Catalyst and TReNDS have been working together on applications of deep learning for neuroimaging and brain dynamics. This post describes the fundamental concepts implemented in Catalyst.Neuro and introduces different deep learning models comparison on brain segmentation task.
For the last three years, Catalyst-Team has been working on Catalyst — a high-level PyTorch framework for Deep Learning Research and Development. In this post, I would like to share our vision on high-level Deep Learning framework API and show current development progress on various examples.
During the last two years, there was enormous progress in representation learning through the Face Recognition task. Starting from well-known ArcFace in 2018, there were a few other “Faces”: SubCenterArcFace, CosFace, AdaCos, CurricularFace, and more. In this post, we would dive into “Faces”, introduce their intuition, and compare them on a small toy task.
In this tutorial, I would like to go through our first deep learning course homework and introduce your 3 Catalyst main abstractions — Experiment, Runner, and Callback.
In this post, I would like to share with you our development progress for the last month. Let’s check what features we have added to the framework in such a short time.
For real breakthroughs in deep learning, we need a strong foundation. In this blog post, I would like to introduce Catalyst framework, developed with focus on reproducibility, fast experimentation and code/idea reusing. We’ll also provide a tutorial on MNIST classification problem as an example.
NeurIPS –– конференция, которая на данный момент считается самым топовым событием в мире машинного обучения. Сегодня я расскажу вам о своем опыте участия в конкурсах NeurIPS: как потягаться с лучшими академиками мира, занять призовое место и опубликовать статью.
Open AI hosted a reinforcement learning competition — Retro Contest this spring. Main goal was to come up with a meta learning algorithm that can transfer knowledge from a set of training levels of “Sonic The Hedgehog” to a set of previously unseen test levels made specifically by OpenAI. Our team took the 4th place out of 900+ teams.