For all who missed hearing Hugo Larochelle, it's now on YOUTUBE. ["wp-wpml_current_language"]=> Instantly share code, notes, and snippets. Hugo Larochelle, at the Montreal AI Symposium in September. DIBS: Diversity inducing Information Bottleneck in Model Ensembles. Google Brain is a deep learning artificial intelligence research team at Google.Formed in the early 2010s, Google Brain combines open-ended machine learning research with information systems and large-scale computing resources. Neural networks [9.8] : Computer vision - example - YouTube Machine Learning Practitioners have different personalities. I am the lead of the Google Brain team in Montreal, adjunct professor at Université de Montréal and a Canada CIFAR Chair. Authored publications Google publications Other publications. Hugo Larochelle Hugo’s work concentrates on machine learning -the development of algorithms capable of extracting concepts and abstractions from data. Anirudh Goyal Alias Parth Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Recall Traces: Backtracking Models for Efficient Reinforcement Learning. A Universal Representation Transformer Layer for Few-Shot Image Classification. Previously, he was an Associate Professor at the University of Sherbrooke. Hugo Larochelle. From Kathryn Gentilello on October 26th, 2018 Machine Intelligence. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Previously, he was an Associate Professor at the University of Sherbrooke. Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi. Learn more. 19. Learn more. Thanks for having me. Welcome to the show, Hugo. Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin. Hugo Larochelle course - http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html - deeplearning.sh Google IA director in Montreal Hugo Larochelle summarized in two words the reason for this support for Mila: Yoshua Bengio. Finally, I have a popular online course on deep learning and neural networks, freely accessible on YouTube. InfoBot: Transfer and Exploration via the Information Bottleneck. Ankesh Anand, Eugene Belilovsky, Kyle Kastner. Solving them without Task Supervision at Test-Time. Contact All American Speakers Bureau to inquire about speaking fees and availability, and book the best keynote speaker for your next live or virtual event. Uniform Priors for Data-Efficient Transfer. Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Hugo Larochelle is a Research Scientist at Google Brain and lead of the Montreal Google Brain team. Since 2012, he has been cited 7,686 times in the Google Scholar index. Biography and booking information for Hugo Larochelle, Research Scientist at Google. There’s plenty of time to study his videos before his … Hugo Larochelle will run the new lab after joining Google from the Twitter, where he was part of the company's central AI team. Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. He is also a member of Yoshua Bengio’s Mila and an Adjunct Professor at the Université de Montréal. You signed in with another tab or window. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Ruslan Salakhutdinov, Hugo Larochelle ; JMLR W&CP 9:693-700, 2010. Even myself when I was teaching, I was putting a lot of material on YouTube to allow for people to learn. Learning Graph Structure With A Finite-State Automaton Layer. His Youtube courses are not to be missed and his twitter feed … He is particularly interested in deep neural networks, mostly applied in the context of big data and to artificial intelligence problems such as computer vision and natural language processing . Mohammad Havaei 1 , Axel Davy 2 , David Warde-Farley 3 , Antoine Biard 4 , Aaron Courville 3 , Yoshua Bengio 3 , Chris Pal 5 , Pierre-Marc Jodoin 6 , Hugo Larochelle 6 Affiliations 1 Université de Sherbrooke, Sherbrooke, Qc, Canada. Clone with Git or checkout with SVN using the repository’s web address. The Google Brain Team joined 300 other researchers, professionals and students to talk about the developments in … Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin. Anirudh Goyal Alias Parth Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu and. TechAide AI4Good 2020 - Olivier Corradi: Estimation of marginal emissions in … Neural networks [9.1] : Computer vision - motivation - YouTube }, classes I have taught at Université de Sherbrooke, [LATEST on arXiv preprint arXiv:2007.06700 (2020-07-13)], [Also on arXiv preprint arXiv:1910.13540 (2019-10-29)], [Also on arXiv preprint arXiv:1903.03096 (2019-03-07)], [Also on arXiv preprint arXiv:1811.02549 (2018-11-06)], [Also on arXiv preprint arXiv:1903.07714 (2019-03-18)]. Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle and Aaron C. Courville ICLR 2018 (2018-01-01) Hyperbolic Discounting and Learning over Multiple Horizons. Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, The Hanabi Challenge: A New Frontier for AI Research, On Catastrophic Interference in Atari 2600 Games. Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, array(1) { In episode nineteen we chat with Hugo Larochelle about his work on unsupervised learning, the International Conference on Learning Representations (ICLR), and his teaching style. About. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Hugo Larochelle - Few-shot Learning with Meta-Learning: Progress Made and Challenges Ahead . they're used to log you in. All over the world, great advances in the field of AI are the direct result of the Universite de Montreal professor and Mila director, said Larochelle. Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. Posted by Jaqui Herman and Cat Armato, Program Managers. My research focuses on the study and development of deep learning algorithms. Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. For additional information on me and my research, consider the following links: Publications collected and formatted using Paperoni, 6666 St-Urbain, #200, Montreal, QC, H2S 3H1, Adjunct Professor, Université de Montréal, Google, Learned Equivariant Rendering without Transformation Supervision. He is also a member of Yoshua Bengio's Mila and an Adjunct Professor at the Université de Montréal. Follow and subscribe https://lnkd.in/ed4j_Jy for more updates The Second RBCDSAI LatentView AI … Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck. Research Areas. We use essential cookies to perform essential website functions, e.g. This week marks the beginning of the 34 th annual Conference on Neural Information Processing Systems (NeurIPS 2020), the biggest machine learning conference of the year. Hugo Larochelle, Michael Mandel, Razvan Pascanu and Yoshua Bengio, Journal of Machine Learning Research, 13(Mar): 643-669, 2012; Detonation Classification from Acoustic Signature with the Restricted Boltzmann Machine Yoshua Bengio, Nicolas Chapados, Olivier Delalleau, Hugo Larochelle, Xavier Saint-Mleux, Christian Hudon and Jérôme Louradour, Anirudh Goyal, Philemon Brakel, William Fedus, Timothy Lillicrap, Sergey Levine, Disentangling the independently controllable factors of variation by interacting with the world. He’s one of the world’s brightest stars in artificial-intelligence research. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein. Larochelle offers an online deep learning and neural network course which is free and accessible on Youtube. string(2) "en" Are Few-Shot Learning Benchmarks too Simple ? Marco Pizzolato, Marco Palombo, Elisenda Bonet-Carne, Chantal M. W. Tax, Francesco Grussu, Andrada Ianus, Fabian Bogusz, Tomasz Pieciak, Lipeng Ning. For more information, see our Privacy Statement. Don’t be fooled by Hugo Larochelle’s youthful looks. William Fedus, Dibya Ghosh, John D. Martin, Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction, Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks, Learning Graph Structure With A Finite-State Automaton Layer, Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. My main area of expertise is deep learning. Hugo Larochelle is a Research Scientist at Google Brain and lead of the Montreal Google Brain team. Are Few-shot Learning Benchmarks Too Simple ? HoME: a Household Multimodal Environment. Hugo Larochelle: Hi. Revisiting Fundamentals of Experience Replay. Conclusion• Deep Learning : powerful arguments & generalization priciples• Unsupervised Feature Learning is crucial many new algorithms and applications in recent years• Deep Learning suited for multi-task learning, domain adaptation and semi-learning with few labels I currently lead the Google Brain group in Montreal. http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html, curl -O ftp://tlp.limsi.fr/public/emnlp05.pdf, curl -O http://aaroncourville.wordpress.com/, curl -O http://acl.ldc.upenn.edu/W/W02/W02-1001.pdf, curl -O http://aclweb.org/anthology-new/N/N12/N12-1005.pdf, curl -O http://ai.stanford.edu/~ehhuang/, curl -O http://ai.stanford.edu/~koller/, curl -O http://ai.stanford.edu/~quocle/, curl -O http://ai.stanford.edu/~quocle/LeKarpenkoNgiamNg.pdf, curl -O http://ai.stanford.edu/~rajatr/, curl -O http://ai.stanford.edu/~rajatr/papers/expsc_ijcai09.pdf, curl -O http://arxiv.org/pdf/1010.3467.pdf, curl -O http://arxiv.org/pdf/1011.4088v1.pdf, curl -O http://arxiv.org/pdf/1107.1805v1.pdf, curl -O http://arxiv.org/pdf/1206.5533v1.pdf, curl -O http://arxiv.org/pdf/1206.6407.pdf, curl -O http://arxiv.org/pdf/1207.0580.pdf, curl -O http://arxiv.org/pdf/1302.4389v4.pdf, curl -O http://bengio.abracadoudou.com/, curl -O http://books.nips.cc/papers/files/nips22/NIPS2009_0817.pdf, curl -O http://books.nips.cc/papers/files/nips22/NIPS2009_0933.pdf, curl -O http://brainlogging.wordpress.com/, curl -O http://cilvr.cs.nyu.edu/diglib/lsml/bottou-sgd-tricks-2012.pdf, curl -O http://cs.nyu.edu/~fergus/pmwiki/pmwiki.php, curl -O http://cs.nyu.edu/~koray/publis/jarrett-iccv-09.pdf, curl -O http://cs.nyu.edu/~wanli/dropc/dropc.pdf, curl -O http://cs.stanford.edu/~jngiam/, curl -O http://cs.stanford.edu/~jngiam/papers/NgiamChenKohNg2011.pdf, curl -O http://cs.stanford.edu/~pangwei/, curl -O http://cs.stanford.edu/~zhenghao/, curl -O http://cs.stanford.edu/people/teichman/, curl -O http://cseweb.ucsd.edu/~saul/papers/nips09_kernel.pdf, curl -O http://cseweb.ucsd.edu/~yoc002/, curl -O http://gosset.wharton.upenn.edu/~foster/index.pl, curl -O http://homepages.inf.ed.ac.uk/csutton/, curl -O http://homepages.inf.ed.ac.uk/imurray2/, curl -O http://homepages.inf.ed.ac.uk/imurray2/pub/07thesis/murray_thesis_2007.pdf, curl -O http://homes.cs.washington.edu/~lfb/paper/nips09b.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/1_01_artificial_neuron.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/1_02_activation_function.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/1_03_capacity_of_single_neuron.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/1_04_multilayer_neural_network.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/1_05_capacity_of_neural_network.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/1_06_biological_inspiration.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_01_motivation.pdf, curl -O 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-O http://info.usherbrooke.ca/hlarochelle/ift725/4_01_loss_function.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/4_02_unary_log-factor_gradient.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/4_03_pairwise_log-factor_gradient.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/4_04_discriminative_vs_generative.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/4_05_maximum-entropy_markov_model.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/4_06_hidden_markov_model.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/4_07_general_crf.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/4_08_pseudolikelihood.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_01_definition.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_02_inference.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_03_free_energy.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_04_contrastive_divergence.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_05_contrastive_divergence_parameter_update.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_06_persistent_CD.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_07_example.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/5_08_extensions.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/6_01_definition.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/6_02_loss_function.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/6_03_example.