poster on "Leveraging machine intelligence on diagnosing UTI" at workshop in #NeurIPS2018.

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Thoughtful and succinct explanation about why NIPS was a problematic name for a machine learning conference -- from Jeff Dean ect. at the #neurips2018 workshop for critiquing and correcting trends in ML.

https://www.dropbox.com/s/sv9qcfnv42zbmib/CRACT_2018_paper_35.pdf?dl=0

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ICYMI — our poster on Latent Embedding Optimization (LEO) at the #NeurIPS2018 meta-Learning workshop earlier today. We combine a latent generative model of parameters with MAML in the latent space. Our paper demonstrates SOTA results on few-shot classification benchmarks.

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Have you heard of #Tube #CNN ?

Object or human detection in video is crucial for many applications.

It can also have useful applications such as in repetitive #manufacturing tasks to monitor and proactively prevent catastrophes.

Compared to images, video provides additional cues which can help to disambiguate the detection problem.

Here the authors attempt to learn discriminative models for the temporal evolution of object appearance and to use such models for object detection.

They introduced space-time tubes corresponding to temporal sequences of bounding boxes. They propose a TPN network where two CNN architectures for generating and classifying tubes, respectively, this helps maximize object recall.

The Tube-CNN then implements a tube-level object detector in the video. Our method improves state of the art on two large-scale datasets for object detection in video: HollywoodHeads and ImageNet VID. Tube models show particular advantages in difficult dynamic scenes.

Link to paper: https://lnkd.in/d3DW5Qe
Pytorch implementation of Tube-CNN : https://lnkd.in/dzxGdtt

#deeplearning #CNN #machinelearning #videoanalytics

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Slides from Pieter Abbeel's talk at #NeurIPS2018 workshop on RL under Partial Observability:

https://lnkd.in/eFHdb9d

#NeurIPS #ReinforcementLearning

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Basic protocols in quantum reinforcement learning with superconducting circuits

https://www.nature.com/articles/s41598-017-01711-

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Progress-in-Quantum-Reinforcement-Learning

http://qtml2017.di.univr.it/resources/Slides/Progress-in-Quantum-Reinforcement-Learning.pdf


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R in Pharmacy? Honestly, I started losing any hope to see the pharmacy turning (even slowly!) towards R. A little has been happening since 1976, when the S (father of R), was born. And even when S, then R, gained the status of an industry standard in widely understood bio-sciences, it has never happened in pharmacy, namely in clinical research. This was -and still is- the kingdom exclusively reigned by SAS The King (with a low % of "supporters", including R).

It was a big shame, but, what could have been done against long years of spread myths, doubt, uncertainty and negative attitude?

Well, this is not that everything was right about R! Serious topics still have to be addressed, including:
1) numerical validation (ideally free, coordinated by, say, R Consortium),
2) support for CDISC-related processes,
3) metadata layer (SAS format/informat),
There are more topics, yet there's no place for details.

And then, about 5 years ago, something started changing. Slowly. More and more top-pharma companies (even FDA!) started talking about their use of R publicly, some even contributed (e.g. Merck's gsDesign tool).

Today I'd like to share with you the news: a new initiative by R Consortium - the "R in Pharma" project. http://rinpharma.com/

#R #statistics

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"The Matrix Calculus You Need For Deep Learning"

By Terence Parr and Jeremy Howard : https://lnkd.in/dC5MqZM

#100DaysOfMLCode #ArtificialIntelligence #BigData #DeepLearning #MachineLearning

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Deeper peer-reviewed dive into how AlphaZero learns to play Chess, Shoji, & Go, beating previous world-class AIs in 4, 2, & 30 hours respectively. Generalizes to potentially any perfect information game. Science

Paper: http://science.sciencemag.org/content/362/6419/1140 …
Blog:

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On intelligence: its creation and understanding

The intertwined quest for understanding biological intelligence and creating artificial intelligence.

By Surya Ganguli, Stanford Human Centered AI Initiative:

https://lnkd.in/ezciPda

#neuroscience #ai #physics #mathematics

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Deep Learning with Electronic Health Record (EHR) Systems

http://bit.ly/2yeNVy5

#AI #DeepLearning #MachineLearning #DataScience

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[ Paper Summary ] Matrix Factorization Techniques for Recommender Systems

#MachineLearning #RecommenderSystems

link

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Michelangelo PyML: Introducing Uber's Platform for Rapid Python ML Model Development

https://ubr.to/2AolQH6

#AI #DeepLearning #MachineLearning #DataScience


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Applied Federated Learning: Improving Google Keyboard Query Suggestions

By Yang, Andrew, and Eichner et al.: https://lnkd.in/gP9uJ7Y

#machinelearning #artificialintelligence #bigdata #deeplearning

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Reading Abstracts from NIPS/NeurIPS 2018! Here is What I Learned

🌎 Link Review

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Completing someone else’s thought is not an easy trick for #AI. But new systems are starting to crack the code of natural language. Read more via

Link

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Key Papers in Deep RL by OpenAI List of papers in deep RL that should provide a useful starting point for someone looking to do research in the field.

🌎 Link Review

#machinelearning #ArtificialIntelligence #ai

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A Beginner's Guide to the Mathematics of Neural Networks

By A.C.C. Coolen : https://lnkd.in/dsxSCBj

#ArtificialIntelligence #NeuralNetworks

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Google AI has released TF-Ranking, a scalable TensorFlow-based library for learning-to-rank. It provides a unified framework to train, evaluate and serve a ranking model that includes a suite of state-of-the-art learning-to-rank algorithms, commonly used ranking metrics, easy visualization and also multi-item scoring for interference. Check out the article, paper and also the repo to walk through the tutorial examples. Myself can't wait to get started with this, in particular for my next search engine problem.
#deeplearning #machinelearning

Article: https://lnkd.in/e59qQdy
Paper: https://lnkd.in/ePwPVst
Github: https://lnkd.in/eZYE-UQ

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PhD Program in "Information Systems for Data Science" at UMASS Boston

We are now accepting application for Fall 2019. Please share the application information with interested candidates. The contacts details are below.

More information can also be found on our website:
https://lnkd.in/eW9Aud6

Interested applicants can contact:
Ehsan Elahi
Associate Professor
Director of the PhD Program (IS for Data Science)
College of Management
University of Massachusetts, Boston
Email: ehsan.elahi@umb.edu
Phone: 617-287-7881

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