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@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@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@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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