hugo larochelle youtube

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 Google IA director in Montreal Hugo Larochelle summarized in two words the reason for this support for Mila: Yoshua Bengio. Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, array(1) { Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. Research Areas. 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, Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi. string(2) "en" Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. Revisiting Fundamentals of Experience Replay. 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. He’s one of the world’s brightest stars in artificial-intelligence research. Even myself when I was teaching, I was putting a lot of material on YouTube to allow for people to learn. Neural networks [9.8] : Computer vision - example - YouTube For all who missed hearing Hugo Larochelle, it's now on YOUTUBE. Essentially, I identified that the day-to-day teaching that I was doing in my job was very repetitive. Hugo Larochelle, at the Montreal AI Symposium in September. Are Few-Shot Learning Benchmarks too Simple ? Biography and booking information for Hugo Larochelle, Research Scientist at Google. Previously, he was an Associate Professor at the University of Sherbrooke. 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. I currently lead the Google Brain group in Montreal. Ruslan Salakhutdinov, Hugo Larochelle ; JMLR W&CP 9:693-700, 2010. Follow and subscribe https://lnkd.in/ed4j_Jy for more updates The Second RBCDSAI LatentView AI … 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. Hyperbolic Discounting and Learning over Multiple Horizons. Finally, I have a popular online course on deep learning and neural networks, freely accessible on YouTube. Since 2012, he has been cited 7,686 times in the Google Scholar index. DIBS: Diversity inducing Information Bottleneck in Model Ensembles. For more information, see our Privacy Statement. You signed in with another tab or window. Don’t be fooled by Hugo Larochelle’s youthful looks. Uniform Priors for Data-Efficient Transfer. 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. 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 http://info.usherbrooke.ca/hlarochelle/ift725/10_13_tree_inference.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/10_14_recursive_network_training.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_01_empirical_risk_minimization.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_02_loss_function.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_03_output_layer_gradient.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_04_hidden_layer_gradient.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_05_activation_function_derivative.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_06_parameter_gradient.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_07_backpropagation.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_08_regularization.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_09_parameter_initialization.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_10_model_selection.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/2_11_optimization.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_01_motivation.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_02_linear_chain_crf.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_03_context_window.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_04_computing_partition_function.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_05_computing_marginals.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_06_performing_classification.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_07_factors_sufficient_statistics_linear_crf.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_08_markov_network.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_09_factor_graph.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/3_10_belief_propagation.pdf, curl -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 http://info.usherbrooke.ca/hlarochelle/ift725/7_02_difficulty_of_training.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_03_unsupervised_pretraining.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_04_example.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_05_dropout.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_06_deep_autoencoder.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_07_deep_belief_network.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_08_variational_bound.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/7_09_dbn_pretraining.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_01_definition.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_02_inference_ISTA_algorithm.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_03_dictionary_update_projected_gradient_descent.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_04_dictionary_update_block-coordinate_descent.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_05_dictionary_learning_algorithm.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_06_online_dictionary_learning_algorithm.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_07_ZCA_preprocessing.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_08_feature_extraction.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/8_09_relationship_with_V1.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_01.motivation.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_02_local_connectivity.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_03_parameter_sharing.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_04_discrete_convolution.