Andrew Ng and Masoumeh Haghpanahi the team's new paper -- Cardiologist-level arrhythmia detection from ECG using deep learning.

https://www.nature.com/articles/s41591-018-0268-3


https://stanfordmlgroup.github.io/projects/ecg2/

https://t.iss.one/ArtificialIntelligenceArticles

#DeepLearning #MachineLearning #artificalintelligence

@ArtificialIntelligenceArticles

Introducing FastBert — A simple Deep Learning library for BERT Models
Blog by Kaushal Trivedi: https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384
#MachineLearning #ArtificialIntelligence #NLP #Bert #NaturalLanguageProcessing

A curated list of gradient boosting research papers from the last 25 years with implementations. It covers NeurIPS, ICML, ICLR, KDD, ICDM, CIKM, AAAI etc.
https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers

Very interesting work applying machine learning to higher order logics and theorem proofs. This could eventually change how we understand and program many different things.

https://arxiv.org/abs/1904.03241

DeepMind Made a Math Test For Neural Networks
https://arxiv.org/abs/1904.01557

Should AI Research Try to Model the Human Brain?


https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30061-0

17 equations that changed the world

Attentive Generative Adversarial Network for Raindrop Removal from A Single Image

Abstract : "Raindrops adhered to a glass window or camera lens can severely hamper the visibility of a background scene and degrade an image considerably. In this paper, we address the problem by visually removing raindrops, and thus transforming a raindrop degraded image into a clean one. The problem is intractable, since first the regions occluded by raindrops are not given. Second, the information about the background scene of the occluded regions is completely lost for most part. To resolve the problem, we apply an attentive generative network using adversarial training (...)."

Qian et al.: https://arxiv.org/pdf/1711.10098.pdf

#artificialintelligence #deeplearning #generativeadversarialnetwork

DLTK – Deep Learning Toolkit for Medical Image Analysis, built on top of TensorFlow
Fast prototyping with a low entry threshold & reproducibility in image analysis applications, with a particular focus on medical imaging.

https://github.com/DLTK/DLTK

News & Views: In Nature Methods, two research teams report substantial improvements in the accurate prediction of fragment ion spectra by deep neural networks.

https://www.nature.com/articles/s41592-019-0428-5.epdf

This Review, published in Nature Reviews Genetics, describes different deep learning techniques and how they can be applied to extract biologically relevant information from large, complex genomic data sets.
https://www.nature.com/articles/s41576-019-0122-6.epdf

Discovering Neural Wirings (https://arxiv.org/abs/1906.00586)

In the past years developing deep neural architectures either required manual design (e.g. AlexNet, ResNet, MobileNet, ...) or require expensive search among possible predefined block structures of layers (NAS, MNas, DART,...). What if we see a neural network as a completely unstructured graph? where each node is running a simple sensing operation over a single data-point or a channel (e.g. 2d filter) and all the nodes are wired up massively in the network. In this paper we explain how to discover a good wiring of a neural network that minimizes the loss function with a limited amount of computation. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training – as we learn the network parameters we also learn the structure itself.

MelNet: A Generative Model for Audio in the Frequency Domain
Sean Vasquez and Mike Lewis: https://arxiv.org/abs/1906.01083
Blog: https://sjvasquez.github.io/blog/melnet/
#ArtificialIntelligence #DeepLearning #MachineLearning

A summary of the debate on human-level AI organized by the World Science Festival last Friday.
I shared the stage with Gary Kasparov, Shannon Vallor, Hod Lipson, and moderator Daniel Sieberg.


https://www.zdnet.com/article/artificial-general-intelligence-is-a-rorschach-test-do-we-need-orangutans/

Functional Adversarial Attacks
Cassidy Laidlaw and Soheil Feizi: https://arxiv.org/abs/1906.00001
#ArtificialIntelligence #DeepLearning #MachineLearning

Deep Learning and the Game of Go

GitHub : https://github.com/maxpumperla/deep_learning_and_the_game_of_go

#artificialintelligence #machinelearning #reinforcementlearning

IoT Network Security from the Perspective of Adversarial Deep Learning. arxiv.org/abs/1906.00076

GRAM: Scalable Generative Models for Graphs with Graph Attention Mechanism
Kawai et al.: https://arxiv.org/abs/1906.01861
#ArtificialIntelligence #GenerativeModels #MachineLearning

Unsupervised Object Segmentation by Redrawing
Chen et al.: https://arxiv.org/abs/1905.13539
#ArtificialIntelligence #DeepLearning #MachineLearning

Nice collection of PyTorch (and some TF) Jupyter notebooks for everything deep learning by Sabastian Raschka.

https://github.com/rasbt/deeplearning-models