Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems. https://arxiv.org/abs/1908.05480

Automated Rib Fracture Detection of Postmortem Computed Tomography Images Using Machine Learning Techniques
https://arxiv.org/abs/1908.05467

State of the art in speech recognition from Google Researchers:

https://www.profillic.com/paper/arxiv:1907.05337

Improvement in performance by a factor of ~10 in separating speech from different speakers (Speaker diarization)

Advanced Machine Learning Online Course
https://www.eventbrite.com/e/advanced-machine-learning-online-course-tickets-65053562958?_eboga=1634426962.1549905080

gerat paper by DeepMind

Behaviour Suite for Reinforcement Learning


https://arxiv.org/abs/1908.03568v1

AutoML: A Survey of the State-of-the-Art
https://arxiv.org/abs/1908.00709 by Xin He et al.
#MachineLearning #DeepLearning

Deep Mind just released its RL Course :

https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs

Colab ( https://colab.research.google.com ) can open notebooks directly from GitHub by simply replacing "https://github.com" with "https://colab.research.google.com/github/" in the notebook URL.

#colab #jupyternotebook #tensorflow

Understanding XLNet
https://www.borealisai.com/en/blog/understanding-xlnet/

Paper-Title: COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity Driven Exploration
#DeepmindAI
Link to the paper: https://arxiv.org/pdf/1905.09275.pdf
The three specific tasks performed in this paper using SpriteWorld(https://github.com/deepmind/spriteworld) are:-
Goal-finding task. The agent must bring the target sprites (squares) to the centre of the arena.
Clustering task. The agent must arrange the sprites into clusters according to their colour.
Sorting task. The agent must sort the sprites into goal locations according to their colour (each colour is associated with a different goal location).
The main technical contributions of the paper are:-
A method for learning action-conditioned dynamics over slot-structured object-centric representations that require no supervision and is trained from raw pixels.
A method for learning a distribution over a multi-dimensional continuous action space. This learned distribution can be sampled efficiently.
An integrated continuous control agent architecture that combines unsupervised learning, adversarial learning through exploration, and model-based RL.

New models in 17 and 100 languages XLM/mBERT pytorch
LM supports multi-GPU and multi-node training

https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models

The world's largest chip : Cerebras Wafer Scale Engine
A new class of computer system that accelerates artificial intelligence by orders of magnitude beyond the current state of the art - https://www.cerebras.net
#ArtificialIntelligence #DeepLearning #Hardware

Andrew ng :


I’m delighted to announce our first international office in Medellin, Colombia! Landing AI, deeplearning.ai and AI Fund have teams there. AI needs to expand beyond a small handful of hubs like Silicon Valley and Beijing, and I’m bullish about Latin America. See why:

https://medium.com/@andrewng/ai-in-latin-america-announcing-our-first-international-office-in-colombia-ac4e203c1564?source=friends_link&sk=22f7f197049bbeb72489ac25dd6e18ed

Transformers from scratch
Modern transformers are super simple, so they can be explained in a really straightforward manner
Blog by Peter Bloem, with pytorch code : https://peterbloem.nl/blog/transformers
#MachineLearning #PyTorch #Transformers

"Mathematics for Machine Learning"
Book by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong : https://mml-book.github.io
#ArtificialIntelligence #MachineLearning #Mathematics