Introduction to Reinforcement Learning
By DeepMind. YouTube: https://www.youtube.com/watch?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ&time_continue=5&v=2pWv7GOvuf0
#deeplearning #artificialintelligence #reinforcementlearning
By DeepMind. YouTube: https://www.youtube.com/watch?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ&time_continue=5&v=2pWv7GOvuf0
#deeplearning #artificialintelligence #reinforcementlearning
YouTube
RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning
#Reinforcement Learning Course by David Silver# Lecture 1: Introduction to Reinforcement Learning
#Slides and more info about the course: https://goo.gl/vUiyjq
#Slides and more info about the course: https://goo.gl/vUiyjq
Tutorial on Graph Neural Networks for Computer Vision and Beyond (Part 1)
https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d
https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d
Medium
Tutorial on Graph Neural Networks for Computer Vision and Beyond (Part 1)
I’m answering questions that AI/ML/CV people not familiar with graphs or graph neural networks typically ask. I provide PyTorch examples …
NeurIPS 2019 : Disentanglement Challenge
Blog by Max Planck Institute for Intelligent Systems: https://www.aicrowd.com/challenges/neurips-2019-disentanglement-challenge
#DeepLearning #Disentanglement #NeurIPS #NeurIPS2019
Blog by Max Planck Institute for Intelligent Systems: https://www.aicrowd.com/challenges/neurips-2019-disentanglement-challenge
#DeepLearning #Disentanglement #NeurIPS #NeurIPS2019
AIcrowd | NeurIPS 2019 : Disentanglement Challenge | Challenges
Disentanglement: from simulation to real-world
New Frontiers of Automated Mechanism Design for Pricing and Auctions by Maria-Florina Balcan, @mldcmu, Tuomas Sandholm, Ellen Vitercik @csdatcmu
Learn more → https://mld.ai/y1m
Tutorial Video Part I: https://youtu.be/buK3KXZcGAI
Tutorial Video Part II: https://youtu.be/T8gaK4Yw4zI
#MechanismDesign #GameTheory #Tutorial #MachineLearning #Optimization #ML
Learn more → https://mld.ai/y1m
Tutorial Video Part I: https://youtu.be/buK3KXZcGAI
Tutorial Video Part II: https://youtu.be/T8gaK4Yw4zI
#MechanismDesign #GameTheory #Tutorial #MachineLearning #Optimization #ML
Google
EC19 New Frontiers of Automated Mechanism Design for Pricing and Auctions
We just released a high-performance graph embedding system called GraphVite, which supports a variety of applications including node embeddings, knowledge graph embeddings and graph&high-dimensional data visualization. It is super fast, which only takes around one minute to learn node embebeddings for a graph with one million node. We benchmarked a variety of models including DeepWalk, LINE, node2vec, TransE, DistMult, ComplEx, SimplE, RotatE, LargeVis... More information is available at: https://graphvite.io/
GraphVite
A general and high-performance graph embedding system for various applications Designed for CPU-GPU hybrid architecture
"Automating Inference, Learning, and Design using Probabilistic Programming"
Tom Rainforth, Wolfson College, University of Oxford : https://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2017thesis.pdf
#ProbabilisticProgramming #MonteCarloinference #MarkovChainMonteCarlo
Tom Rainforth, Wolfson College, University of Oxford : https://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2017thesis.pdf
#ProbabilisticProgramming #MonteCarloinference #MarkovChainMonteCarlo
SLIDES
Beyond Domain Randomization
Josh Tobin
https://josh-tobin.com/assets/pdf/BeyondDomainRandomization_Tobin_RSS19.pdf
Paper: Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://arxiv.org/pdf/1703.06907.pdf
Beyond Domain Randomization
Josh Tobin
https://josh-tobin.com/assets/pdf/BeyondDomainRandomization_Tobin_RSS19.pdf
Paper: Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://arxiv.org/pdf/1703.06907.pdf
Learning to Train with Synthetic Humans
https://arxiv.org/pdf/1908.00967.pdf
https://arxiv.org/pdf/1908.00967.pdf
An Electronic Chip That Makes ‘Memories’ Is A Step Towards Creating Bionic Brains
https://liwaiwai.com/2019/07/29/an-electronic-chip-that-makes-memories-is-a-step-towards-creating-bionic-brains/
https://liwaiwai.com/2019/07/29/an-electronic-chip-that-makes-memories-is-a-step-towards-creating-bionic-brains/
Liwaiwai
An Electronic Chip That Makes ‘Memories’ Is A Step Towards Creating Bionic Brains - Liwaiwai
What better way to build smarter computer chips than to mimic nature’s most perfect computer – the human brain? Being able to store, delete and process information is crucial for computing, and the brain does this extremely efficiently. Our new electronic…
The 10 most helpful *free* online machine learning courses, via Chipro.
