Slim-CNN: A Light-Weight CNN for Face Attribute Prediction. arxiv.org/abs/1907.02157

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs. arxiv.org/abs/1907.02154

Learning graph-structured data using Poincar\'e embeddings and Riemannian K-means algorit... arxiv.org/abs/1907.01662

Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases: https://arxiv.org/abs/1906.09587 in MVD workshop (https://s1155026040.github.io/mvd-2019-cvpr-workshop/) at CVPR'19

Artificial Intelligence & Human Rights: Opportunities & Risks
Raso et al.: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3259344
#AIEthics #ArtificialIntelligence #HumanRights

Artificial Intelligence In Healthcare | Examples Of AI In Healthcare

https://www.youtube.com/watch?v=j6EB9HO6acE

DL-workshop-series

Github: https://github.com/Machine-Learning-Tokyo/DL-workshop-series

Link to the presentation: https://drive.google.com/open?id=1sXztx3E9M3G0BIRLh6sxaqVOEOdJVJTrzHOixA5b-rM

Videos: https://www.youtube.com/playlist?list=PLaPdEEY26UXxvlzz485w61W4LgO0lUZfg

How to Perform Face Detection with Deep Learning in Keras

https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

Deep Learning Lecture
https://www.youtube.com/watch?v=FQw2l0AJ2iw

A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.


A mathematical model from 103 years ago predicted something that was seen for the first time today: a black hole.

Machine Learning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.

Neural Style Transfer with Adversarially Robust Classifiers
Blog by Reiichiro Nakano: https://reiinakano.com/2019/06/21/robust-neural-style-transfer.html
Colab: https://colab.research.google.com/github/reiinakano/adversarially-robust-neural-style-transfer/blob/master/Robust_Neural_Style_Transfer.ipynb
#ArtificialIntelligence #DeepLearning #MachineLearning

"Introducing Google Research Football: A Novel Reinforcement Learning Environment"
Blog by Karol Kurach and Olivier Bachem: https://ai.googleblog.com/2019/06/introducing-google-research-football.html
#reinforcementlearning #footfall #artificialintelligence

SLIDES
Introduction to Machine Learning:Linear Learners

Lisbon Machine Learning School, 2018

Stefan Riezler

https://lxmls.it.pt/2018/slidesLXMLS2018.pdf

A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym 🤫

GitHub by Adam King: https://github.com/notadamking/RLTrader

#cryptocurrency #deeplearning #reinforcementlearning #openaigym #trading

With Artificial Intelligence, Udacity can generate lecture videos from just audio narration with LumièreNet.
This means we can automate the production of MOOC video lectures using deep learning by directly mapping between audio and corresponding visuals.

Great work with bidirectional recurrent long-short term memory (BLSTM) network by Byung-Hak Kim and Varun Ganapathi

LumièreNet is a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length. Unlike prior works, LumièreNet is entirely composed of trainable neural network modules to learn mapping functions from the audio to video through (intermediate) estimated pose-based compact and abstract latent codes.

Watch: https://vimeo.com/327196551
Paper: https://arxiv.org/pdf/1907.02253.pdf

Neural Networks: parameters, hyperparameters and optimization strategies
https://towardsdatascience.com/neural-networks-parameters-hyperparameters-and-optimization-strategies-3f0842fac0a5

TRFL (pronounced "truffle"): a library of reinforcement learning building blocks
By the Research Engineering team at DeepMind: https://github.com/deepmind/trfl
#artificialintelligence #deeplearning #reinforcementlearning

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

Rules of Machine Learning: Best Practices for ML Engineering
By Martin Zinkevich: https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf #ArtificialIntelligence #MachineLearning

Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science: MIT

Download Link: https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf

Find other free courses, notes,etc of Stanford, Harvard, Cornell, NYU,etc here at : https://www.marktechpost.com/free-resources/

Subsampling Bias and The Best-Discrepancy Systematic Cross Validation.

arxiv.org/abs/1907.02437