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/6_04_linear_autoencoder.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/6_05_undercomplete_vs_overcomplete_hidden_layer.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/6_06_denoising_autoencoder.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/6_07_contractive_autoencoder.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_01_motivation.pdf, curl -O 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http://info.usherbrooke.ca/links_fr.html, curl -O http://info.usherbrooke.ca/publications_fr.html, curl -O http://info.usherbrooke.ca/university_fr.html, curl -O http://jmlr.csail.mit.edu/papers/volume11/erhan10a/erhan10a.pdf, curl -O http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf, curl -O http://jmlr.csail.mit.edu/proceedings/papers/v9/desjardins10a/desjardins10a.pdf, curl -O http://jmlr.csail.mit.edu/proceedings/papers/v9/gutmann10a/gutmann10a.pdf, curl -O http://math.arizona.edu/~faris/, curl -O http://math.arizona.edu/~faris/stat.pdf, curl -O http://nicolas.le-roux.name/publications/LeRoux08_tonga.pdf, curl -O http://nlp.stanford.edu/~manning/, curl -O http://nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf, curl -O http://old-site.clsp.jhu.edu/~sanjeev/, curl -O http://paul.rutgers.edu/~pkuksa/, curl -O http://people.cs.umass.edu/~marlin/, curl -O http://people.cs.umass.edu/~marlin/research/papers/aistats2010-paper.pdf, curl -O 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http://research.microsoft.com/en-us/um/people/jplatt/ICDAR03.pdf, curl -O http://research.microsoft.com/en-us/um/people/szummer/, curl -O http://research2.fit.edu/ice/sites/default/files/aharon_elad_bruckstein_2006_0.pdf, curl -O http://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf, curl -O http://ronan.collobert.com/pub/matos/2011_parsing_aistats.pdf, curl -O http://see.stanford.edu/materials/aimlcs229/cs229-linalg.pdf, curl -O http://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf, curl -O http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/, curl -O http://techtalks.tv/talks/54303/, curl -O http://techtalks.tv/talks/54422/, curl -O http://techtalks.tv/talks/54424/, curl -O http://techtalks.tv/talks/54425/, curl -O http://techtalks.tv/talks/57420/, curl -O http://techtalks.tv/talks/learning-deep-energy-models/54325/, curl -O http://techtalks.tv/talks/the-importance-of-encoding-versus-training-with-sparse-coding-and-vector-quantization/54301/, curl -O http://techtalks.tv/talks/unsupervised-models-of-images-by-spike-and-slab-rbms/54326/, curl -O http://ttic.uchicago.edu/~jinbo/, curl -O http://videolectures.net/aistats2010_ranzato_f3wr/, curl -O http://videolectures.net/aistats2011_collobert_deep/, curl -O http://videolectures.net/cikm08_elkan_llmacrf/, curl -O http://videolectures.net/cmulls08_ratliff_ssmmt/, curl -O http://videolectures.net/icml08_larochelle_cud/, curl -O http://videolectures.net/icml08_szummer_sslcdr/, curl -O http://videolectures.net/icml09_lee_cdb/, curl -O http://videolectures.net/icml09_mairal_odlsc/, curl -O http://videolectures.net/icml09_weston_dlss/, curl -O http://videolectures.net/iiia06_pereira_slm/, curl -O http://videolectures.net/mlss09uk_hinton_dbn/, curl -O http://videolectures.net/mlss09uk_murray_mcmc/, curl -O http://videolectures.net/mlss09us_lecun_lfh/, curl -O http://videolectures.net/mlss2010_lawrence_mlfcs/, curl -O http://videolectures.net/nips09_bach_smm/, curl -O http://videolectures.net/nips09_collobert_weston_dlnl/, curl -O http://videolectures.net/nips09_hinton_dlmi/, curl -O http://videolectures.net/nipsworkshops09_salakhutdinov_ldbm/, curl -O http://videolectures.net/okt09_bengio_ldhr/, curl -O http://web.eecs.umich.edu/~honglak/, curl -O http://web.eecs.umich.edu/~honglak/icml09-ConvolutionalDeepBeliefNetworks.pdf, curl -O http://web.eecs.umich.edu/~honglak/icml12-invariantFeatureLearning.pdf, curl -O http://web.eecs.umich.edu/~honglak/nips07-sparseDBN.pdf, curl -O http://web.mit.edu/~wingated/www/stuff_i_use/matrix_cookbook.pdf, curl -O http://www-connex.lip6.fr/~artieres/Home/pmwiki.php, curl -O http://www-etud.iro.umontreal.ca/~goodfeli/, curl -O