ArtificialIntelligenceArticles pinned «Graph Transformer Networks. https://arxiv.org/abs/1911.06455»
Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement. https://arxiv.org/abs/1911.06443
arXiv.org
Gated Variational AutoEncoders: Incorporating Weak Supervision to...
Variational AutoEncoders (VAEs) provide a means to generate representational
latent embeddings. Previous research has highlighted the benefits of achieving
representations that are disentangled,...
latent embeddings. Previous research has highlighted the benefits of achieving
representations that are disentangled,...
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...