Video from Stills: Lensless Imaging with Rolling Shutter
Antipa et al.: https://arxiv.org/abs/1905.13221v1
#ArtificialIntelligence #DeepLearning #MachineLearning
Antipa et al.: https://arxiv.org/abs/1905.13221v1
#ArtificialIntelligence #DeepLearning #MachineLearning
On Conditioning GANs to Hierarchical Ontologies.) arxiv.org/abs/1905.06586
Important paper from Zellers et al. - "Defending Against Neural Fake News": arxiv.org/abs/1905.12616
Great to see more technical work on this topic, as well as further discussion of appropriate language model publication norms.
Great to see more technical work on this topic, as well as further discussion of appropriate language model publication norms.
arXiv.org
Defending Against Neural Fake News
Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable...
A Brief Introduction to Machine Learning for Engineers
By Osvaldo Simeone: https://arxiv.org/abs/1709.02840
#ArtificialIntelligence #Engineering #MachineLearning #NeuralNetworks
By Osvaldo Simeone: https://arxiv.org/abs/1709.02840
#ArtificialIntelligence #Engineering #MachineLearning #NeuralNetworks
arXiv.org
A Brief Introduction to Machine Learning for Engineers
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and...
The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
Benyamin Ghojogh and Mark Crowley : https://arxiv.org/abs/1905.12787
#ArtificialIntelligence #DeepLearning #MachineLearning
Benyamin Ghojogh and Mark Crowley : https://arxiv.org/abs/1905.12787
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
The Theory Behind Overfitting, Cross Validation, Regularization,...
In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and...
A Guide for Making Black Box Models Explainable
By Christoph Molnar: https://christophm.github.io/interpretable-ml-book/
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks
By Christoph Molnar: https://christophm.github.io/interpretable-ml-book/
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks
christophm.github.io
Interpretable Machine Learning
Notes from Karpathy on common mistakes when training NN
https://karpathy.github.io/2019/04/25/recipe/
https://karpathy.github.io/2019/04/25/recipe/
karpathy.github.io
A Recipe for Training Neural Networks
Musings of a Computer Scientist.
CompILE: Compositional Imitation Learning and Execution
Kipf et al.: https://arxiv.org/abs/1812.01483
Code: https://github.com/tkipf/compile
#ArtificialIntelligence #DeepLearning #MachineLearning
Kipf et al.: https://arxiv.org/abs/1812.01483
Code: https://github.com/tkipf/compile
#ArtificialIntelligence #DeepLearning #MachineLearning
DeepFake:
- not as effective as you might think.
- more easily detectable as you might think.
Which may explain why trolls and propagandists haven't used it much.
https://www.theverge.com/2019/3/5/18251736/deepfake-propaganda-misinformation-troll-video-hoax
- not as effective as you might think.
- more easily detectable as you might think.
Which may explain why trolls and propagandists haven't used it much.
https://www.theverge.com/2019/3/5/18251736/deepfake-propaganda-misinformation-troll-video-hoax
The Verge
Deepfake propaganda is not a real problem
We’ve spent the last year wringing our hands about a crisis that doesn’t exist
The Beijing Academy of Artificial Intelligence publishes AI ethics guidelines.
Yes, the protection of individual privacy is mentioned.
Commentary at MIT Tech Review: https://www.technologyreview.com/s/613610/why-does-china-suddenly-care-about-ai-ethics-and-privacy/
Yes, the protection of individual privacy is mentioned.
Commentary at MIT Tech Review: https://www.technologyreview.com/s/613610/why-does-china-suddenly-care-about-ai-ethics-and-privacy/
MIT Technology Review
Why does Beijing suddenly care about AI ethics?
New guidelines on freedom and privacy protection signal that the Chinese state is open to dialogue about how it uses technology.