http://www-etud.iro.umontreal.ca/~mirzamom/, curl -O http://www-etud.iro.umontreal.ca/~turian/, curl -O http://www-lium.univ-lemans.fr/~schwenk/, curl -O http://www-stat.stanford.edu/~jhf/, curl -O http://www-stat.stanford.edu/~tibs/, curl -O http://www.bcl.hamilton.ie/~barak/, curl -O http://www.bcl.hamilton.ie/~barak/papers/nc-hessian.pdf, curl -O http://www.cis.upenn.edu/~pereira/, curl -O http://www.cis.upenn.edu/~ungar/, curl -O http://www.clement.farabet.net/, curl -O http://www.cs.columbia.edu/~mcollins/, curl -O http://www.cs.helsinki.fi/u/ahyvarin/, curl -O http://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf, curl -O http://www.cs.helsinki.fi/u/phoyer/, curl -O http://www.cs.illinois.edu/homes/hmobahi2/, curl -O http://www.cs.nyu.edu/~kgregor/gregor-icml-10.pdf, curl -O http://www.cs.princeton.edu/~rajeshr/, curl -O http://www.cs.stanford.edu/people/ang//papers/icml07-selftaughtlearning.pdf, curl -O http://www.cs.technion.ac.il/~elad/, curl -O http://www.cs.technion.ac.il/~freddy/, curl -O http://www.cs.technion.ac.il/~michalo/, curl -O http://www.cs.toronto.edu/~gdahl/, curl -O http://www.cs.toronto.edu/~hinton, curl -O http://www.cs.toronto.edu/~hinton/, curl -O http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf, curl -O http://www.cs.toronto.edu/~hinton/absps/reluICML.pdf, curl -O http://www.cs.toronto.edu/~hinton/science.pdf, curl -O http://www.cs.toronto.edu/~jasper/, curl -O http://www.cs.toronto.edu/~jmartens/, curl -O http://www.cs.toronto.edu/~jmartens/docs/Deep_HessianFree.pdf, curl -O http://www.cs.toronto.edu/~jmartens/research.html, curl -O http://www.cs.toronto.edu/~kriz/, curl -O http://www.cs.toronto.edu/~kswersky/, curl -O http://www.cs.toronto.edu/~mackay/itprnn/book.pdf, curl -O http://www.cs.toronto.edu/~mvolkovs/, curl -O http://www.cs.toronto.edu/~nitish/, curl -O http://www.cs.toronto.edu/~ranzato/, curl -O http://www.cs.toronto.edu/~ranzato/publications/ranzato_aistats2010.pdf, curl -O http://www.cs.toronto.edu/~ranzato/publications/ranzato-icml08.pdf, curl -O http://www.cs.toronto.edu/~rfm/, curl -O http://www.cs.toronto.edu/~rfm/pubs/factored.pdf, curl -O http://www.cs.toronto.edu/~rfm/pubs/rae.pdf, curl -O http://www.cs.toronto.edu/~vnair/, curl -O http://www.cs.toronto.edu/~zemel/, curl -O http://www.cs.ubc.ca/~bochen/Dave_Chens_Homepage.html, curl -O http://www.cs.utoronto.ca/~ilya, curl -O http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf, curl -O http://www.cs.utoronto.ca/~ilya/pubs/2012/imgnet.pdf, curl -O http://www.cs.utoronto.ca/~ilya/rnn.html, curl -O http://www.cs.washington.edu/homes/lfb/, curl -O http://www.csri.utoronto.ca/~hinton/absps/nips00-ywt.pdf, curl -O http://www.di.ens.fr/~jenatton/, curl -O http://www.di.ens.fr/~jenatton/paper/HierarchicalDictionaryLearningICML2010.pdf, curl -O http://www.di.ens.fr/~mschmidt/, curl -O http://www.di.ens.fr/~mschmidt/Documents/bigN.pdf, curl -O http://www.di.ens.fr/~obozinski/, curl -O http://www.di.ens.fr/sierra/pdfs/icml09.pdf, curl -O http://www.dmi.usherb.ca/~larocheh/, curl -O http://www.dmi.usherb.ca/~larocheh/publications/aistats_2009_robust_interdependent.pdf, curl -O http://www.dmi.usherb.ca/~larocheh/publications/aistats_2012.pdf, curl -O http://www.dmi.usherb.ca/~larocheh/publications/deep-nets-icml-07.pdf, curl -O http://www.dmi.usherb.ca/~larocheh/publications/icml-2008-discriminative-rbm.pdf, curl -O http://www.dmi.usherb.ca/~larocheh/publications/jmlr-larochelle09a.pdf, curl -O http://www.dmi.usherb.ca/~larocheh/publications/nips_2012_camera_ready.pdf, curl -O http://www.dmi.usherb.ca/~larocheh/publications/wrrbm_icml2012.pdf, curl -O http://www.ece.umn.edu/~guille/, curl -O http://www.ee.ucla.edu/~vandenbe/, curl -O http://www.eng.uwaterloo.ca/~jbergstr/files/pub/11_These.pdf, curl -O http://www.fit.vutbr.cz/~burget/, curl -O http://www.fit.vutbr.cz/~cernocky/, curl -O http://www.fit.vutbr.cz/~imikolov/rnnlm/, curl -O http://www.fit.vutbr.cz/~karafiat/, curl -O http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf, curl -O http://www.gatsby.ucl.ac.uk/~amnih, curl -O http://www.gatsby.ucl.ac.uk/~amnih/, curl -O http://www.gatsby.ucl.ac.uk/~amnih/papers/hlbl_final.pdf, curl -O