​​TOWARDS FEDERATED LEARNING AT SCALE: SYSTEM DESIGN

Google published how they do #FederatedLearning at scale on tens of millions of mobile phones. This is about training model on decentralized data.

ArXiV: https://arxiv.org/pdf/1902.01046.pdf

#Google #Privacy

​​Learning to Generalize from Sparse and Underspecified Rewards

Applying reinforcement learning to environments with sparse and underspecified rewards is an ongoing challenge, requiring generalization from limited feedback. Novel method that provides more refined feedback to the agent.

Link: https://ai.googleblog.com/2019/02/learning-to-generalize-from-sparse-and.html

#Google #RL

​​How 20th Century Fox uses ML to predict a movie audience

All modern blockbusters seem the same. They have common patterns of more exciting periods following less exciting, rotating emotional moments with action period. It is more about following well-known structure and template to make a well-boxing movie, than about director’s skill. No suprise, that #ML can be used to predict success of the movie by its trailer.

Link: https://cloud.google.com/blog/products/ai-machine-learning/how-20th-century-fox-uses-ml-to-predict-a-movie-audience

#DL #LAindustry #Google

Introducing Activation Atlases by #OpenAI

OpenAI in collaboration with #Google created activation atlases, a new technique for visualizing what interactions between neurons can represent.

Link: https://blog.openai.com/introducing-activation-atlases/
Direct demo link: https://distill.pub/2019/activation-atlas/app.html
Github: https://github.com/tensorflow/lucid/#activation-atlas-notebooks

#Google has open-sourced #FederatedLearning code

Step-by-step #tutorial showing how to perform Federated Learning using the same infrastructure Google
uses on 10s of millions of smartphones.

Link: https://medium.com/tensorflow/introducing-tensorflow-federated-a4147aa20041

Coconet: the ML model behind 20th of March Bach Doodle

Network trained to recreate Bach's music.

Link: https://magenta.tensorflow.org/coconet

#magenta #google #audiolearning

​​Reducing the Need for Labeled Data in Generative Adversarial Networks

How combination of self-supervision and semi-supervision can help learn from partially labeled data.

Link: https://ai.googleblog.com/2019/03/reducing-need-for-labeled-data-in.html

#GAN #DL #Google #supervisedvsunsupervised

​​Google announced the updated YouTube-8M dataset

Updated set now includes a subset with verified 5-s segment level labels, along with the 3rd Large-Scale Video Understanding Challenge and Workshop at #ICCV19.

Link: https://ai.googleblog.com/2019/06/announcing-youtube-8m-segments-dataset.html

#Google #YouTube #CV #DL #Video #dataset

​​XLNet: Generalized Autoregressive Pretraining for Language Understanding

Researchers at Google Brain and Carnegie Mellon introduce #XLNet, a pre-training algorithm for natural language processing systems. It helps NLP models (in this case, based on Transformer-XL) achieve state-of-the-art results in 18 diverse language-understanding tasks including question answering and sentiment analysis.

Article: https://towardsdatascience.com/what-is-xlnet-and-why-it-outperforms-bert-8d8fce710335
ArXiV: https://arxiv.org/pdf/1906.08237.pdf

#Google #GoogleBrain #CMU #NLP #SOTA #DL

​​Using Deep Learning to Inform Differential Diagnoses of Skin Diseases

Deep Learning System (DLS) for quicker and cheaper skin diseases detection. DLS showed accuracy across 26 skin conditions on par with U.S. board-certified dermatologists, when presented with identical information about a patient case (images and metadata). This is an amazing example of how technology can help fight notoriously high medical bills in the USA and make top-level care available and more affordable in all other the world.

Link: https://ai.googleblog.com/2019/09/using-deep-learning-to-inform.html?m=1
ArXiV: https://arxiv.org/abs/1909.05382

#Inception4 #Google