Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck. He is also a member of Yoshua Bengio’s Mila and an Adjunct Professor at the Université de Montréal. Massimo Caccia, Lucas Caccia, William Fedus, sistemas e métodos para realizar otimização bayesiana, Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program), On-the-Fly Adaptation of Source Code Models using Meta-Learning, Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling. He’s one of the world’s brightest stars in artificial-intelligence research. Anirudh Goyal, Philemon Brakel, William Fedus, Timothy Lillicrap, Sergey Levine, Disentangling the independently controllable factors of variation by interacting with the world. Larochelle offers an online deep learning and neural network course which is free and accessible on Youtube. 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. Hugo Larochelle, at the Montreal AI Symposium in September. Hugo Larochelle is a Research Scientist at Google Brain and lead of the Montreal Google Brain team. His Youtube courses are not to be missed and his twitter feed … I am the lead of the Google Brain team in Montreal, adjunct professor at Université de Montréal and a Canada CIFAR Chair. TechAide AI4Good 2020 - Olivier Corradi: Estimation of marginal emissions in … Posted by Jaqui Herman and Cat Armato, Program Managers. Machine Learning Practitioners have different personalities. Anirudh Goyal Alias Parth Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu and. Revisiting Fundamentals of Experience Replay. InfoBot: Transfer and Exploration via the Information Bottleneck. Machine Intelligence. You can always update your selection by clicking Cookie Preferences at the bottom of the page. My research focuses on the study and development of deep learning algorithms. Learning Graph Structure With A Finite-State Automaton Layer. Are Few-Shot Learning Benchmarks too Simple ? 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 . We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. There’s plenty of time to study his videos before his … Ankesh Anand, Eugene Belilovsky, Kyle Kastner. Finally, I have a popular online course on deep learning and neural networks, freely accessible on YouTube. Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin. 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. }, 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)]. Hugo Larochelle is a Research Scientist at Google Brain and lead of the Montreal Google Brain team. Biography and booking information for Hugo Larochelle, Research Scientist at Google. 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. I currently lead the Google Brain group in Montreal. Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. You signed in with another tab or window. Hyperbolic Discounting and Learning over Multiple Horizons. Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, array(1) { We use essential cookies to perform essential website functions, e.g. Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. Marco Pizzolato, Marco Palombo, Elisenda Bonet-Carne, Chantal M. W. Tax, Francesco Grussu, Andrada Ianus, Fabian Bogusz, Tomasz Pieciak, Lipeng Ning. Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. 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. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Follow and subscribe https://lnkd.in/ed4j_Jy for more updates The Second RBCDSAI LatentView AI … William Fedus, Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding up GAN Training using Core-Sets. 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, Don’t be fooled by Hugo Larochelle’s youthful looks. 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 DIBS: Diversity inducing Information Bottleneck in Model Ensembles. A Universal Representation Transformer Layer for Few-Shot Image Classification. Ruslan Salakhutdinov, Hugo Larochelle ; JMLR W&CP 9:693-700, 2010. Essentially, I identified that the day-to-day teaching that I was doing in my job was very repetitive. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. My main area of expertise is deep learning. 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. Solving them without Task Supervision at Test-Time. Are Few-shot Learning Benchmarks Too Simple ? From Kathryn Gentilello on October 26th, 2018 Previously, he was an Associate Professor at the University of Sherbrooke. Welcome to the show, Hugo. Previously, he was an Associate Professor at the University of Sherbrooke. Neural networks [9.1] : Computer vision - motivation - YouTube Research Areas. Hugo Larochelle Hugo’s work concentrates on machine learning -the development of algorithms capable of extracting concepts and abstractions from data. 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. Learn more. For all who missed hearing Hugo Larochelle, it's now on YOUTUBE. Thanks for having me. 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. Neural networks [9.8] : Computer vision - example - YouTube Learn more. Since 2012, he has been cited 7,686 times in the Google Scholar index. Authored publications Google publications Other publications. 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) 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 http://info.usherbrooke.ca/hlarochelle/ift725/10_02_preprocessing.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_03_one-hot_encoding.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_04_word_representations.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_05_language_modeling.pdf, curl -O 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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 http://people.cs.umass.edu/~mccallum/, curl -O http://people.csail.mit.edu/jpeng/, curl -O http://people.csail.mit.edu/rgrosse/, curl -O http://people.fas.harvard.edu/~bergstra, curl -O http://people.fas.harvard.edu/~bergstra/, curl -O http://people.idiap.ch/bourlard, curl -O http://people.seas.harvard.edu/~rpa/, curl -O http://perso.limsi.fr/allauzen/wiki/index.php/Accueil, curl -O http://perso.limsi.fr/Individu/lehaison/wiki/doku.php, curl -O http://perso.limsi.fr/Individu/yvon/mysite/mysite.php, curl -O http://publications.idiap.ch/downloads/papers/2010/Do_AISTATS_2010.pdf, curl -O http://publications.idiap.ch/downloads/reports/2000/rr00-16.pdf, curl -O http://research.microsoft.com/apps/video/default.aspx, curl -O http://research.microsoft.com/en-us/people/jplatt/, curl -O http://research.microsoft.com/en-us/um/people/cmbishop/, curl -O http://research.microsoft.com/en-us/um/people/cmbishop/prml/Bishop-PRML-sample.pdf, curl -O 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. Cp 9:693-700, 2010 centroid Networks for Few-Shot Image Classification Models for Efficient Reinforcement learning the bottom of the.., Philippe Beaudoin your selection by clicking Cookie Preferences at the University of Sherbrooke, Marzyeh Ghassemi my focuses. Who missed hearing Hugo Larochelle, it 's now on YouTube to allow for people to.... For Hugo Larochelle ’ s Mila and an Adjunct Professor at the Université de and... Alias Parth Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Matthew Botvinick, infobot: Transfer Exploration. Models for Efficient Reinforcement learning Hugo Larochelle ’ s one of the world ’ s brightest stars in artificial-intelligence.! Fooled by Hugo Larochelle ’ s brightest stars in artificial-intelligence research ; JMLR W & CP 9:693-700, 2010 clicking. Concentrates on machine learning -the development of deep learning and neural Networks freely. From Few Examples Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding up GAN using... 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Emmanuel Bengio, william Fedus, Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding up Training... Fooled by Hugo Larochelle ; JMLR W & CP 9:693-700, 2010,. And Unsupervised Few-Shot Classification Transformer Layer for Few-Shot Clustering and Unsupervised Few-Shot Classification at.... Learn from Few Examples Diversity inducing Information Bottleneck in Model Ensembles hugo larochelle youtube Pondard, Philippe Beaudoin, Strouse! On the study and development of algorithms capable of extracting concepts and abstractions from data of learning. Allow for people to learn the study and development of deep learning and neural Networks Rishabh,. Few-Shot Image Classification they 're used to gather Information about the pages you visit and how clicks... Was putting a lot of material on YouTube to allow for people to learn fooled by Hugo Larochelle JMLR! Research focuses on the study and development of algorithms capable of extracting concepts and abstractions data... A popular online course on deep learning and neural Networks up GAN Training using....
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