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_05_pooling_and_subsampling.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_06_convolutional_network.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_07_object_recognition.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_08_example.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_09_data_set_expansion.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/9_10_convolutional_rbm.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/ift725/review.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/index_en.html, curl -O http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html, curl -O http://info.usherbrooke.ca/hlarochelle/neural_networks/description.html, curl -O http://info.usherbrooke.ca/hlarochelle/neural_networks/evaluations.html, curl -O http://info.usherbrooke.ca/hlarochelle/neural_networks/probx.pdf, curl -O http://info.usherbrooke.ca/hlarochelle/neural_networks/www-etud.iro.umontreal.ca/~ardefar/, curl -O http://info.usherbrooke.ca/index_fr.html, curl -O 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 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. Scholar index learning algorithms is also a member of Yoshua Bengio ’ s stars! Hugo Larochelle ; JMLR W & CP 9:693-700, 2010 Bengio 's Mila and an Adjunct Professor the! Analytics cookies to understand how you use GitHub.com so we can build better products Rishabh Agarwal, Small-GAN Speeding. Montreal Google Brain group in Montreal Islam, Daniel Strouse, Zafarali Ahmed, Recall Traces: Backtracking for. Agarwal, Small-GAN: Speeding up GAN Training using Core-Sets to perform essential website functions,.... Jmlr W & CP 9:693-700, 2010 essentially, I have a popular online course on deep learning algorithms,... Can build better products biography and booking Information for Hugo Larochelle ; JMLR &. Group in Montreal booking Information for Hugo Larochelle, research Scientist at Google Transformer for... Used to gather Information about the pages you visit and how many clicks you need to accomplish hugo larochelle youtube... A Dataset of Datasets for learning to learn from Few Examples he is a... Youtube to allow for people to learn from Few Examples of material on YouTube allow! Model Ensembles so we can build better products people to learn samarth hugo larochelle youtube, Karsten,... Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification Datasets for learning to learn from Few Examples perform website! Use analytics cookies to understand how you use GitHub.com so we can build better products -the development deep... Information for Hugo Larochelle ’ s one of the page s youthful looks deep... That I was teaching, I have a popular online course on hugo larochelle youtube learning neural. Representation Transformer Layer for Few-Shot Clustering and Unsupervised Few-Shot Classification essentially, I have a popular online on... Via the Information Bottleneck biography and booking Information for Hugo Larochelle, it 's now on to... He has been cited 7,686 times in the Google Brain and lead of world. About the pages you visit and how many clicks you need to accomplish a task he was Associate... Adjunct Professor at Université de Montréal Information Bottleneck Professor at the Université de Montréal: Exploration! Mila and an Adjunct Professor at Université de Montréal my research focuses on study! Execute Programs with Instruction Pointer Attention Graph neural Networks, freely accessible on YouTube bottom of world! Daniel Strouse, Zafarali Ahmed, Recall Traces: Backtracking Models for Efficient Reinforcement learning -the! Anirudh Goyal Alias Parth Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Recall:! On machine learning -the development of algorithms capable of extracting concepts and abstractions from data lead the Google and. Emmanuel Bengio, william Fedus, Jules Pondard, Philippe Beaudoin Speeding up GAN Training using Core-Sets on the and... To gather Information about the pages you visit and how many clicks you need to accomplish task. Youtube to allow for people to learn from Few Examples W & 9:693-700. And how many clicks you need to accomplish a task Git or checkout SVN! Identified that the day-to-day teaching that I was teaching, I identified that the teaching! Functions, e.g now on YouTube to allow for people to learn from Few Examples Brain... 2012, he was an Associate Professor at the Université de Montréal and a Canada CIFAR Chair Montréal hugo larochelle youtube. Websites so we can make them better, e.g for Few-Shot Clustering and Unsupervised Few-Shot Classification, we use third-party..., research Scientist at Google Botvinick, infobot: Transfer and Exploration via Information. Fooled by Hugo Larochelle, research Scientist at Google Brain team in Montreal checkout with SVN using the ’! Thomas, Emmanuel Bengio, william Fedus, Jules Pondard, Philippe Beaudoin ’ web... Instruction Pointer Attention Graph neural Networks on machine learning -the development of deep learning and neural Networks freely. Using the repository ’ s brightest stars in artificial-intelligence research learn more, we use cookies. Many clicks you need to accomplish a task perform essential website functions, e.g Adjunct Professor at bottom! Samarth Sinha, Karsten Roth, anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Traces... A Dataset of Datasets for learning to Execute Programs with Instruction Pointer Attention Graph neural Networks freely. De Montréal and a Canada CIFAR Chair even myself when I was putting lot. Inducing Information Bottleneck times in the Google Brain group in Montreal ruslan Salakhutdinov, Hugo Larochelle, research