Deep Convolutional Networks as Shallow Gaussian Processes #iclr2019
By @AdriGarriga
The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on
MNIST, SoTA for GPs with comparable params size
Github
https://github.com/convnets-as-gps/convnets-as-gps
ArXiv
https://arxiv.org/abs/1808.05587
By @AdriGarriga
The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on
MNIST, SoTA for GPs with comparable params size
Github
https://github.com/convnets-as-gps/convnets-as-gps
ArXiv
https://arxiv.org/abs/1808.05587
GitHub
convnets-as-gps/convnets-as-gps
Code for "Deep Convolutional Networks as shallow Gaussian Processes" - convnets-as-gps/convnets-as-gps
CS 294-112. Deep Reinforcement Learning by Sergey Levine. UC Berkeley. Fall 2018
Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37
Lecture Slides: https://rail.eecs.berkeley.edu/deeprlcourse/
Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37
Lecture Slides: https://rail.eecs.berkeley.edu/deeprlcourse/
On the Fairness of Disentangled Representations
Locatello et al.: https://arxiv.org/abs/1905.13662
#ArtificialIntelligence #DeepLearning #MachineLearning
Locatello et al.: https://arxiv.org/abs/1905.13662
#ArtificialIntelligence #DeepLearning #MachineLearning
Luck Matters: Understanding Training Dynamics of Deep ReLU Networks
Tian et al.: https://arxiv.org/abs/1905.13405
#ArtificialIntelligence #DeepLearning #MachineLearning
Tian et al.: https://arxiv.org/abs/1905.13405
#ArtificialIntelligence #DeepLearning #MachineLearning
Geoffrey Hinton Leads Google Brain Representation Similarity Index Research Aiming to Understand…
https://medium.com/syncedreview/geoffrey-hinton-leads-google-brain-representation-similarity-index-research-aiming-to-understand-b5d14bf77f49
https://medium.com/syncedreview/geoffrey-hinton-leads-google-brain-representation-similarity-index-research-aiming-to-understand-b5d14bf77f49
Medium
Geoffrey Hinton Leads Google Brain Representation Similarity Index Research Aiming to Understand Neural Networks
A Google Brain research team led by Turing Award recipient Geoffrey Hinton recently published a paper that presents an effective method for…
New algorithm may help people store more pictures, share videos faster
https://news.psu.edu/story/576002/2019/05/29/research/new-algorithm-may-help-people-store-more-pictures-share-videos
https://news.psu.edu/story/576002/2019/05/29/research/new-algorithm-may-help-people-store-more-pictures-share-videos
How to create your first Sequential model in Python (w/ Py code) using Colab (link: https://github.com/gcosma/DeepLearningTutorials/blob/master/SimpleSequentialModelColab.ipynb) #DataScience #DeepLearning #MachineLearning #AI
@GoogleColab
@GoogleColab
GitHub
gcosma/DeepLearningTutorials
Deep Learning Tutorials in Colab. Contribute to gcosma/DeepLearningTutorials development by creating an account on GitHub.
Very proud to announce new work from the
@DeepMindAI
cognition team, bridging the gap between #DeepLearning and symbolic #AI, with Marta Garnelo, Kyriacos Nikiforou,
@ToniCreswell
, Christos Kaplanis, and
@dgbarrett
arxiv.org/abs/1905.10307
@DeepMindAI
cognition team, bridging the gap between #DeepLearning and symbolic #AI, with Marta Garnelo, Kyriacos Nikiforou,
@ToniCreswell
, Christos Kaplanis, and
@dgbarrett
arxiv.org/abs/1905.10307
arXiv.org
An Explicitly Relational Neural Network Architecture
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an...
An outstanding Nature Medicine
guide to deep learning in healthcare, including computer vision, natural language processing, reinforcement learning, and generalized methods in genomic medicine and beyond. https://www.nature.com/articles/s41591-018-0316-z
guide to deep learning in healthcare, including computer vision, natural language processing, reinforcement learning, and generalized methods in genomic medicine and beyond. https://www.nature.com/articles/s41591-018-0316-z
He spent 30 years hammering away at an idea most people dismissed as nonsense. Now he's the most important figure in artificial intelligence https://torontolife.com/tech/ai-superstars-google-facebook-apple-studied-guy/