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

Wogrammer has a new story about a female software engineer in Iran, Melika Farahani.

https://medium.com/wogrammer/how-melika-farahani-builds-her-confidence-and-a-path-to-success-41d10026a442

Out With The Old and In With The New: How Samira Korani promotes Artificial Intelligence & Tech in Iran


https://medium.com/wogrammer/out-with-the-old-and-in-with-the-new-how-samira-korani-promotes-artificial-intelligence-tech-in-bd00ae7d4f92 https://t.iss.one/ArtificialIntelligenceArticles

Best paper award at #CVPR2018 :

"Taskonomy: Disentangling Task Transfer Learning"

Abstract : Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks (...).

Paper: https://arxiv.org/pdf/1804.08328.pdf
Data: https://taskonomy.stanford.edu

#award #artificialintelligence #deeplearning #transferlearning

ICYMI: Best Paper Award talk #ICRA2019!

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
paper: https://www.profillic.com/paper/arxiv:1810.10191

They present their results in simulation and on a real robot!

For more like this: https://www.profillic.com

Top 8 Free Must-Read Books on Deep Learning

https://www.kdnuggets.com/2018/04/top-free-books-deep-learning.html

In case that you didn't read that awesome research about wellness. So if you asked yourself why, that's real answer
https://arxiv.org/abs/1802.07068

ML Resources
By Sam Finlayson: https://sgfin.github.io/learning-resources/
#ArtificialIntelligence #DeepLearning #MachineLearning

Overlooked No More: Alan Turing never had an obituary in the New York Times.
Until now.
By Alan Cowell: https://www.nytimes.com/2019/06/05/obituaries/alan-turing-overlooked.html
#AlanTuring #ArtificialIntelligence #Mathematics