Workshop on Theory of Deep Learning: Where next?

2019-2020

Tuesday, October 15, 2019 - 09:00 to Friday, October 18, 2019 - 06:00

At Institute for Advanced Studies
Princeton University

Speakers:
Anima Anandkumar, Raman Arora, Sanjeev Arora, Mikhail Belkin, Léon Bottou, Joan Bruna, Michael Collins, Simon Du, Gintare Karolina Dziugaite, Surya Ganguli, Rong Ge, Suriya Gunasekar, Stefanie Jegelka, Chi Jin, Sham Kakade, Yann LeCun, Jason Lee, Ke Li, Tengyu Ma, Aleksander Madry, Chris Manning, Behnam Neyshabur, Dan Roy, Nathan Sbrero, Rachel Ward, Bin Yu

#deeplearning

Live Stream:

https://www.ias.edu/livestream

Stabilizing Transformers for Reinforcement Learning
Parisotto et al.: https://arxiv.org/abs/1910.06764
#DeepLearning #Transformers #ReinforcementLearning

#OpenAi've trained an AI system to solve the Rubik's Cube with a human-like robot hand.

This is an unprecedented level of dexterity for a robot, and is hard even for humans to do.

The system trains in an imperfect simulation and quickly adapts to reality: https://openai.com/blog/solving-rubiks-cube/

BoTorch: Programmable Bayesian Optimization in PyTorch
Balandat et al.: https://arxiv.org/abs/1910.06403
Code: https://github.com/pytorch/botorch
#MachineLearning #Bayesian #PyTorch

🎓 Reinforcement Learning Course from OpenAI

Reinforcement Learning becoming significant part of the data scientist toolbox.
OpenAI created and published one of the best courses in #RL. Algorithms implementation written in #Tensorflow.
But if you are more comfortable with #PyTorch, we have found #PyTorch implementation of this algs

OpenAI Course: https://spinningup.openai.com/en/latest/
Tensorflow Code: https://github.com/openai/spinningup
PyTorch Code: https://github.com/kashif/firedup

#MOOC #edu #course #OpenAI

"Quand la machine apprend"
Un livre dont je suis l'auteur qui sort aujourd'hui en librairie.
Edité par Odile Jacob.

Broché: https://livre.fnac.com/a13700561/Yann-Le-Cun-Quand-la-machine-apprend

Broché: https://www.amazon.fr/gp/aw/d/2738149316/ref=ox_sc_act_image_1

En PDF et ePub: https://www.odilejacob.fr/catalogue/sciences/informatique/quand-la-machine-apprend_9782738149312.php

Format Kindle:
https://www.amazon.fr/dp/B07Z5N3LQM/ref=cm_sw_r_tw_awdb_c_x_wIUPDbTFT7Q07

Unsupervised Word Embeddings Capture Latent Knowledge from Materials Science Literature,"
https://go.nature.com/32dCEfi

An algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge.

3.3 million abstracts of published materials science papers were fed into an algorithm called Word2vec.

By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.

https://towardsdatascience.com/using-unsupervised-machine-learning-to-uncover-hidden-scientific-knowledge-6a3689e1c78d

Online evaluation of machine learning models
https://conferences.oreilly.com/artificial-intelligence/ai-eu/public/schedule/detail/78170

U of T, Vector Institute create new deep learning faculty positions in honour of Geoffrey Hinton


https://www.utoronto.ca/news/u-t-vector-institute-create-new-deep-learning-faculty-positions-honour-geoffrey-hinton https://t.iss.one/ArtificialIntelligenceArticles

High energy: Facebook's AI guru LeCun imagines AI's next frontier
https://www.zdnet.com/article/high-energy-facebooks-ai-guru-lecun-imagines-ais-next-frontier/

Emergent properties of the local geometry of neural loss landscapes
Stanislav Fort and Surya Ganguli : https://arxiv.org/abs/1910.05929
#MachineLearning #NeuralNetwork #DeepLearning

Video Architecture Search
Blog by Michael S. Ryoo and AJ Piergiovanni : https://ai.googleblog.com/2019/10/video-architecture-search.html
#ArtificialIntelligence #DeepLearning #NeuralArchitectureSearch

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
Video: https://www.youtube.com/watch?v=-5XxXRABXJs&feature=youtu.be
Code: https://github.com/MIT-SPARK/Kimera
Paper: https://arxiv.org/abs/1910.02490

From Facebook AI:

ReAgent, the most comprehensive and modular open source platform for creating AI-based reasoning systems, and the first to include policy evaluation to incorporate offline feedback to improve models.
https://ai.facebook.com/blog/open-sourcing-reagent-a-platform-for-reasoning-systems/

Quantized Reinforcement Learning (QUARL)
Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi : https://arxiv.org/abs/1910.01055
Code: https://github.com/harvard-edge/quarl
#DeepLearning #ReinforcementLearning #Quantization #Robotics

Minimal implementation of a Neural Architecture Search system
https://github.com/nicklashansen/minimal-nas

Active Learning for Graph Neural Networks via Node Feature Propagation
Wu et al.: https://arxiv.org/abs/1910.07567
#ArtificialIntelligence #GraphNeuralNetworks #MachineLearning

Deep Reinforcement Learning meets Graph Neural Networks: An optical network routing use case
Almasan et al.: https://arxiv.org/abs/1910.07421
#DeepLearning #ReinforcementLearning #GraphNeuralNetworks

Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting.
https://pubs.rsna.org/doi/10.1148/radiol.2018171820