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

Dzmitry Bahdanau dimabgv at gmail.com
Mer 30 Aou 13:51:22 EDT 2017


The talk will start with *a short delay of ~10 minutes, at 14:10 *(the
speaker is stuck in traffic).

Dima

On Wed, 30 Aug 2017 at 10:10 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> 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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