[Lisa_seminaires] [TeaTalk] Iulian Serban (Wednesday, Aug 30) and Andrew Jesson (Friday, Sept 1)

Dzmitry Bahdanau dimabgv at gmail.com
Lun 28 Aou 08:40:42 EDT 2017


Hi all,

We are lucky to have two speakers this week!

First,* Iulian Serban* will tell us about the progress of MILA team in
Alexa challenge (*Z-209 between 14h00 - 15h00, August 30th*).

Next, we will learn about deep learning with extreme class imbalance
from *Andrew
Jesson *from Imagia (*AA6214, 13:45  - 14:45, September 1*).

Please find the details below.

Dima

*Speaker:* Iulian Serban

*Title:* A Deep Reinforcement Learning Chatbot

*Abstract*: We present MILABOT: a deep reinforcement learning chatbot
developed at MILA for the Amazon Alexa Prize competition. MILABOT is
capable of conversing with humans on popular small talk topics through both
speech and text. The system consists of an ensemble of natural language
generation and retrieval models, including sequence-to-sequence and latent
variable neural network models. By applying reinforcement learning to
crowd-sourced data and real-world user interactions, the system has been
trained to select an appropriate response from the models in its ensemble.
The system has been evaluated through A/B testing with real-world users,
where it performed excellent compared to competing systems.

*Bio:* Iulian Serban is a PhD candidate at MILA, where he investigates deep
learning and reinforcement learning methods for dialogue and other natural
language processing applications. Iulian is supervised by Yoshua Bengio,
Aaron Courville and Joelle Pineau. He holds an MSc in machine learning from
University College London, and a BSc in theoretical mathematics and
statistics from Copenhagen University. Previously, Iulian worked at
DigiCorpus ApS, where he developed a physiotherapy computer vision system,
now deployed in clinics across Denmark.

*Speaker:* Andrew Jesson

*Title:* CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance

*Abstract: *We introduce CASED, a novel curriculum sampling algorithm that
facilitates the optimization of deep learning segmentation or detection
models on data sets with extreme class imbalance. We evaluate the CASED
learning framework on the task of lung nodule detection in chest CT. In
contrast to two-stage solutions wherein nodule candidates are first
proposed by a segmentation model and then refined by a second detection
stage, CASED improves the training of deep nodule segmentation models (e.g.
UNet) to the point where state of the art results are achieved using only a
trivial detection stage. CASED improves the optimization of deep
segmentation models by allowing them to first learn how to distinguish
nodules from their immediate surroundings, while continuously adding a
greater proportion of difficult-to-classify global context, until uniformly
sampling from the empirical data distribution. Using CASED during training
yields a minimalist proposal to the lung nodule detection problem
competitive the LUNA16 nodule detection benchmark with an average
sensitivity score of 88.7%. Furthermore, we find that models trained using
CASED are robust to nodule annotation quality by showing that comparable
results can be achieved when only a point and radius for each ground truth
nodule are provided during training. Finally, the CASED learning framework
makes no assumptions with regard to imaging modality or segmentation target
and so should generalize to other medical imaging problems where class
imbalance is a persistent problem.

*Bio:* Andrew Jesson is the leader of the new A.I. Research team at Imagia.
He is currently subitting his Master's thesis in electrical engineering
with a focus on medical imaging at McGill University. Andrew has spent the
last 12 years in Montreal where he has aslo earned a B.A. in philosophy
from Concordia University and a B.Eng from McGill.
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