SmartData conference talks.

Ivan Yamschikov

The Max Planck Institute, Leipzig, Germany / Creaited Labs

Day 1 / 17:50 / Track 1 / / Введение в технологию

Neurona: why we taught a neural network to write poems in Kurt Cobain style

We'll discuss modern tasks in developing a creative AI and why it is important and interesting, along with sharing our experience of creating Neurona, Neuronnaya Oborona and Pianola.

Dmitry Bugaychenko

Odnoklassniki

Day 1 / 14:25 / Track 1 / / Для практикующих инженеров

From click to predict and back: Data Science pipelines at Odnoklassniki

We will consider one complex task of news feed personalization and talk about getting a model and model predictions, along with different data processing and storage technologies from Hadoop ecosystem.

Artem Marinov

Directual

Day 1 / 15:35 / Track 1 / / Для практикующих инженеров

Segmenting 600 millions of users in real-time mode every day

How we've changed the architecture of the platform to process in real-time 600 million users' data (hundreds of thousands of events per second), which problems we've faced and how we've dealt with them.

Sergey Nikolenko

PDMI RAS

Day 1 / 12:50 / Track 1 / / Для практикующих инженеров

Deep convolutional networks for object detection and image segmentation

In this talk we'll discuss how CNNs have evolved from classifying individual objects to detecting multiple objects on a picture. We'll dwell on YoLo, single-shot detectors, and a range of models from R-CNN till Mask R-CNN.

Alexey Potapov

ITMO

Day 1 / 11:40 / Track 2 / / Хардкор. Сложный низкоуровневый доклад, требующий от слушателя знаний технологии.

Deep learning, probabilistic programming and meta-estimation: point of intersection

Discussing generative and discriminative models' connections in terms of program specialization, their role within the framework of deep learning and probabilistic programming. Addressing neural Bayesian approach, neural probabilistic programming as an integration of two paradigms on an example of Edward library.

Vitaly Khudobakhshov

JetBrains

Day 1 / 10:30 / Track 1 / / Введение в технологию

Name is a feature

We'll talk about the most unexpected and counterintuitive observations you can do using data analysis in social networks, along with the statistical significance of such observations, bot influence, and false correlations.

Artem Grigoriev

Yandex

Day 1 / 11:40 / Track 3 / / Введение в технологию

Crowdsourcing: How to train your crowd

The talk encouraged by the experience of the Yandex.Toloka crowdsourcing platform creation and use shares clues on the quality control, motivation of performers and various models of aggregating separate judgements.

Anna Veronika Dorogush

Yandex

Day 1 / 15:35 / Track 3 / / Для практикующих инженеров

CatBoost — the next generation of gradient boosting

How to effectively use an open-source algorithm of CatBoost gradient boosting, who benefits from it now, where it will be used and who should pay attention to it.

Andrey Boyarov

Mail.Ru Group

Day 1 / 14:25 / Track 3 / / Для практикующих инженеров

Deep Learning: Scene recognition and attractions recognition in the images

In this talk we will discuss the development of a system for solving the problem of scene recognition with the help of a state-of-the-art approach based on deep convolutional neural networks.

Vladimir Krasilschik

Yandex

Day 1 / 14:25 / Track 2 / / Введение в технологию

Back to the future of a modern banking system

This talk will cover Audit-Driven Development and its origin, how to organize a bitemporal database of facts, why a built-in time machine must be in every modern distributed system. Also, we'll share a "universal formula of fact" and what the tasks of so called "analytics" most frequently appear to be.

Alexander Serbul

1C-Bitrix

Day 1 / 12:50 / Track 2 / / Введение в технологию

Applied machine learning in e-commerce — scenarios and architectures of pilots and real-world projects

Presenting a number of company's pilots and real-world projects applying popular and "rare" algorithms of machine learning, as well as technical implementation on different platforms such as Java, PHP, Python using open libraries and a range of Amazon Web Services tools.

Ivan Drokin

BrainGarden

Day 1 / 15:35 / Track 2 / / Введение в технологию

No data? No problem! Deep Learning with CGI

We will consider an example of training deep convolutional networks for object keypoints localization on a fully synthetic data set.

Alexander Sibiryakov

Scrapinghub

Day 1 / 16:45 / Track 3 / / Введение в технологию

Automatic contact information extraction from the web

This talk is about a distributed web-crawler for search and extraction of contact information from corporate websites.

Alexander Krasheninnikov

Badoo

Day 1 / 16:45 / Track 1 / / Для практикующих инженеров

Hadoop high availability: Badoo experience

We will discuss how to provide high availability of Hadoop cluster components and why we need it.

Alexey Natekin

DM Labs, Arktur, Open Data Science

Day 1 / 16:45 / Track 2 / / Для практикующих инженеров

Lock, stock and two boosting barrels

We'll try to figure out if we need a video card in 2017-2018 for gradient boosting learning.

Boris Shminke

ivi

Day 1 / 12:50 / Track 3 / / Для практикующих инженеров

Distributed ML on Big Data: recommender system building experience at ivi

How we walked a path from a single matrix multiplying script to a Hadoop Data Lake of our own, distributed Machine Learning with Spark, offline evaluation framework in Scala and, as a result, a Recommender System highly tunable to business needs.

Mikhail Kamalov

Epam Systems

Day 1 / 11:40 / Track 1 / / Для практикующих инженеров

Recommendation systems: from matrix factorization to deep learning in stream mode

The talk will cover the practical aspects of using deep learning, collaborative and content filtering and filtering by time, as well as approaches for recommendation systems. In addition, the construction of hybrid advisory systems and modifications of approaches for online learning on Spark will be considered.

SmartData talks

Ivan Yamschikov

Neurona: why we taught a neural network to write poems in Kurt Cobain style

Dmitry Bugaychenko

From click to predict and back: Data Science pipelines at Odnoklassniki

Artem Marinov

Segmenting 600 millions of users in real-time mode every day

Sergey Nikolenko

Deep convolutional networks for object detection and image segmentation

Alexey Potapov

Deep learning, probabilistic programming and meta-estimation: point of intersection

Vitaly Khudobakhshov

Name is a feature

Artem Grigoriev

Crowdsourcing: How to train your crowd

Anna Veronika Dorogush

CatBoost — the next generation of gradient boosting

Andrey Boyarov

Deep Learning: Scene recognition and attractions recognition in the images

Vladimir Krasilschik

Back to the future of a modern banking system

Alexander Serbul

Applied machine learning in e-commerce — scenarios and architectures of pilots and real-world projects

Ivan Drokin

No data? No problem! Deep Learning with CGI

Alexander Sibiryakov

Automatic contact information extraction from the web

Alexander Krasheninnikov

Hadoop high availability: Badoo experience

Alexey Natekin

Lock, stock and two boosting barrels

Boris Shminke

Distributed ML on Big Data: recommender system building experience at ivi

Mikhail Kamalov

Recommendation systems: from matrix factorization to deep learning in stream mode

Contacts