Deep Learning for Computational Chemistry

Garrett B. Goh, Nathan Oken Hodas, Abhinav Vishnu

Published in Journal of Computational… 2017

DOI:10.1002/jcc.24764

Arxiv Free Download:
https://arxiv.org/abs/1701.04503

Paywall:
https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.24764

#deeplearning #AI #artificialintelligence #chemistry #computationalchemistry

In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics.

By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction.

In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks.

Designing neural networks through Neuroevolution

https://www.nature.com/articles/s42256-018-0006-z

Classification of Histopathology Images with Deep Learning: A Practical Guide
Blog by Jason Wei : https://medium.com/health-data-science/classification-of-histopathology-images-with-deep-learning-a-practical-guide-2e3ffd6d59c5
#MachineLearning #DeepLearning #Healthcare
join
https://t.iss.one/ArtificialIntelligenceArticles

ICCV 2019 Best Paper Award (Marr Prize): SinGAN: Learning a Generative Model from a Single Natural Image https://arxiv.org/abs/1905.01164

Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks
https://www.profillic.com/paper/arxiv:1910.06444
(They compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake)

Modeling Feature Representations for Affective Speech using Generative Adversarial Networks. https://arxiv.org/abs/1911.00030

Deep Learning for Population Genetic Inference
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004845

Tackling Climate Change with Machine Learning

Rolnick et al.: https://arxiv.org/abs/1906.05433

#Artificialintelligence #ClimateChange #MachineLearning

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Zhang et al.: https://arxiv.org/abs/1911.00536
#ArtificialIntelligence #MachineLearning #Transformer

News classification using classic Machine Learning tools (TF-IDF) and modern NLP approach based on transfer learning (ULMFIT) deployed on GCP

By Imad El Hanafi

Live version: https://nlp.imadelhanafi.com/

Github: https://github.com/imadelh/NLP-news-classification

Blog: https://imadelhanafi.com/posts/text_classification_ulmfit/

#DeepLearning #MachineLearning #NLP

Came across this tool that lets you convert images to LaTeX

It works by taking a screenshot of maths and pasting the LaTeX into an editor with a keyboard shortcut
https://mathpix.com/

@ArtificialIntelligenceArticles

How your brain invents morality: fantastic interview of neurophilosopher Patricia Churchland on the neuro-evolutionary origin of morality.

https://www.vox.com/future-perfect/2019/7/8/20681558/conscience-patricia-churchland-neuroscience-morality-empathy-philosophy

Patricia Churchland: I am baffled that in 2019, so many intellectuals are still offended by that ideas that the brain is a machine, and everything it does is some sort of computation, including emotions, morality, etc.
I'm baffled that people still believe that if that is the case, we should think less of humans. We should not.
Science has been bringing humans down from their pedestal for centuries. One should be used to it by now.
Whatever happened to rational thought?

Using electrode implants that feed data into computational models known as neural networks, scientists reconstructed words and sentences from brain activity that were, in some cases, intelligible to human listeners.

https://www.sciencemag.org/news/2019/01/artificial-intelligence-turns-brain-activity-speech

@ArtificialIntelligenceArticles

Designing neural networks through Neuroevolution

https://www.nature.com/articles/s42256-018-0006-z

This Tensorflow based Python Library ‘Spleeter’ splits vocals from finished tracks

Github: https://github.com/deezer/spleeter

https://www.marktechpost.com/2019/11/10/this-tensorflow-based-python-library-spleeter-splits-vocals-from-finished-tracks/

Probabilistic Logic Neural Networks for Reasoning
Meng Qu, Jian Tang : https://arxiv.org/abs/1906.08495
#MachineLearning #ArtificialIntelligence #NeuralNetworks

US National Security Commission on Artificial Intelligence

Interim Report for Congress, November 2019

#AI #ArtificialIntelligence #Security #NSCAI

https://www.nationaldefensemagazine.org/-/media/sites/magazine/03_linkedfiles/nscai-interim-report-for-congress.ashx?la=en