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

Interpretable Convolutional Filters with SincNet"

Paper by Mirco Ravanelli, Yoshua Bengio: https://arxiv.org/abs/1811.09725

Code: https://github.com/mravanelli/pytorch-kaldi

Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
https://arxiv.org/abs/1811.09236

Code (Fully Advesarial Mosaics):https://github.com/zalandoresearch/famos

Machine Learning Open Source of the Month (v.Nov 2018)

https://goo.gl/oiGj3b

Matching Features without Descriptors: Implicitly Matched Interest Points (IMIPs)

By Cieslewski et al.: https://arxiv.org/abs/1811.10681

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

By Huang et al.: https://arxiv.org/abs/1811.06965

#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning

A General Method for Amortizing Variational Filtering"

By Marino et al.: https://www.yisongyue.com/publications/nips2018_filtering.pdf

#DeepLearning #MachineLearning #NeuralNetworks #NIPS2018

Partial Convolution based Padding

By Liu et al.: https://arxiv.org/pdf/1811.11718.pdf

Code: https://github.com/NVIDIA/partialconv

#artificialintelligence #deeplearning #machinelearning #technology

Summaries of Top AI Research Papers of 2018

By Mariya Yao: https://www.topbots.com/most-important-ai-research-papers-2018/

A combined network and machine learning approaches for product market forecasting

By Fan et al.: https://arxiv.org/abs/1811.10273

#Technology #Physics #Society @ArtificialIntelligenceArticles

Deep Learning for the Masses (… and The Semantic Layer)
https://www.kdnuggets.com/2018/11/deep-learning-masses-semantic-layer.html

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

Deep Learning in the Brain, by Blake Richards. Nice to think about whether backprop-ish processes happen in brains. https://www.youtube.com/watch?v=dZwB5Mj-PPM

"Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups"
https://goo.gl/tUysBT

Self-Attention Generative Adversarial Networks"

Tensorflow implementation: https://github.com/brain-research/self-attention-gan

Paper by Zhang et al.: https://arxiv.org/abs/1805.08318

Synthesizing Tabular Data using Generative Adversarial Networks

By Lei Xu, Kalyan Veeramachaneni: https://arxiv.org/abs/1811.11264

Robust Artificial Intelligence and Robust Human Organizations

Thomas G. Dietterich: https://arxiv.org/abs/1811.10840