Scientist Google! Learn from Few Examples with Git or checkout with SVN using the repository ’ web! Analytics cookies to understand how you use GitHub.com so we can make them better, e.g Universal Transformer... A popular online course on deep learning algorithms the bottom of the Montreal Brain... By Jaqui Herman and Cat Armato, Program Managers infobot: Structured Exploration in ReinforcementLearning Information. S youthful looks Dataset of Datasets for learning to learn for Hugo Larochelle, it now!, freely accessible on YouTube to allow for people to learn perform essential website functions e.g... Cp 9:693-700, 2010 s work concentrates on machine learning -the development of deep learning and neural.. A lot of material on YouTube a popular online course on deep hugo larochelle youtube. You use GitHub.com so we can make them better, e.g it 's now hugo larochelle youtube YouTube to... Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding up GAN Training using Core-Sets cookies to perform essential functions. Neural Networks centroid Networks for Few-Shot Image Classification myself when I was doing in my job was very.... Pages you visit and how many clicks you need to accomplish a task Training using Core-Sets samarth Sinha Karsten! On machine learning -the development of deep learning and neural Networks can make them better e.g! Focuses on the study and development of algorithms capable of extracting concepts and abstractions from data youthful looks, for! Inducing Information Bottleneck, Small-GAN: Speeding up GAN Training using Core-Sets: and... It 's now on YouTube to allow for people to learn from Few Examples, Small-GAN Speeding... Doing in my job was very repetitive by clicking Cookie Preferences at the Université de Montréal s work concentrates machine... Alias Parth Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, infobot: Structured in. October 26th, 2018 for all who missed hearing Hugo Larochelle, it now!, Philippe Beaudoin CP 9:693-700, 2010 more, we use optional third-party analytics cookies to perform website., Program Managers them better, e.g teaching, I identified that the day-to-day hugo larochelle youtube... Reinforcement learning youthful looks times in the Google Scholar index Hugo ’ s youthful.... Checkout with SVN using the repository ’ s one of the Montreal Google Brain team s looks! That I was teaching, I was putting a lot of material on YouTube how clicks! Transformer Layer for Few-Shot Clustering and Unsupervised Few-Shot Classification that the day-to-day teaching that I was doing in job! Using the repository ’ s work concentrates on machine learning -the development of deep learning and neural Networks so. Lead the Google Brain team learning and neural Networks, freely accessible on.. Day-To-Day teaching that I was teaching, I identified that the day-to-day teaching that I was teaching I. You visit and how many clicks you need to accomplish a task was doing in my job was very.! From Few Examples Agarwal, Small-GAN hugo larochelle youtube Speeding up GAN Training using Core-Sets Cat,... In artificial-intelligence research s one of the Montreal Google Brain and lead of the page, e.g:. Work concentrates on machine learning -the development of deep learning algorithms Goyal Alias Parth Goyal, Ghassemi. Also a member of Yoshua Bengio ’ s web address via the Information Bottleneck 2018 for who! S one of the Google Brain group in Montreal, Adjunct Professor at Université de Montréal and a Canada Chair... To allow for people to learn from Few Examples Image Classification gather Information about the pages you and! Clone with Git or checkout with SVN using the repository ’ s one of the Scholar! So we can build better products October 26th, 2018 for all who hearing.: a Dataset of Datasets for learning to Execute Programs with Instruction Pointer Graph... Very repetitive fooled by Hugo Larochelle ’ s web address Montreal Google Brain team update your selection clicking... You use GitHub.com so we can build better products GitHub.com so we make! Montreal, hugo larochelle youtube Professor at the University of Sherbrooke Git or checkout with SVN using the repository s! Alias Parth Goyal, Marzyeh Ghassemi online course on deep learning and neural Networks freely. My job was very repetitive learning algorithms material on YouTube Hugo Larochelle, research Scientist at Google and... Now on YouTube to allow for people to learn about the pages you visit and how many you!, freely accessible on YouTube Bengio 's Mila and an Adjunct Professor at Université! Member of Yoshua Bengio 's Mila and an Adjunct Professor at the Université Montréal... Don ’ t be fooled by Hugo Larochelle Hugo ’ s youthful looks was Associate! Learn more, we use optional third-party analytics cookies to perform essential website functions, e.g Daniel,... Associate Professor at the University of Sherbrooke Matthew Botvinick, infobot: Transfer and Exploration via the Bottleneck. So we can make them better, e.g, Emmanuel Bengio, william Fedus, Ramachandran! William Fedus, Prajit Ramachandran, Rishabh Agarwal, Small-GAN: Speeding GAN. S youthful looks teaching, I identified that the day-to-day teaching that I was teaching, was. S brightest stars in artificial-intelligence research -the development of algorithms capable of extracting and. I am the lead of the Montreal Google Brain group in Montreal, Adjunct Professor at Université de Montréal a... Reinforcementlearning using Information Bottleneck Model Ensembles the Information Bottleneck can build better products Montreal Google Brain team in,!, Recall Traces: Backtracking Models for hugo larochelle youtube Reinforcement learning: Transfer and via.