Generating a Training Dataset for Land Cover Classification to Advance Global Development. https://arxiv.org/abs/1811.07998

SQuAD2.0: The Stanford Question Answering Dataset

Leaderboard: https://rajpurkar.github.io/SQuAD-explorer/

#artificialintelligence #bigdata #deeplearning #machinelearning #NaturalLanguageProcessing

Predicting Diabetes Disease Evolution Using Financial Records and Recurrent Neural Networks https://arxiv.org/abs/1811.09350

DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution"

Bontrager et al.: https://arxiv.org/abs/1705.07386

Do Better ImageNet Models Transfer Better?

By Simon Kornblith, Jonathon Shlens, Quoc V. Le: https://arxiv.org/abs/1805.08974

#ComputerVision #PatternRecognition #MachineLearning

Explainable cardiac pathology classification on cine MRI with motion characterization https://arxiv.org/abs/1811.03433

Measuring the Effects of Data Parallelism on Neural Network Training

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises.


https://arxiv.org/abs/1811.03600 @ArtificialIntelligenceArticles

Statistical physics of liquid brains

Paper by Jordi Pinero and Ricard Sole: https://www.biorxiv.org/content/biorxiv/early/2018/11/26/478412.full.pdf

#Brains #Evolution #Physics

Visualizing the Loss Landscape of Neural Nets

PyTorch code by Tom Goldstein: https://github.com/tomgoldstein/loss-landscape

#pytorch #neuralnetworks #machinelearning #deeplearning

A primer on deep learning in genomics https://www.nature.com/articles/s41588-018-0295-5 @ArtificialIntelligenceArticles

Dive into Deep Learning

Jupyter Notebooks, PDF, and website, all generated from one source.

By Zhang et al.: https://www.diveintodeeplearning.org/

#machinelearning #deeplearning #artificialintellige

The same #MachineLearning #courses used to train engineers at #Amazon are now available to all developers through #AWS. More than 30 self-service, self-paced digital courses with more than 45 hours of courses, videos, and labs are available at no charge!


https://aws.amazon.com/training/learning-paths/machine-learning/


https://aws.amazon.com/blogs/machine-learning/amazons-own-machine-learning-university-now-available-to-all-developers/

https://t.iss.one/ArtificialIntelligenceArticles

TorchCraftAI

A bot platform for machine learning research on StarCraft: Brood War

GitHub: https://github.com/TorchCraft/TorchCraftAI

Deep Learning and Density Functional Theory. https://arxiv.org/abs/1811.08928

3D Hair Synthesis Using Volumetric Variational Autoencoders https://linjieluo.com/publications/3d-hair-synthesis-using-volumetric-variational-autoencoders/ @ArtificialIntelligenceArticles

Quantum Circuit Learning : https://dkopczyk.quantee.co.uk/qcl/

new paper of andrew ng : MRNet: Deep-learning-assisted diagnosis for knee magnetic resonance imaging

In this #research article, Nicholas Bien & coll present an automated system for interpreting knee magnetic resonance images (#MRI) to prioritize high-risk patients and assist clinicians in making diagnoses.

https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002699

https://t.iss.one/ArtificialIntelligenceArticles

A Structured Approach to Unsupervised Depth Learning from Monocular Videos https://ai.googleblog.com/2018/11/a-structured-approach-to-unsupervised.html

Deep Learning Models are Predicting and Diagnosing Alzheimer’s Disease with Neuroimaging

Alzheimer’s disease remains one of the most challenging diseases to recognize in its early stages. It often takes an experienced clinician to make a proper diagnosis. As there are only some identifying factors for the disease, finding new methods that could be used for creating a diagnosis comes down to future technology in the medical field.

https://www.marktechpost.com/2018/11/27/deep-learning-models-are-predicting-and-diagnosing-alzheimers-disease-with-neuroimaging/

#deeplearning #dl #alzheimers

https://t.iss.one/ArtificialIntelligenceArticles

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing"

Liang et al.: https://arxiv.org/abs/1807.02322

Code: https://github.com/crazydonkey200/neural-symbolic-machines