An Empirical Study of Example Forgetting During Deep Neural Network Learning
Joint work with Alessandro Sordoni, Remi Tachet, Adam Trischler, Yoshua Bengio, and Geoff Gordon

Paper: https://bit.ly/2H8yQUg
Code: https://bit.ly/2vMH6mw

#ICLR #ICLR2019 #MachineLearning

Learning higher-order sequential structure with cloned HMMs

Dedieu et al.: https://arxiv.org/abs/1905.00507

#ArtificialIntelligence #MachineLearning #Technology

Unified Language Model Pre-training for Natural Language Understanding and Generation
Dong et al.: https://arxiv.org/abs/1905.03197v1
#ArtificialIntelligence #DeepLearning #MachineLearning

A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research.

https://www.technologynetworks.com/neuroscience/news/ai-can-detect-depression-in-a-childs-speech-319040

"The Roles of Supervised Machine Learning in Systems Neuroscience" https://arxiv.org/abs/1805.08239

"Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences"

https://www.cell.com/action/showPdf?pii=S0092-8674%2819%2930391-5

"AI Can Detect Alzheimer’s Disease in Brain Scans Six Years Before a Diagnosis"

News article: https://www.ucsf.edu/news/2018/12/412946/artificial-intelligence-can-detect-alzheimers-disease-brain-scans-six-years

Research article: https://www.ehidc.org/sites/default/files/resources/files/A%20Deep%20Learning%20Model%20to%20Predict%20a%20Diagnosis%20of%20Alzheimer%20Disease.pdf

A new study using machine learning has identified brain-based dimensions of mental health disorders, an advance towards much-needed biomarkers to more accurately diagnose and treat patients.

The research is in Nature Communications. (full open access)

https://neurosciencenews.com/machine-learning-brain-networks-mental-health-9650/

“Godfather of Deep Learning” geoffreyhinton

“We humans are neural nets. What we can do, machines can do”

Peak Hubris. "Smashing the territory with the map".

https://syncedreview.com/2019/05/10/google-i-o-2019-geoffrey-hinton-says-machines-can-do-anything-humans-can/

List of Best Machine Learning Resources


https://medium.com/machine-learning-for-humans/how-to-learn-machine-learning-24d53bb64aa1

PyTorch implementation of the Leap Meta-Learner

GitHub: https://github.com/amzn/metalearn-leap

Paper by Flennerhag et al.: https://arxiv.org/abs/1812.01054

#MachineLearning #ArtificialIntelligence #TransferLearning

Learning Loss for Active Learning

Donggeun Yoo and In So Kweon: https://arxiv.org/abs/1905.03677

#ArtificialIntelligence #DeepLearning #NeuralNetworks

A Survey on Neural Architecture Search
Wistuba et al.: https://arxiv.org/abs/1905.01392
#MachineLearning #ComputerVision #EvolutionaryComputing

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

Sen et al.: https://arxiv.org/abs/1905.03806

#ArtificialIntelligence #DeepLearning #MachineLearning

Flowpoints
An intuitive approach to creating deep learning models
By Marius Brataas: https://github.com/mariusbrataas/flowpoints_ml#readme
#deeplearning #pytorch #machinelearning #python

"Top 8 trends from ICLR 2019"
By Chip Huyen: https://huyenchip.com/2019/05/12/top-8-trends-from-iclr-2019.html
#deeplearning #iclr2019 #technology

Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition

190 Tutorials for young specialists
https://www.kaggle.com/kashnitsky/mlcourse/kernels

GAN Lab: Play with Generative Adversarial Networks (GANs) in your browser!
By created by Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, and Martin Wattenberg: https://poloclub.github.io/ganlab/
Research paper: https://minsuk.com/research/papers/kahng-ganlab-vast2018.pdf
#AI #ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks