Search engine for computer vision datasets

[https://www.visualdata.io/](https://www.visualdata.io/)

DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Gelada et al.: https://arxiv.org/abs/1906.02736
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

1000x Faster Data Augmentation: New paper and code from Berkeley AI research.

From the announcement:

"Population Based Augmentation (PBA), an algorithm that quickly and efficiently learns a state-of-the-art approach to augmenting data for neural network training. PBA matches the previous best result on CIFAR and SVHN but uses one thousand times less compute, enabling researchers and practitioners to effectively learn new augmentation policies using a single workstation GPU. You can use PBA broadly to improve deep learning performance on image recognition tasks."

Announcement: https://lnkd.in/frFCS8R
Arxiv: https://lnkd.in/f58piHN
Code: https://lnkd.in/fQfWpkR

#neuralnetworks #deeplearning #artificialintelligence #machinelearning

Provably efficient reinforcement learning with rich observations.

https://www.microsoft.com/en-us/research/blog/provably-efficient-reinforcement-learning-with-rich-observations/

This is probably the best #PyTorch Deep Learning course I have encountered.
https://fleuret.org/dlc/

andrew ng : ML+ radiologist outperforms a human radiologist alone at detecting cerebral aneurysms.

Deep Learning–Assisted Diagnosis of Cerebral Aneurysms https://news.stanford.edu/2019/06/07/ai-tool-helps-radiologists-detect-brain-aneurysms/

Visualizing and Measuring the Geometry of BERT
Coenen, Reif, Yuan et al.: https://arxiv.org/pdf/1906.02715.pdf
#ArtificialIntelligence #DeepLearning #BERT #NLP

Language, trees, and geometry in neural networks
code https://pair-code.github.io/interpretability/bert-tree/
paper https://arxiv.org/pdf/1906.02715.pdf

Population-based Augmentation
1000x Faster Data Augmentation
Daniel Ho, Eric Liang, Richard Liaw Jun 7, 2019
https://bair.berkeley.edu/blog/2019/06/07/data_aug/
paper https://arxiv.org/pdf/1905.05393.pdf

Material used for Deep Learning related workshops for Machine Learning Tokyo
Implementation and Cheat Sheet: https://github.com/Machine-Learning-Tokyo/DL-workshop-series
#artificialintelligence #deeplearning #machinelearning

Residual Flows for Invertible Generative Modeling
Chen et al.: https://arxiv.org/abs/1906.02735
#artificialintelligence #deeplearning #generativemodels

Hot Papers from Google Brain, DeepMind and Facebook AI




https://www.google.com/amp/s/syncedreview.com/2019/06/02/hot-papers-from-google-brain-deepmind-and-facebook-ai/amp/

A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research.

https://news.mit.edu/2019/machine-learning-amino-acids-protein-function-0322

Yann LeCun et al. publishing evolutionary algorithm tools. Welcoming the era of deep neuroevolution indeed! (https://eng.uber.com/deep-neuroevolution) Great to see the traditional ML community adopt these tools in the cases when they are useful.

Disentangling Disentanglement in Variational Autoencoders
Mathieu et al.: https://proceedings.mlr.press/v97/mathieu19a.html
#ArtificialIntelligence #DeepLearning #VariationalAutoencoders #VAE

Best of arXiv.org for AI, Machine Learning, and Deep Learning – April 2019 by insidebigdata

https://insidebigdata.com/2019/05/22/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-april-2019/

#IntelAI Research has 6 paper acceptances at #ICML2019! Find full list of papers and more here: https://www.intel.ai/icml-2019/

All 1,294 papers at #CVPR2019
Index: https://openaccess.thecvf.com/content_CVPR_2019/html/
#ArtificialIntelligence #DeepLearning #MachineLearning

Practical Deep Learning with Bayesian Principles
Osawa et al.: https://arxiv.org/pdf/1906.02506.pdf
#Bayesian #DeepLearning #PyTorch #VariationalInference

Butterfly Transform: An Efficient FFT Based Neural Architecture Design
Alizadeh et al.: https://arxiv.org/abs/1906.02256
#ArtificialIntelligence #DeepLearning #MachineLearning