Unconstrained Monotonic Neural Networks

Antoine Wehenkel and Gilles Louppe : https://arxiv.org/abs/1908.05164

#NeuralNetworks #MachineLearning #NeuralComputing

12 NLP Researchers, Practitioners & Innovators You Should Be Following
Check out this list of NLP researchers, practitioners and innovators you should be following, including academics, practitioners, developers, entrepreneurs, and more.
https://www.kdnuggets.com/2019/08/nlp-researchers-practitioners-innovators-should-follow.html

Conditional Neural Processes
Garnelo et al.: https://arxiv.org/abs/1807.01613
1. Repo: https://github.com/deepmind/neural-processes
2. NoteBook: https://github.com/deepmind/neural-processes/blob/master/conditional_neural_process.ipynb
#ArtificialIntelligence #MachineLearning

A guide to convolution arithmetic for deep learning
Vincent Dumoulin and Francesco Visin : https://arxiv.org/pdf/1603.07285.pdf
#artificialintelligence #deeplearning #machinelearning

CS231n Convolutional Neural Networks for Visual Recognition

https://cs231n.github.io/neural-networks-3/#loss

Sports Matches & Artificial Intelligence
https://www.youtube.com/watch?v=kaslJ-8piSE&feature=youtu.be

Gitflow - Using git in a right way

https://sharetechlinks.com/post/detail/89/gitflow-using-git-in-a-right-way

Does Deep Learning Still Need Backpropagation?
https://syncedreview.com/2019/08/14/does-deep-learning-still-need-backpropagation/

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.