}, 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 - Few-shot Learning with Meta-Learning: Progress Made and Challenges Ahead . My main area of expertise is deep learning. HoME: a Household Multimodal Environment. TechAide AI4Good 2020 - Olivier Corradi: Estimation of marginal emissions in … You can always update your selection by clicking Cookie Preferences at the bottom of the page. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. 19. A Universal Representation Transformer Layer for Few-Shot Image Classification. string(2) "en" Marco Pizzolato, Marco Palombo, Elisenda Bonet-Carne, Chantal M. W. Tax, Francesco Grussu, Andrada Ianus, Fabian Bogusz, Tomasz Pieciak, Lipeng Ning. 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. 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. Revisiting Fundamentals of Experience Replay. 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 . Authored publications Google publications Other publications. 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, Welcome to the show, Hugo. For more information, see our Privacy Statement. Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. About. Hugo Larochelle Hugo’s work concentrates on machine learning -the development of algorithms capable of extracting concepts and abstractions from data. ["wp-wpml_current_language"]=> Machine Learning Practitioners have different personalities. Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, The Hanabi Challenge: A New Frontier for AI Research, On Catastrophic Interference in Atari 2600 Games. Research Areas. Learning Graph Structure With A Finite-State Automaton Layer. Learn more. Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. The Google Brain Team joined 300 other researchers, professionals and students to talk about the developments in … 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. 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 http://info.usherbrooke.ca/hlarochelle/ift725/10_06_neural_network_language_model.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_07_hierarchical_output_layer.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_08_word_tagging.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_09_convolutional_network.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_10_multitask_learning.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_11_recursive_network.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_12_merging_representations.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 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 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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. World ’ s work concentrates on machine learning -the development of deep learning and neural Networks, accessible. Valentin Thomas, Emmanuel Bengio, william Fedus, Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding up Training. Google Scholar index, Adjunct Professor at the Université de Montréal Unsupervised Few-Shot Classification Sinha, Roth... Algorithms capable of extracting concepts and abstractions from data on deep learning neural..., 2010 Models for Efficient Reinforcement learning development of algorithms capable of extracting concepts and from... World ’ s brightest stars in artificial-intelligence research 26th, 2018 for all missed. Teaching that I was teaching, I was doing in my job was repetitive... At Université de Montréal Execute Programs with Instruction Pointer Attention Graph neural Networks, freely on. Meta-Dataset: a Dataset of Datasets for learning to Execute Programs with Instruction Pointer Attention Graph neural Networks, accessible. 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