This computer is 26 inches tall and houses a 400,000-core processor
https://www.pcgamer.com/this-computer-is-26-inches-tall-and-houses-a-400000-core-processor/
https://www.pcgamer.com/this-computer-is-26-inches-tall-and-houses-a-400000-core-processor/
What can artificial intelligence do for physics? And what will it do to physics?
https://backreaction.blogspot.com/2019/11/what-can-artificial-intelligence-do-for.html
https://backreaction.blogspot.com/2019/11/what-can-artificial-intelligence-do-for.html
Blogspot
What can artificial intelligence do for physics? And what will it do <i>to</i> physics?
Science News, Physics, Science, Philosophy, Philosophy of Science
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Hataya et al.: https://arxiv.org/abs/1911.06987
#ArtificialIntelligence #DeepLearning #MachineLearning
Hataya et al.: https://arxiv.org/abs/1911.06987
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown that...
Bayesian Deep Learning - NeurIPS 2019 Workshop
Friday, December 13, 2019 — Vancouver Convention Center, Vancouver, Canada : https://bayesiandeeplearning.org
#bayesian #deeplearning #neurips2019
Friday, December 13, 2019 — Vancouver Convention Center, Vancouver, Canada : https://bayesiandeeplearning.org
#bayesian #deeplearning #neurips2019
bayesiandeeplearning.org
Bayesian Deep Learning Workshop | NeurIPS 2021
Bayesian Deep Learning Workshop at NeurIPS 2021 — Tuesday, December 14, 2021, Virtual.
How to Connect Model Input Data With Predictions for Machine Learning
https://machinelearningmastery.com/how-to-connect-model-input-data-with-predictions-for-machine-learning/
https://machinelearningmastery.com/how-to-connect-model-input-data-with-predictions-for-machine-learning/
MachineLearningMastery.com
How to Connect Model Input Data With Predictions for Machine Learning - MachineLearningMastery.com
Fitting a model to a training dataset is so easy today with libraries like scikit-learn.
A model can be fit and evaluated on a dataset in just a few lines of code. It is so easy that it has become a problem.
The same few lines of code are repeated again…
A model can be fit and evaluated on a dataset in just a few lines of code. It is so easy that it has become a problem.
The same few lines of code are repeated again…
Tomorrow I will be interviewing Katy Cook on her fantastic new book The Psychology of #SiliconValley: Ethical Threats and Emotional Unintelligence in the #Tech Industry. Get your free open access book here https://snglrty.co/2OGGKH9
Springer
The Psychology of Silicon Valley - Ethical Threats and Emotional Unintelligence in the Tech Industry | Katy Cook | Springer
This open access book explores the conscious and unconscious norms, values and characteristics that drive behaviors within the high-technology industry capital of the world, Silicon Valley, and it presents recommendations for how to practically improve ethics.…
"Latent ODEs for Irregularly-Sampled Time Series"
Paper by Rubanova et al.: https://arxiv.org/abs/1907.03907
GitHub: https://github.com/YuliaRubanova/latent_ode
#MachineLearning #OrdinaryDifferentialEquations #TimeSeries
Paper by Rubanova et al.: https://arxiv.org/abs/1907.03907
GitHub: https://github.com/YuliaRubanova/latent_ode
#MachineLearning #OrdinaryDifferentialEquations #TimeSeries
arXiv.org
Latent ODEs for Irregularly-Sampled Time Series
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous-time hidden...
Language Models as Knowledge Bases?
Petroni et al.: https://arxiv.org/abs/1909.01066
#Transformers #NaturalLanguageProcessing #MachineLearning
Petroni et al.: https://arxiv.org/abs/1909.01066
#Transformers #NaturalLanguageProcessing #MachineLearning
arXiv.org
Language Models as Knowledge Bases?
Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be...