Full thread: bit.ly/Top10chipro
Full thread: bit.ly/Top10chipro
Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar and Marc G. Bellemare: https://arxiv.org/abs/1812.06110
Code: https://github.com/google/dopamine
#deeplearning #reinforcementlearning #tensorflow
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar and Marc G. Bellemare: https://arxiv.org/abs/1812.06110
Code: https://github.com/google/dopamine
#deeplearning #reinforcementlearning #tensorflow
arXiv.org
Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for...
Unsupervised Separation of Dynamics from Pixels
Silvia Chiappa and Ulrich Paquet : https://arxiv.org/abs/1907.12906
#DeepLearning #MachineLearning #UnsupervisedLearning
Silvia Chiappa and Ulrich Paquet : https://arxiv.org/abs/1907.12906
#DeepLearning #MachineLearning #UnsupervisedLearning
arXiv.org
Unsupervised Separation of Dynamics from Pixels
We present an approach to learn the dynamics of multiple objects from image
sequences in an unsupervised way. We introduce a probabilistic model that first
generate noisy positions for each object...
sequences in an unsupervised way. We introduce a probabilistic model that first
generate noisy positions for each object...
Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar and Marc G. Bellemare: https://arxiv.org/abs/1812.06110
Code: https://github.com/google/dopamine
#deeplearning #reinforcementlearning #tensorflow
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar and Marc G. Bellemare: https://arxiv.org/abs/1812.06110
Code: https://github.com/google/dopamine
#deeplearning #reinforcementlearning #tensorflow
arXiv.org
Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for...
"Long Short-Term-Memory"
Sepp Hochreiter and Jürgen Schmidhuber : https://www.bioinf.jku.at/publications/older/2604.pdf
#ArtificialIntelligence #LongShortTermMemory #LSTM
Sepp Hochreiter and Jürgen Schmidhuber : https://www.bioinf.jku.at/publications/older/2604.pdf
#ArtificialIntelligence #LongShortTermMemory #LSTM
Adversarial Examples Are Not Bugs, They Are Features
Ilyas et al.: https://arxiv.org/abs/1905.02175
#MachineLearning #Cryptography #Security
Ilyas et al.: https://arxiv.org/abs/1905.02175
#MachineLearning #Cryptography #Security
arXiv.org
Adversarial Examples Are Not Bugs, They Are Features
Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be...
TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer
Allevato et al.: https://arxiv.org/abs/1907.11200
#ArtificialIntelligence #MachineLearning #Robotics
Allevato et al.: https://arxiv.org/abs/1907.11200
#ArtificialIntelligence #MachineLearning #Robotics
arXiv.org
TuneNet: One-Shot Residual Tuning for System Identification and...
As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating...
Why Real Neurons Learn Faster
A closer look into differences between natural nervous systems & artificial #NeuralNetworks
https://www.codeproject.com/Articles/1275031/Why-Real-Neurons-Learn-Faster
A closer look into differences between natural nervous systems & artificial #NeuralNetworks
https://www.codeproject.com/Articles/1275031/Why-Real-Neurons-Learn-Faster
Codeproject
Why Real Neurons Learn Faster
A closer look into differences between natural nervous systems and artificial neural networks