5 challenges for the next 5 years of computer vision research by Jitendra Malik at ICCV2019

The latest from TensorFlow
Tensorflow 2.0
Transformers library
Up to 3x training performance improvement
Addons and extensions
Tensorboard, debugging and visualization
Tensorflow Hub: pretrained models
Deploy ML anywhere: TF-extended (server), TF-lite (mobile) and TF-js (web)

https://www.youtube.com/watch?v=n56syJSLouA

CE7454 : Deep Learning for Data Science
Lecture 13: Attention Neural Networks
Xavier Bresson : https://dropbox.com/s/kbrsvhwe2lac1uo/lecture13_attention_neural_networks.pdf?dl=0
Demo :
https://github.com/xbresson/CE7454_2019/blob/master/codes/labs_lecture13/seq2seq_transformers_demo.ipynb
#DeepLearning #DataScience #Transformer

Building the first holographic brain 'atlas'

A team of researchers, led by Case Western Reserve University scientists and technicians using the Microsoft HoloLens mixed reality platform, has created what is believed to be the first interactive holographic mapping system of the axonal pathways in the human brain.

https://medicalxpress.com/news/2019-11-holographic-brain-atlas.html

Fruit identification using Arduino and TensorFlow

By Dominic Pajak and Sandeep Mistry : https://blog.arduino.cc/2019/11/07/fruit-identification-using-arduino-and-tensorflow/

#Arduino #TensorFlow #DeepLearning

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels #NeurIPS2019
Du et al. : https://arxiv.org/abs/1905.13192
GitHub : https://github.com/KangchengHou/gntk
#MachineLearning #ArtificialIntelligence #DeepLearning

Feedforward and feedback processes in visual recognition
https://www.youtube.com/watch?v=a4yoqdUr2hU

HoloGAN (A new generative model) learns 3D representation from natural images

Paper: https://arxiv.org/pdf/1904.01326.pdf
Github: https://github.com/thunguyenphuoc/HoloGAN
Dataset: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
https://www.marktechpost.com/2019/11/04/hologan-a-new-generative-model-learns-3d-representation-from-natural-images/

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
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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