Tea Talks

Talks are generally on Friday at 13:30 in room AA-6214.

The schedules of the talks and some of the presentation slides are available (see below).
If you find any one of the papers interesting, please email to .

2016 – Fall

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 TorontoAA6214TBA
3/31/201713:30Brian ZiebartUniversity of Illinois at ChicagoAA6214Supervised Machine Learning as an Adversarial Game

See the Google doc

Available slides