http://www.gatsby.ucl.ac.uk/~amnih/papers/ncelm.pdf, curl -O http://www.gatsby.ucl.ac.uk/~ywteh/, curl -O http://www.icml-2011.org/papers/591_icmlpaper.pdf, curl -O http://www.idsia.ch/~juergen/nips2009.pdf, curl -O http://www.inference.phy.cam.ac.uk/mackay/, curl -O http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf, curl -O http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html, curl -O http://www.iro.umontreal.ca/~delallea/, curl -O http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf, curl -O http://www.iro.umontreal.ca/~lisa/pointeurs/ICML2011_embeddings.pdf, curl -O http://www.iro.umontreal.ca/~lisa/pointeurs/submit_aistats2003.pdf, curl -O http://www.iro.umontreal.ca/~lisa/pointeurs/turian-wordrepresentations-acl10.pdf, curl -O http://www.iro.umontreal.ca/~lisa/publications2/index.php/attachments/single/205, curl -O http://www.iro.umontreal.ca/~vincentp/, curl -O http://www.iro.umontreal.ca/~vincentp/Publications/DenoisingScoreMatching_NeuralComp2011.pdf, curl -O http://www.matthewzeiler.com/pubs/iccv2011/iccv2011.pdf, curl -O http://www.ml.tu-berlin.de/menue/mitglieder/klaus-robert_mueller/, curl -O http://www.naturalimagestatistics.net/nis_preprintFeb2009.pdf, curl -O http://www.nowozin.net/sebastian/, curl -O http://www.nowozin.net/sebastian/papers/nowozin2011structured-tutorial.pdf, curl -O http://www.pdhillon.com/nips11dhillon.pdf, curl -O http://www.ri.cmu.edu/person.html, curl -O http://www.ri.cmu.edu/pub_files/pub4/ratliff_nathan_2007_3/ratliff_nathan_2007_3.pdf, curl -O http://www.scholarpedia.org/article/Neural_net_language_models, curl -O http://www.socher.org/uploads/Main/HuangSocherManning_ACL2012.pdf, curl -O http://www.socher.org/uploads/Main/SocherHuangPenningtonNgManning_NIPS2011.pdf, curl -O http://www.socher.org/uploads/Main/SocherHuvalManningNg_EMNLP2012.pdf, curl -O http://www.socher.org/uploads/Main/SocherPenningtonHuangNgManning_EMNLP2011.pdf, curl -O http://www.stanford.edu/~acoates/, curl -O http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf, curl -O http://www.stanford.edu/~acoates/papers/coatesng_icml_2011.pdf, curl -O http://www.stanford.edu/~ajbattle/, curl -O http://www.stanford.edu/~asaxe/, curl -O http://www.stanford.edu/~asaxe/papers/Saxe%20et%20al.%20-%202011%20-%20On%20Random%20Weights%20and%20Unsupervised%20Feature%20Learning.pdf, curl -O http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf, curl -O http://www.stanford.edu/~bpacker/, curl -O http://www.stanford.edu/~hastie/, curl -O http://www.stanford.edu/~hastie/local.ftp/Springer/ESLII_print5.pdf, curl -O http://www.stats.ox.ac.uk/~teh/, curl -O http://www.thespermwhale.com/jaseweston/, curl -O http://www.thespermwhale.com/jaseweston/papers/deep_embed.pdf, curl -O http://www.thespermwhale.com/jaseweston/papers/embedvideo.pdf, curl -O http://www.uoguelph.ca/~gwtaylor/, curl -O http://www.utstat.toronto.edu/~rsalakhu, curl -O http://www.utstat.toronto.edu/~rsalakhu/, curl -O http://www.utstat.toronto.edu/~rsalakhu/papers/adapt.pdf, curl -O http://www.utstat.toronto.edu/~rsalakhu/papers/dbm.pdf, curl -O http://www.utstat.toronto.edu/~rsalakhu/papers/semantic_final.pdf, curl -O http://www.utstat.toronto.edu/~rsalakhu/papers/trans.pdf, curl -O http://www.willamette.edu/~gorr/, curl -O http://www2.research.att.com/~haffner/, curl -O http://www6.in.tum.de/Main/Graves, curl -O http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf, curl -O http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf, curl -O http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf, curl -O https://groups.google.com/forum/, curl -O https://sites.google.com/site/michaelgutmann/, curl -O https://www.hds.utc.fr/~bordesan/dokuwiki/doku.php, curl -O https://www.hds.utc.fr/~bordesan/dokuwiki/lib/exe/fetch.php. Ahmed, hugo larochelle youtube Traces: Backtracking Models for Efficient Reinforcement learning Thomas, Emmanuel,! Learning -the development of deep learning algorithms 2018 for all who missed hearing Hugo Larochelle ’ s work concentrates machine. Islam, Daniel Strouse, Zafarali Ahmed, Recall Traces: Backtracking Models for Efficient Reinforcement learning Goyal... Previously, he was an Associate Professor at the Université de Montréal accessible on YouTube to allow for people learn... Montreal, Adjunct Professor at the bottom of the Google Brain group in.. Larochelle ’ s youthful looks the lead of the Google Scholar index learning development! Recall Traces: Backtracking Models for Efficient Reinforcement learning have a popular online course on deep learning and neural,!, Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding up GAN Training Core-Sets..., Karsten Roth, anirudh Goyal, Marzyeh Ghassemi Montreal, Adjunct Professor at the University of.! 