Nos « Tea Talks »

Nos conférences sont généralement présentées les vendredis à 13:30, salle AA-6214.

L’horaire de ces conférences et quelques unes des diapositives présentées sont disponibles (voir ci-dessous). Si vous trouvez un de ces articles intéressant, envoyez un courriel à .

Dates [M/D/Y]
Time
Speakers
Affiliation
Place
Titles
1/13/201711:00Myriam, Fred, YoshuaMILAAA3195Welcome Meeting MILA
1/20/201713:30Prof. Brendan FreyUniversity of TorontoAA6214Using Machine Learning to Detect and Treat Genetic Disease
1/27/201713:30Çağlar Gülçehre & Vincent DumoulinMILAAA6214TARDIS: an RNN with Wormhole Connections & A Learned Representation for Artistic Style
2/3/201713:30Prof. Hervé LombaertL'École de technologie supérieure de MontréalAA6214Spectral Matching & Learning of Surface Data - Example on Brain Surfaces
2/10/201713:30Alex ShestopaloffUniversity of TorontoAA6214Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method
2/17/201713:30Jean-Marc RousseauIVADOAA6214Presentation on entrepreneurship
2/24/201713:30no speakerno affiliationno roomNo Tea Talk - ICML deadline
3/3/201713:30Zhouhan Lin & Kundan KumarMILAAA6214A Structured Self-Attentive Sentence Embedding & Sample RNN: An Unconditional End-to-End Neural Audio Generation Model
3/7/201714:30Martin ArjovskyNYUAA6214On Different Distances Between Distributions and Generative Adversarial Networks
3/10/201713:30Jörn DiedrichsenUniversity of Western OntarioAA6214The brain’s GPU?
In search of the cerebellum’s universal computation
3/17/201713:30Devon Hjelm & Laurent DinhMILAAA6214Boundary-Seeking Generative Adversarial Networks & Sharp Minima Can Generalize For Deep Nets
3/24/201713:45Andreas MoshovosUniversity of TorontoAA6214Exploiting Value Content to Accelerate Inference with Convolutional Neural Networks
3/31/201713:45Brian ZiebartUniversity of Illinois at ChicagoAA5340Supervised Machine Learning as an Adversarial Game
4/7/201713:45Amir MoravejBotler AIZ209AI-Powered Immigration Chatbot
4/14/201713:45N/AN/AN/APublic holiday
4/21/201713:45Michael JamesCerebras SystemsAA5340Continuous Propagation and Computer Architectures Specialized for Deep Learning
5/5/201713:45Timnit GebruStanford UniversityAA5340Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
5/12/201713:45N/AN/AN/AOne week before NIPS deadline
5/19/201713:45N/AN/AN/ANIPS Deadline
5/26/201713:45Marco PedersoliL'École de technologie supérieure de MontréalAA5340Areas of Attention for Image Captioning
6/2/201713:45Christian Gagné & Jean-François LalondeUniversity of LavalAA5340Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks & Learning to Predict Illumination from a Single Image
6/9/201713:45Danny TarlowGoogle Brain MontrealAA5340Learning to Code: Machine Learning for Program Induction
6/13/201714:00Jackie CheungMcGillZ209Leveraging External Knowledge for Machine Comprehension of Rare Entities
6/15/201711:00Alexandros DimakisUniversity of Texas at AustinAA6214Generative Models and Compressed Sensing
6/23/2017N/AN/AN/APublic holiday
6/30/2017N/AN/AN/ADeep learning summer school
7/7/201713:45Rémi Leblond & Jean-Baptiste AlayracINRIAAA3195SeaRNN: Training RNNs with Global-Local Losses & Joint discovery of objects and manipulation actions
7/14/201713:45Ethan PerezRice UniversityAA6214Learning Visual Reasoning Without Strong Priors
7/21/201713:45Eugene VorontsovÉcole polytechnique de MontréalAA6214On orthogonality and learning recurrent networks with long term dependencies
7/28/201713:45James WrightMicrosoft Research NYCAA3195Deep Learning for Predicting Human Strategic Behavior
8/4/201713:45Yacine JerniteNYUAA6214Learning Representations of Language from Text
8/11/2017N/AN/AN/AICML
8/18/2017N/AN/AN/Ano volunteers
8/24/201713:30Yaniv RomanoTechnionZ-209A Quest for a Universal Model for Signals: From Sparsity to ConvNets
8/25/201713:45Georgy DerevyankoConcordiaAA6214Protein folding project
9/1/2017Andrew Jesson
9/8/2017Karan Grewal

Voir le Google doc

Diapositives disponibles