Next-level learning approach: using MRI to peek into baby brains to improve CV

MRI Scanning 17 babies for 26 hours to see how face-recognizing brain regions mature. When just 4-6 months, babies prefer to look at faces & socially relevant things. This means face recognition is learned via evolution: data-hungry & sample-inefficient.

Link: https://www.nature.com/articles/ncomms13995

#nature

​​One-shot object detection

Long and complete post explaining how these one-shot detectors work and how they are trained and evaluated.

Link: https://machinethink.net/blog/object-detection/

#cv #dl #objectdetection

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

A deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). #nn achieves an #AUC of 0.895 in predicting whether there is a cancer in the breast, when tested on the screening population.

Link: https://arxiv.org/abs/1903.08297

#cv #dl #cancer #objectdetection

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ML helping to fight world hunger

Plantix is an app helping farmers in India by detecting plant diseases from images. With its help people are able to see where diseases are spreading and warn the users in certain areas.

Link: https://www.thebetterindia.com/175044/india-farmer-earning-lakhs-plantix-app-crop-health/

#mlatwork #cv

Coconet: the ML model behind 20th of March Bach Doodle

Network trained to recreate Bach's music.

Link: https://magenta.tensorflow.org/coconet

#magenta #google #audiolearning

Big article on how #uber ML system Michelangelo works

Michelangelo enables internal teams to seamlessly build, deploy, and operate machine learning solutions at Uber’s scale. It is designed to cover the end-to-end ML workflow: manage data, train, evaluate, and deploy models, make predictions, and monitor predictions. The system also supports traditional ML models, time series forecasting, and deep learning.


Link: https://eng.uber.com/michelangelo/

#ML #MLSystem #MLatwork #practical

🔥Quasi-Breaking: An Algorithm Inks a Record Deal With Warner Music

Endel uses machine learning to create personalized tracks meant to help people focus, relax and sleep better by inputting factors such as heart rate, time of day, location and weather.
Looking forward to actual music-generating algorithm being signed up for label.

Link: https://hypebeast.com/2019/3/endel-algorithm-record-deal-warner-music

#MLHype #audiolearning #DL #Endel

​​Reducing the Need for Labeled Data in Generative Adversarial Networks

How combination of self-supervision and semi-supervision can help learn from partially labeled data.

Link: https://ai.googleblog.com/2019/03/reducing-need-for-labeled-data-in.html

#GAN #DL #Google #supervisedvsunsupervised

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💫Prefect (Airflow alternative) has gone Open Source

Prefect is capable of:
* Handling data processing timeline
* Orchestrating the backend of Cloud execution platform
* Parameterizing machine learning models
* Execute other ETL patterns

Docs: https://docs.prefect.io
Link: https://medium.com/the-prefect-blog/prefect-is-open-source-744e3c00cf35
GitHub: https://github.com/prefecthq/prefect

#ml_pipeline #mlflow

​​A comprehensive beginner’s guide to create a Time Series Forecast (with Codes in Python)

A middle-level article on #TS forecasting in #Python.

Link: https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/

​​An End-to-End Project on Time Series Analysis and Forecasting with Python

Today’s second article about #TS forecasting, to cover basics and provide knowledge about how to approach TS data mining.

Link: https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b

#Python

Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber

More complex article on #TS forecasting from #Uber team.

Link: https://eng.uber.com/neural-networks-uncertainty-estimation/

#RNN #LSTM #Uber

💉Using AI to help increase blood donations

This is a post describing how #Facebook uses #ML and #AI to detect intent or interest in blood donation and to match these people with local hospitals, increasing probability of people actually donating blood. Sounds like an awesome and inspiring application for AI.

Link: https://tech.fb.com/using-ai-to-help-increase-blood-donations/

IPython notebooks and git

#IPython or #Jupyter is one of the most popular tools in Data Science. It usage may questionable, but it is optimal for beginners and people who are making thier first steps. This article covers rare theme — keeping notebooks in git repository and optimizing collaboration using them. Main problem lies in technical information (like cell execution count), which is redundant and can be omitted, but still gets written to git in the default scenario.

Link: https://pascalbugnion.net/blog/ipython-notebooks-and-git.html
Code: https://gist.github.com/pbugnion/ea2797393033b54674af

#datascience #practicalML

​​PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain

Paper on how image can be reconstracted from doodle and color palette.

Link: https://arxiv.org/pdf/1903.10146.pdf

#ImageReconstruction #CV #DL

​​Fast video object segmentation with Spatio-Temporal GANs

Spatio-Temporal GANs to the Video Object Segmentation task, allowing to run at 32 FPS without fine-tuning.

#FaSTGAN #GAN #Segmentation #videomining #CV #DL

​​Step Change Improvement in Molecular Property Prediction with PotentialNet

Paper on a significant improvement in ability to predict molecular properties in drug design. #ML algorithms are getting better and better than classical methods.

Link: https://medium.com/@pandelab/step-change-improvement-in-molecular-property-prediction-with-potentialnet-f431ffa32a2c

#drugsdesign #biolearning #healthcare

​​Open-sourcing PyTorch-BigGraph for faster embeddings of extremely large graphs

PyTorch-BigGraph— a tool that for faster and easier producing graph embeddings for extremely large graphs. Outputs high-quality embeddings without specialized computing resources like GPUs or huge amounts of memory.

Link: https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/
Github: https://github.com/facebookresearch/PyTorch-BigGraph

#PyTorch #Facebook #OpenSourceRelease #Embeddings #GraphLearning