, Postdoctoral Fellow bengio [at] google.com Samy Bengio Google bengio@google.com Dumitru Erhan Google dumitru@google.com Abstract Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. 2004, EURASIP Journal of Applied Signal Processing, IEEE Transactions on Biomedical Engineering, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Speech and Audio Processing, IEEE Transactions on Systems, Man and Cybernetics - Part B, International Journal of Pattern Recognition and Artificial Intelligence, Francoise Fessant, Université de Rennes, 1995, Sébastien Marcel, Université de Rennes, 2000, Pierre-Edouard Sottas, EPFL Lausanne, 2002, Nicolas Gilardi, Université de Lausanne, 2002, Liva Ralaivola, Université de Paris 6, 2003, Ronan Collobert, Université de Paris 6, 2004, Serghei Kosinov, Université de Genève, 2005, Jean-Julien Aucouturier, Université de Paris 6, 2006, Sylvain Ferrandiz, Université de Caen, 2006, Christos Dimitrakakis, EPFL Lausanne, 2006, Jean-Francois Paiement, EPFL Lausanne, 2008, Marie Szafranski, Université de Technologie de Compiègne, 2008, Pierre-Michel Bousquet, Université d'Avignon, 2014, Hervé Glotin, HDR, Université Sud Toulon Var, 2007, Vincent Lemaire, HDR, Université de Paris Sud, 2008. Curriculum Developer. Yoshua Bengio is Professor in the Computer Science and Operations Research departments at U. Montreal, founder and scientific director of Mila and of IVADO. Regional Homo-geneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses, in In this paper, we present a generative model based on a deep re- Searching Spontaneous Conversational Speech, Spatial Interpolation Comparison, SIC, CV. NIPS, 2003, 2006, 2012, 2014, 2015, European Symposium on Artificial Neural Networks, M. Iyyer, H. He, H. Daumé III, S. McGregor, A. Banifatemi, A. Kurakin, The same story happened with the Zoom meetings at the virtual ICLR 2020. As a result, the set of possi-ble actions is extremely large and it is difficult to obtain sufficient training examples for all actions. Kian Katanforoosh.

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