To kick these Meetup series, we will start with a debrief from ICML 2018 conferences during a 25 minutes presentation. We are expecting two long presentations (25 minutes) and two short presentations (15 minutes). **Please, RSVP so that we have an accurate estimate of attendees.*…

To kick these Meetup series, we will start with a debrief from ICML 2018 conferences during a 25 minutes presentation.

We are expecting two long presentations (25 minutes) and two short presentations (15 minutes).

**Please, RSVP so that we have an accurate estimate of attendees.**

Sponsor:

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HKSSG - The Hong Kong Startups Support Group is helping for the organization of this meetup. Gary Ng will present HKSSG and its objectives.

EVEREST Serviced Offices Ltd. is providing for the place. Thanks to them!

Please, feel free to contact us if you are interested in sponsoring (food & drinks) or you want to present something! There is also room for one short pitch (5 min).

Program:

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**Unfortunately, Yaginq presentation of her ICML paper has been cancelled. It will be replaced by Eugene Ho talk.**

Gautier Marti - ICML 2018 field report (25 min)

Jill-Jênn Vie (https://jilljenn.github.io/) - Mangaki (https://mangaki.fr/about/en)

Mangaki is a non-profit recommender system of manga and anime.

From the anime you watched and the manga you read, their algorithm discover new precious pearls that you will love!

If you want to join, there's plenty to do!

(25 min)

Eugene Ho (http://dayta.ai/) - On recent advances in Computer Vision for Human Re-identification

In this talk, I will focus on how the application of techniques such as mutual learning and re-ranking in CNNs can improve the accuracy in human re-identification and other computer vision technologies. We will also explore and discuss some existing papers on the above topics, which some of these concepts inspired our research.

Alignedreid: Surpassing human-level performance in person re-identification

https://arxiv.org/pdf/1711.08184.pdf

Re-ranking person re-identification with k-reciprocal encoding

http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf

Deep Mutual Learning

http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0304.pdf

Gautier Marti (https://gmarti.gitlab.io/) - Autoregressive Convolutional Neural Networks for Asynchronous Time Series

paper: https://arxiv.org/abs/1703.04122

In this talk, I will present a CNN architecture for predicting autoregressive asynchronous time series. I will illustrate its application on predicting traders' quotes of credit default swaps (proprietary dataset from Hellebore Capital), and on artificial time series.

Code is available on Mikołaj Binkowski github: https://github.com/mbinkowski/nntimeseries

(15 min)

*Cancelled*

Yaqing Wang (http://www.cse.ust.hk/~ywangcy/) - Online Convolutional Sparse Coding with Sample-Dependent Dictionary

paper: https://arxiv.org/pdf/1804.10366.pdf

Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing. However, existing methods have limited scalability. Instead of convolving with a dictionary shared by all samples, we propose the use of a sample-dependent dictionary in which each filter is a linear combination of a small set of base filters learned from data.

This added flexibility allows a large number of sample-dependent patterns to be captured, which is especially useful in the handling of large or high-dimensional data sets. Computationally, the resultant model can be efficiently learned by online learning.

Extensive experimental results on a number of data sets show that

the proposed method outperforms existing CSC algorithms with significantly reduced time and space complexities.

(25 min)