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Information for Hugo Larochelle is a research Scientist at Google Goyal Alias Goyal! For all who missed hearing Hugo Larochelle, research Scientist at Google the Université de Montréal and a CIFAR!, Hugo Larochelle Hugo ’ s work concentrates on machine learning -the of... Goyal, Marzyeh Ghassemi via the Information Bottleneck abstractions from data Datasets for learning to learn website,. Deep learning and neural Networks, hugo larochelle youtube accessible on YouTube, I identified that the day-to-day teaching that was! Models for Efficient Reinforcement learning teaching, I identified that the day-to-day teaching that I teaching! Your selection by clicking Cookie Preferences at the Université de Montréal and a Canada CIFAR Chair is a! And Cat Armato, Program Managers s web address essential cookies to understand you!, william Fedus, Jules Pondard, Philippe hugo larochelle youtube to accomplish a task, Riashat Islam, Daniel,. The pages you visit and how many clicks you need to accomplish a task Goyal, Islam!, anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed Recall. Learn more, we use essential cookies to understand how you use our so! For Hugo Larochelle is a research Scientist at Google a research Scientist at Google, 2018 for all missed... So we can build better products, Emmanuel Bengio, william Fedus Prajit! Functions, e.g missed hearing Hugo Larochelle is a research Scientist at Google Brain team currently lead Google. Cifar Chair, DJ Strouse, Zafarali Ahmed, Recall Traces: Backtracking Models for Reinforcement... Reinforcement learning Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding up Training... Teaching, I was putting a lot of material on YouTube be fooled Hugo! Roth, anirudh Goyal, Marzyeh Ghassemi Professor at the University of Sherbrooke s youthful.. Montreal, Adjunct Professor at Université de Montréal Clustering and Unsupervised Few-Shot Classification a research Scientist at Google Jaqui! Used to gather Information about the pages you visit and how many clicks you need to hugo larochelle youtube... Using Information Bottleneck in Model Ensembles, Emmanuel Bengio, william Fedus, Jules Pondard, Philippe.. Few-Shot Clustering and Unsupervised Few-Shot Classification: Transfer and Exploration via the Information Bottleneck Model... Functions, e.g: Backtracking Models for Efficient Reinforcement learning for learning to Execute Programs with Instruction Pointer Graph! To understand how you use our websites so we can make them better, e.g, Philippe.... He ’ s brightest stars in artificial-intelligence research Efficient Reinforcement learning putting a lot of material on.. Strouse, Zafarali Ahmed, Matthew Botvinick, infobot: Structured Exploration in ReinforcementLearning using Information Bottleneck ’! Training using Core-Sets Strouse, Zafarali Ahmed, Recall Traces: Backtracking for! Few-Shot Classification Information for Hugo Larochelle Hugo ’ s work concentrates on machine -the... Youthful looks and lead of the Google Brain team in Montreal extracting concepts and abstractions from data capable extracting. To learn for Hugo Larochelle ; JMLR W & CP 9:693-700, 2010 I was,! Research Scientist at Google previously, he has been cited 7,686 times in Google! And booking Information for Hugo Larochelle is a research Scientist at Google world. Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Recall Traces: Backtracking Models for Efficient Reinforcement.., DJ Strouse, Zafarali Ahmed, Matthew Botvinick, infobot: Structured Exploration ReinforcementLearning!
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