​SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models

Sentence embedding is an important research topic in natural language processing (NLP) since it can transfer knowledge to downstream tasks. Meanwhile, a contextualized word representation, called BERT, achieves the state-of-the-art performance in quite a few NLP tasks.

Yet, it is an open problem to generate a high quality sentence representation from BERT-based word models. It was shown in previous study that different layers of BERT capture different linguistic properties. This allows us to fusion information across layers to find better sentence representation.

[GitHub]

https://github.com/BinWang28/SBERT-WK-Sentence-Embedding

[arXiv]
https://arxiv.org/abs/2002.06652

#ai #artificialintelligence #deeplearning #nlp #nlproc #machinelearning

🔗 BinWang28/SBERT-WK-Sentence-Embedding
Code for Paper: SBERT-WK: A Sentence Embedding Method By Dissecting BERT-based Word Models - BinWang28/SBERT-WK-Sentence-Embedding

​Animate your Graphs in Python in 4 Easy Steps!

🔗 Animate your Graphs in Python in 4 Easy Steps!
Getting Creative with Data Visualization using COVID-19 Data

​Вебинар «Удаленный мониторинг и диагностика оборудования на современном производстве»

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​Data Engineer и Data Scientist: что умеют и сколько зарабатывают

🔗 Data Engineer и Data Scientist: что умеют и сколько зарабатывают
Вместе с Еленой Герасимовой, руководителем факультета «Data Science и аналитика» в Нетологии продолжаем разбираться, как взаимодействуют между собой и чем различ...

​Dataprep.eda: Accelerate your EDA

🔗 Dataprep.eda: Accelerate your EDA
Everything you need to know about dataprepare.eda.

​Latent Stochastic Differential Equations

🔗 Latent Stochastic Differential Equations
David Duvenaud | TMLS2019

​It’s just a Minor Thing: Keeping up with the Red Hot Chili Peppers

🔗 It’s just a Minor Thing: Keeping up with the Red Hot Chili Peppers
Tempo analysis of the RHCP with SpotifyR

🎥 PyTorch Tutorial 17 - Saving and Loading Models
👁 1 раз ⏳ 1104 сек.
Learn all the basics you need to get started with this deep learning framework! In this part we will learn how to save and load our model. I will show you the different functions you have to remember, and the different ways of saving our model. I also show you what you must consider when using a GPU.

Functions you must know:
- torch.save()
- torch.load()
- torch.nn.Module().load_state_dict()

Part 17: Saving and Loading Models

If you enjoyed this video, please subscribe to the channel!

Official website:

🎥 Latent Stochastic Differential Equations | David Duvenaud
👁 1 раз ⏳ 1487 сек.
A talk from the Toronto Machine Learning Summit: https://torontomachinelearning.com/
The video is hosted by https://towardsdatascience.com/

About the speaker:
David Duvenaud is an assistant professor in computer science and statistics at the University of Toronto. He holds a Canada Research Chair in generative models. His postdoctoral research was done at Harvard University, where he worked on hyperparameter optimization, variational inference, and chemical design. He did his Ph.D. at the University of

🎥 Машинное обучение для анализа данных RNA-Seq
👁 1 раз ⏳ 1456 сек.
Транскриптом - совокупность всех транскриптов, синтезируемых одной клеткой или группой клеток, включая мРНК и некодирующие РНК

На семинаре мы рассмотрим задачи, возникающие при анализе данных секвенирования транскриптома, а также подходы к их решению с помощью машинного обучения с примерами.

В частности, обсудим CIBERSORT — метод для определения клеточного состава сложных тканей по их профилям экспрессии генов, поговорим о некоторых исследованиях, связанных с определением активности сигнальных путей. Такж

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🔗 Online Courses
Browse the latest online courses from Harvard University, including "CS50's Introduction to Game Development" and "CS50's Web Programming with Python and JavaScript."

