2021

Showing your offline reinforcement learning work: Online evaluation budget matters

In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms. First, we delineate that the online evaluation budget is problem-dependent, where some problems allow for less but …

Showing your offline reinforcement learning work: Online evaluation budget matters

Over the recent years, vast progress has been made in Offline Reinforcement Learning (Offline-RL) for various decision-making domains: from finance to robotics. However, comparing and reporting new Offline-RL algorithms has been noted as …

Classification in the wild

A few best practices for machine learning classification tasks.

Tinkoff AI – CV

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TTRS: Tinkoff Transactions Recommender System benchmark

Over the past decade, tremendous progress has been made in inventing new RecSys methods. However, one of the fundamental problems of the RecSys research community remains the lack of applied datasets and benchmarks with well-defined evaluation rules …

4th place solution for the SIGIR 2021 challenge.

4th place solution for the SIGIR 2021 challenge.

LRWR: Large-Scale Benchmark for Lip Reading in Russian language

Lipreading, also known as visual speech recognition, aims to identify the speech content from videos by analyzing the visual deformations of lips and nearby areas. One of the significant obstacles for research in this field is the lack of proper …

PyTorch Community Voices | Catalyst

Join us for an interview with star PyTorch community member Sergey Kolesnikov, the creator of Catalyst, a high-level PyTorch framework for Deep Learning Research and Development.

ETNA

Time Series Library

Как мы в SIGIR-соревновании участвовали

Летом этого года на конференции SIGIR проводился Workshop On eCommerce, посвященный прогнозам намерений и рекомендаций. По традиции к воркшопу приурочили небольшое соревнование, посвященное использованию последних наработок в области RecSys. Мы в Tinkoff.AI решили немного развеяться и поучаствовать.