Finally Mr.Yoshua Bengio talks about causal inference.
https://www.youtube.com/watch?v=0GsZ_LN9B24&feature=youtu.be&fbclid=IwAR09eC-Pg_uB6vNCHZcek5jJaRLIX09Yo5SxmKAba0xPi_bAndvcYdIOxvo
https://t.iss.one/ArtificialIntelligenceArticles
https://www.youtube.com/watch?v=0GsZ_LN9B24&feature=youtu.be&fbclid=IwAR09eC-Pg_uB6vNCHZcek5jJaRLIX09Yo5SxmKAba0xPi_bAndvcYdIOxvo
https://t.iss.one/ArtificialIntelligenceArticles
YouTube
WSAI Americas 2019 - Yoshua Bengio - Moving beyond supervised deep learning
Moving beyond supervised deep learning
Watch Yoshua Bengio, Professor of Computer Science and Operations Research at Université de Montréal on stage at World Summit AI Americas 2019. americas.worldsummit.ai
Watch Yoshua Bengio, Professor of Computer Science and Operations Research at Université de Montréal on stage at World Summit AI Americas 2019. americas.worldsummit.ai
Write-A-Video: Computational Video Montage from Themed Text
webpage: https://faculty.idc.ac.il/arik/site/writeVideo.asp
video: https://vimeo.com/357657704
webpage: https://faculty.idc.ac.il/arik/site/writeVideo.asp
video: https://vimeo.com/357657704
Object-Guided Instance Segmentation for Biological Images. https://arxiv.org/abs/1911.09199
arXiv.org
Object-Guided Instance Segmentation for Biological Images
Instance segmentation of biological images is essential for studying object
behaviors and properties. The challenges, such as clustering, occlusion, and
adhesion problems of the objects, make...
behaviors and properties. The challenges, such as clustering, occlusion, and
adhesion problems of the objects, make...
Active Learning for Deep Detection Neural Networks. https://arxiv.org/abs/1911.09168
"Deep Learning with PyTorch" provides a detailed, hands-on introduction to building and training neural networks with PyTorch, a popular open-source machine learning framework.
This book includes:
Introduction to deep learning and the PyTorch library
Pre-trained networks
Tensors
The mechanics of learning
Using a neural network to fit data
Get a free copy for a limited time👇
https://lnkd.in/gGHeyst
This book includes:
Introduction to deep learning and the PyTorch library
Pre-trained networks
Tensors
The mechanics of learning
Using a neural network to fit data
Get a free copy for a limited time👇
https://lnkd.in/gGHeyst
ArtificialIntelligenceArticles
Finally Mr.Yoshua Bengio talks about causal inference. https://www.youtube.com/watch?v=0GsZ_LN9B24&feature=youtu.be&fbclid=IwAR09eC-Pg_uB6vNCHZcek5jJaRLIX09Yo5SxmKAba0xPi_bAndvcYdIOxvo https://t.iss.one/ArtificialIntelligenceArticles
He has been talking about this for a while :
https://www.youtube.com/watch?v=ph04b-poNr4&feature=youtu.be&fbclid=IwAR1g-SrVXbxzitP9DKIGCJO3tLDSngC9AqA8HY6AGAcpZUL3qANbJCzck9s
https://www.youtube.com/watch?v=ph04b-poNr4&feature=youtu.be&fbclid=IwAR1g-SrVXbxzitP9DKIGCJO3tLDSngC9AqA8HY6AGAcpZUL3qANbJCzck9s
YouTube
RIIAA 2.0 Keynote: Yoshua Bengio (MILA, Turing Award 2018)
Yoshua Bengio is considered one of the leaders heading the advancement of deep learning during the last three decades. Yoshua Bengio is Professor at the Department of Computer Science and Operations Research, and Scientific Director of the Montreal Institute…
PyTorch 101
By Ayoosh Kathuria: https://blog.paperspace.com/pytorch-101-understanding-graphs-and-automatic-differentiation/
1. Understanding Graphs, Automatic Differentiation and Autograd
2. Building Your First Neural Network
3. Going Deep with PyTorch
4. Memory Management and Using Multiple GPUs
5. Understanding Hooks for debugging back pass
By Ayoosh Kathuria: https://blog.paperspace.com/pytorch-101-understanding-graphs-and-automatic-differentiation/
1. Understanding Graphs, Automatic Differentiation and Autograd
2. Building Your First Neural Network
3. Going Deep with PyTorch
4. Memory Management and Using Multiple GPUs
5. Understanding Hooks for debugging back pass
Digitalocean
PyTorch 101, Understanding Graphs, Automatic Differentiation and Autograd | DigitalOcean
In this article, we dive into how PyTorch’s Autograd engine performs automatic differentiation.
"Fast Task Inference with Variational Intrinsic Successor Features"
Hansen et al.: https://arxiv.org/abs/1906.05030
#DeepLearning #ReinforcementLearning #UnsupervisedLearning
Hansen et al.: https://arxiv.org/abs/1906.05030
#DeepLearning #ReinforcementLearning #UnsupervisedLearning