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

Dzmitry Bahdanau dimabgv at gmail.com
Mer 30 Aou 10:10:19 EDT 2017


A kind reminder: Iulian's talk is today! And it's also a LURG meeting,
2-in-1.

On Mon, 28 Aug 2017 at 08:40 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> 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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