The Data Science Workflow

Article giving a full setup for #DS #workflow. How to train, evaluate, deploy and monitor performance of a model

Medium link: https://medium.com/@kt.era.ee/the-data-science-workflow-43859db0415

#practicalML

​​Live demo of GAN paint brush

Now you can paint with textures on any images, drawing buildings, doors and complex objects by selecting an area where you want to draw an object. The #GAN takes care of merging part into the picture.

Link: https://gandissect.res.ibm.com/ganpaint.html

​​Dimensionality reduction for visualizing single-cell data using UMAP

UMAP is an t-SNE replacement for #visualization.

UMAP is being increasingly accepted as a powerful tool for visualizing single cell datasets. This paper compares UMAP to #TSNE

While UMAP is unquestionably better than default t-SNE in preserving global structure, it's worth mentioning that (very recently) it was shown that this limitation of t-SNE appears to be addressable with better parameters/initialization.

Article link: https://www.nature.com/articles/nbt.4314

​​Papers from #DeepMind panel at #NIPS2018

Work on radiotherapy planning: https://arxiv.org/abs/1809.04430
Triaging eye diseases: https://www.nature.com/articles/s41591-018-0107-6
Probabilistic U-net: https://arxiv.org/abs/1806.05034

#segmentation #CV #Unet

FAIR turns five: What we’ve accomplished and where we’re headed

#Facebook AI Research group report on the work done.

Link: https://code.fb.com/ai-research/fair-fifth-anniversary/

​​Probabilistic Image Segmentation

Re-implementation of the model described in «A Probabilistic U-Net for Segmentation of Ambiguous Images».

ArXiV: https://arxiv.org/abs/1806.05034
Github: https://github.com/SimonKohl/probabilistic_unet

Imitation by watching YouTube

Learning features from YouTube videos through self-supervision allows us to solve hard exploration games in Atari.

Paper: https://papers.nips.cc/paper/7557-playing-hard-exploration-games-by-watching-youtube.pdf
Youtube: https://www.youtube.com/watch?v=s8ZSVfYmtpc&feature=youtu.be

#RL #YouTube

Forensic Deep Learning: Kaggle Camera Model Identification Challenge

Report on Kaggle solution on camera model identification.

Link: https://towardsdatascience.com/forensic-deep-learning-kaggle-camera-model-identification-challenge-f6a3892561bd

#CV #Kaggle

​​Visualizing the Loss Landscape of Neural Nets

Github: https://github.com/tomgoldstein/loss-landscape

#NN #loss #vizualization #DL

Do Better ImageNet Models Transfer Better?

Finding: better ImageNet architectures tend to work better on other datasets too. Surprise: pretraining on ImageNet dataset sometimes doesn't help very much.

ArXiV: https://arxiv.org/abs/1805.08974

#ImageNet #finetuning #transferlearning

How Many Samples are Needed to Learn a Convolutional Neural Network

Article questioning fact that CNNs use a more compact representation than the Fully-connected Neural Network (FNN) and thus require fewer training samples to accurately estimate their parameters.

ArXiV: https://arxiv.org/abs/1805.07883

#CNN #nn

​​Deep learning for chest X-rays

Important work on chest X-Ray analysis.

ArXiV: https://arxiv.org/abs/1711.05225

#DL #medical #bioinformatics

Critics on the last article, suggesting that data was not so good and the advance not that significant as claimed.

Link: https://lukeoakdenrayner.wordpress.com/2018/01/24/chexnet-an-in-depth-review/

​​UberAI introduces a new approach for making Neural Networks process images faster & more accurately with jpeg representations.

Link: https://eng.uber.com/neural-networks-jpeg/
Paper: https://papers.nips.cc/paper/7649-faster-neural-networks-straight-from-jpeg

#nn #CV #Uber

​​Stunning face generation results in “A Style-Based Generator Architecture for Generative Adversarial Networks”

ArXiV: https://arxiv.org/pdf/1812.04948.pdf

#CV #nn #GAN

Yeah, these are not real people.

New book by Andrew Ng

Drawn from his experience leading Google Brain, Baidu's AI Group, and Landing AI, this 5-step Playbook provides a roadmap for your company to transform into a great AI company.

Site: https://landing.ai/ai-transformation-playbook
Direct link: https://d6hi0znd7umn4.cloudfront.net/content/uploads/2018/12/AI-Transformation-Playbook.pdf

#book