Dropout — метод решения проблемы переобучения в нейронных сетях
https://habrahabr.ru/company/wunderfund/blog/330814/
https://habrahabr.ru/company/wunderfund/blog/330814/
Хабр
Dropout — метод решения проблемы переобучения в нейронных сетях
Переобучение (overfitting) — одна из проблем глубоких нейронных сетей (Deep Neural Networks, DNN), состоящая в следующем: модель хорошо объясняет только пример...
Один из самых значимых результатов в AI Safety (и не только) за последнее время, полученный при коллаборации исследователей из OpenAI и Deepmind.
https://blog.openai.com/deep-reinforcement-learning-from-human-preferences/
https://blog.openai.com/deep-reinforcement-learning-from-human-preferences/
OpenAI
Learning from Human Preferences
One step towards building safe AI systems is to remove the need for humans to
write goal functions, since using a simple proxy for a complex goal, or getting
the complex goal a bit wrong, can lead to undesirable and even dangerous
behavior [https://arxiv…
write goal functions, since using a simple proxy for a complex goal, or getting
the complex goal a bit wrong, can lead to undesirable and even dangerous
behavior [https://arxiv…
AgeHack — первый онлайн-хакатон по продлению жизни на платформе MLBootCamp
https://habrahabr.ru/company/mailru/blog/330960/
https://habrahabr.ru/company/mailru/blog/330960/
Хабр
AgeHack — первый онлайн-хакатон по продлению жизни на платформе MLBootCamp
Сегодня, 15 июня, стартует чемпионат на платформе ML Boot Camp, посвященный проблемам здравоохранения и долголетия человечества. Чемпионат организован нами совм...
Keras implementation of [A simple neural network module for relational reasoning]
https://github.com/Alan-Lee123/relation-network
https://github.com/Alan-Lee123/relation-network
GitHub
Alan-Lee123/relation-network
relation-network - keras implementation of [A simple neural network module for relational reasoning](https://arxiv.org/pdf/1706.01427.pdf)
PyTorch Implementation of "Principled Detection of Out-of-Distribution Examples in Neural Networks" (UIUC, Cornell)
https://github.com/shiyuliang/odin-pytorch
https://github.com/shiyuliang/odin-pytorch
GitHub
ShiyuLiang/odin-pytorch
odin-pytorch - Principled Detection of Out-of-Distribution Examples in Neural Networks
A TensorFlow Implementation of the Transformer: Attention Is All You Need
https://github.com/Kyubyong/transformer
https://github.com/Kyubyong/transformer
GitHub
GitHub - Kyubyong/transformer: A TensorFlow Implementation of the Transformer: Attention Is All You Need
A TensorFlow Implementation of the Transformer: Attention Is All You Need - Kyubyong/transformer
Phase-Functioned Neural Networks for Character Control
http://theorangeduck.com/page/phase-functioned-neural-networks-character-control
http://theorangeduck.com/page/phase-functioned-neural-networks-character-control
Theorangeduck
Phase-Functioned Neural Networks for Character Control
Computer Science, Machine Learning, Programming, Art, Mathematics, Philosophy, and Short Fiction
Feeding Word2vec with tens of billions of items, what could possibly go wrong?
https://www.youtube.com/watch?v=qYpdW9cyEqY&feature=youtu.be&list=PLq-odUc2x7i-9Nijx-WfoRMoAfHC9XzTt
https://www.youtube.com/watch?v=qYpdW9cyEqY&feature=youtu.be&list=PLq-odUc2x7i-9Nijx-WfoRMoAfHC9XzTt
YouTube
#bbuzz 17: Feeding Word2vec with tens of billions of items, what could possibly go wrong?
Feeding Word2vec with tens of billions of items, what could possibly go wrong? Word2vec is an unsupervised algorithm which is able to represent words as vect...
How to Develop a Bidirectional LSTM For Sequence Classification in Python with Keras
http://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/
http://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/
Random Effects Neural Networks in Edward and Keras
http://willwolf.io/2017/06/15/random-effects-neural-networks/
http://willwolf.io/2017/06/15/random-effects-neural-networks/
willwolf.io
Random Effects Neural Networks in Edward and Keras
Coupling nimble probabilistic models with neural architectures in Edward and Keras: "what worked and what didn't," a conceptual overview of random effects, and directions for further exploration.
"Accelerating Deep Learning Research with the Tensor2Tensor Library"+trained translation model releases of: SliceNet, ByteNet, GNMT, Mixture-GNMT, Attention is all You Need
https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html
https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html
Googleblog
Accelerating Deep Learning Research with the Tensor2Tensor Library
Using ANNs on small data – Deep Learning vs. Xgboost
http://maxberggren.se/2017/06/18/deep-learning-vs-xgboost/
http://maxberggren.se/2017/06/18/deep-learning-vs-xgboost/
maxberggren.se
Using ANNs on small data – Deep Learning vs. Xgboost
Andrew Beam does a great job showing that small datasets are not off limits for current neural net methods. If you use the regularisation methods at hand – A...
Practical Deep OCR for scene text using CTPN + CRNN
https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/blob/master/notebooks/OCR/readme.md
https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/blob/master/notebooks/OCR/readme.md
GitHub
AKSHAYUBHAT/DeepVideoAnalytics
DeepVideoAnalytics - A distributed visual search and visual data analytics platform.
Machine Learning for Image Content Analysis
https://exploreai.org/p/machine-learning-image-content-analysis
https://exploreai.org/p/machine-learning-image-content-analysis
exploreai.org
Machine Learning - Image Content Analysis
Human and Machine Judgements about Russian Semantic Relatedness
http://russe.nlpub.ru/downloads/
http://russe.nlpub.ru/downloads/