​Nevergrad, an evolutionary optimization platform, adds new key features

Facebook AI’s open source Python3 library for derivative-free and evolutionary optimization.

https://ai.facebook.com/blog/nevergrad-an-evolutionary-optimization-platform-adds-new-key-features/

GitHub: https://github.com/facebookresearch/nevergrad

Documentation: https://facebookresearch.github.io/nevergrad/index.html
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Nevergrad, an evolutionary optimization platform, adds new key features
We have added a range of noteworthy new features to Nevergrad, Facebook AI’s open source Python3 library for derivative-free and evolutionary optimization.

​Fractional statistics in anyon collisions

🔗 Fractional statistics in anyon collisions
Elementary particles in three dimensions are either bosons or fermions, depending on their spin. In two dimensions, it is in principle possible to have particles that lie somewhere in between, but detecting the statistics of these so-called anyons directly is tricky. Bartolomei et al. built a collider of anyons in a two-dimensional electron gas of GaAs/AlGaAs (see the Perspective by Feldman). Two beams of anyons collided at a beam splitter and then exited the device at two outputs. The researchers studied the correlations of current fluctuations at the outputs, which revealed signatures of anyonic statistics. Science , this issue p. [173][1]; see also p. [131][2] Two-dimensional systems can host exotic particles called anyons whose quantum statistics are neither bosonic nor fermionic. For example, the elementary excitations of the fractional quantum Hall effect at filling factor ν = 1/ m (where m is an odd integer) have been predicted to obey Abelian fractional statistics, with a phase ϕ associated

​COVID-19: как перестать читать новости и начать анализировать данные

🔗 COVID-19: как перестать читать новости и начать анализировать данные
Привет, Хабр! Где-то месяц назад у меня появилось чувство постоянного беспокойства. Я стал плохо есть, еще хуже спать и постоянно читать-смотреть-слушать тонну...

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Не знаешь чем заняться и где можно заработать?

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и не зависеть от обстоятельств

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​Видео @Databases Meetup: безопасность СУБД, Tarantool в IoT, Greenplum для аналитики Big Data

🔗 Видео @Databases Meetup: безопасность СУБД, Tarantool в IoT, Greenplum для аналитики Big Data
28 февраля прошел митап @Databases, организованный Mail.ru Cloud Solutions. Более 300 участников собрались в Mail.ru Group, чтобы обсудить актуальные проблемы...

​EfficientDet: Towards Scalable and Efficient Object Detection">
EfficientDet: Towards Scalable and Efficient Object Detection

🔗 EfficientDet: Towards Scalable and Efficient Object Detection
Posted by Mingxing Tan, Software Engineer and Adams Yu, Research Scientist, Google Research As one of the core applications in computer ...

​Myths and Reality around Correlation

🔗 Myths and Reality around Correlation
The ultimate guide for getting your correlation concept right

​Pandas Pivot — The Ultimate Guide

🔗 Pandas Pivot — The Ultimate Guide
Everything you’ve always wanted to know about pandas Pivot but were too afraid to ask.

🎥 Python Coding - Spam Detection using Machine Learning
👁 1 раз ⏳ 885 сек.
For more see: https://vinsloev.com/

What is Bayes Teorem?
Describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the probability that a incoming spam mail is related to the total presence of the word “Free”, using Bayes’ theorem the word “Free” can be used to more accurately assess the probability of a mail being spam than can be done without knowledge of the words within the mail.

🎥 AI Show Custom Skills In Azure Cognitive Search
👁 1 раз ⏳ 1342 сек.
This video helps the user understand how to add custom skills to a skillset in Azure Cognitive Search. It explains what it means to enrich content as part of the ingestion pipeline. The video describe the interface for a custom skill and how you can create your own custom skill. It introduces you to power skills so you don’t have to start from scratch

Jump To:
[06:29] Demo Start

Learn More:
Power Skills GitHub https://aka.ms/AzureSearchPowerSkills
Knowledge Mining Solution Accelerator https://github.c