Graduate Student Solves Quantum Verification Problem

Article by Erica Klarreich : https://www.quantamagazine.org/graduate-student-solves-quantum-verification-problem-20181008/

Reinforcement Learning for Improving Agent Design
https://designrl.github.io/
https://arxiv.org/pdf/1810.03779.pdf

Interview with Deep Learning Researcher and The GANfather: Dr. Ian Goodfellow

https://hackernoon.com/interview-with-deep-learning-researcher-and-the-ganfather-dr-ian-go-ganfather-dr-ian-goodfellow-cd

Applying to Ph.D. Programs in Computer Science
https://www.cs.cmu.edu/~harchol/gradschooltalk.pdf @ArtificialIntelligenceArticles

"Quantum MachineLearning (Quantum ML)": An Overview

It is a symbiotic association- leveraging the power of QuantumComputing to produce quantum versions of ML algorithms, and applying classical ML algorithms to analyze quantum systems


link : https://goo.gl/7q54AP

https://t.iss.one/ArtificialIntelligenceArticles

Quantum Machine Learning - Prof. Lilienfeld

Prof. O. Anatole von Lilienfeld of the University of Bassel presented his labs work on Quantum Machine Learning at the 2017 Conference on Neural Information Processing Systems on December 8th

https://goo.gl/DULxh2


https://t.iss.one/ArtificialIntelligenceArticles

Quantum Machine Learning
https://arxiv.org/abs/1611.09347 @ArtificialIntelligenceArticles

Deep Learning and Quantum Physics: A Fundamental Bridge, https://arxiv.org/abs/1704.01552 @ArtificialIntelligenceArticles

fast.ai

Making neural nets uncool again

Learn :

- Intro Machine Learning : https://course.fast.ai/ml

- Practical Deep Learning : https://course.fast.ai/

- Cutting Edge Deep Learning : https://course.fast.ai/part2.html

- Computational Linear Algebra : https://github.com/fastai/numerical-linear-algebra

@ArtificialIntelligenceArticles

Industrial AI: BHGE’s Physics-based, Probabilistic Deep Learning Using TensorFlow Probability - Part 1
https://goo.gl/Sbkwru @ArtificialIntelligenceArticles

Here are 660 free online programming and computer science courses you can start in October https://medium.freecodecamp.org/99725c056812

Self-Attention GAN

Tensorflow implementation for reproducing main results in the paper "Self-Attention Generative Adversarial Networks" : https://github.com/brain-research/self-attention-gan

Paper : https://arxiv.org/abs/1805.08318

#artificialintelligence #deeplearning #neuralnetworks

Deep Recurrent Level Set for Segmenting Brain Tumors. https://arxiv.org/abs/1810.04752

Quantum Deep Learning
https://arxiv.org/abs/1412.3489

Next Fall, the Institute for Pure and Applied Mathematics at UCLA (IPAM) will host a semester-long program entitled "Machine Learning for Physics and the Physics of Learning".

Among other events, there will be four one-week-long workshops:
- Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics (September 23 - 27, 2019) co-organized by Alan Aspuru-Guzik.


https://www.ipam.ucla.edu/programs/workshops/workshop-i-from-passive-to-active-generative-and-reinforcement-learning-with-physics/

- Workshop II: Interpretable Learning in Physical Sciences (October 14 - 18, 2019) co-organized by my NYU colleague Kyle Cranmer. https://www.ipam.ucla.edu/programs/workshops/workshop-ii-interpretable-learning-in-physical-sciences/

- Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature (October 28 - November 1, 2019) co-organized by my NYU colleague Joan Bruna Estrach
https://www.ipam.ucla.edu/programs/workshops/workshop-iii-validation-and-guarantees-in-learning-physical-models-from-patterns-to-governing-equations-to-laws-of-nature/

- Workshop IV: Using Physical Insights for Machine Learning (November 18 - 22, 2019) which I co-organize with Riccardo Zecchina, Lenka Zdeborova and Matthias Rupp. https://www.ipam.ucla.edu/programs/workshops/workshop-iv-using-physical-insights-for-machine-learning/

Tons of fascinating topics in perspective with new applications of ML/DL.

https://www.ipam.ucla.edu/programs/long-programs/machine-learning-for-physics-and-the-physics-of-learning/

@ArtificialIntelligenceArticles

Review Article: Deep Learning in Neuroradiology https://www.ajnr.org/content/39/10/1776 @ArtificialIntelligenceArticles

MedicalTorch is an open-source framework for pytorch, implemeting an extensive set of loaders, pre-processors and datasets for medical imaging.
https://medicaltorch.readthedocs.io/en/stable/

The Statistical Physics of Real-World Networks

Cimini et al.: https://arxiv.org/abs/1810.05095

#physics #artificialintelligence #neuralnetworks

The Statistical Physics of Real-World Networks

Cimini et al.: https://arxiv.org/abs/1810.05095

#artificialintelligence #neuralnetworks #physics

Eager Execution Enabled

https://colab.research.google.com/github/zaidalyafeai/Notebooks/blob/master/Eager_Execution_Enabled.ipynb

Flow-based Deep Generative Models

By Lilian Weng : https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models.html

#deeplearning #machinelearning #neuralnetworks