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

Dzmitry Bahdanau dimabgv at gmail.com
Mer 30 Aou 14:04:06 EDT 2017


Because we don't have the keys *the talk is moved to AA3195*.

Dima

On Wed, 30 Aug 2017 at 13:51 Dzmitry Bahdanau <dimabgv at gmail.com> wrote:

> 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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