Advancing Self-Supervised and Semi-Supervised Learning with SimCLR

https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html

There are quotes from Yoshua Bengio, Samy Bengio, Rich Richard S. Sutton, Pieter Abbeel, Sergey Levine, David Cox, and me.
Some of my quotes: << “My money is on self-supervised learning,” he said, referring to computer systems that ingest huge amounts of unlabeled data and make sense of it all without supervision or reward. He is working on models that learn by observation, accumulating enough background knowledge that some sort of common sense can emerge. @ArtificialIntelligenceArticles
“Imagine that you give the machine a piece of input, a video clip, for example, and ask it to predict what happens next,” Dr. LeCun said in his office at New York University, decorated with stills from the movie “2001: A Space Odyssey.” “For the machine to train itself to do this, it has to develop some representation of the data. It has to understand that there are objects that are animate and others that are inanimate. The inanimate objects have predictable trajectories, the other ones don’t.”
After a self-supervised computer system “watches” millions of YouTube videos, he said, it will distill some representation of the world from them. Then, when the system is asked to perform a particular task, it can draw on that representation — in other words, it can teach itself.

https://www.nytimes.com/2020/04/08/technology/ai-computers-learning-supervised-unsupervised.html

https://t.iss.one/ArtificialIntelligenceArticles

Neural Voice Puppetry: Audio-driven Facial Reenactment
Thies et al.: https://arxiv.org/abs/1912.05566
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Friston said he always assumed his ideas about how neurons organize would be used to build more efficient neuromorphic computer chips—hardware that tries to mimic how the brain processes information much more closely than today’s standard computer chips do. The idea of trying to integrate biological neurons with semiconductors is not, Friston said, an idea he’d anticipated.
“But to my surprise and delight they have gone straight for the real thing,” he said of Cortical Labs’ use of real biological neurons. “What this group has been able to do is, to my mind, the right way forward to making these ideas work in practice.”

https://fortune.com/2020/03/30/startup-human-neurons-computer-chips/

A mountable toilet system for personalized health monitoring via the analysis of excreta
@ArtificialIntelligenceArticles
https://www.nature.com/articles/s41551-020-0534-9
https://t.iss.one/ArtificialIntelligenceArticles

ML Code Completeness Checklist

https://medium.com/paperswithcode/ml-code-completeness-checklist-e9127b168501

Tips for Publishing Research Code: https://github.com/paperswithcode/releasing-research-code

Facebook blog: https://ai.facebook.com/blog/new-code-completeness-checklist-and-reproducibility-updates/

RLlib: Scalable Reinforcement Learning
RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.
RLlib natively supports TensorFlow, TensorFlow Eager, and PyTorch, but most of its internals are framework agnostic.
The Ray Team : https://ray.readthedocs.io/en/latest/rllib.html
#ReinforcementLearning #PyTorch #TensorFlow

An Overview of Early Vision in InceptionV1 https://distill.pub/2020/circuits/early-vision/

Our team at Google set a new world record in (quantum processor based) resolving the energy spectrum of a chemical compound.
https://arxiv.org/abs/2004.04174
https://t.iss.one/ArtificialIntelligenceArticles

10 Must-read Machine Learning Articles

https://towardsdatascience.com/10-must-read-machine-learning-articles-march-2020-80da9c380981

Google Open-Sources FUSS: The Free Universal Sound Separation Dataset
Github: https://github.com/google-research/sound-separation
Reference Paper: https://arxiv.org/pdf/1810.04826.pdf

LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelih... https://arxiv.org/abs/2004.02980

It’s Time to Improve the Scientific Paper Review Process – But How?
https://syncedreview.com/2020/04/10/its-time-to-improve-the-scientific-paper-review-process-but-how/

Kaggle is hiring a data scientist (contract role).

The work will involve the following:

Data analytics:

Applying various data science methods to understanding the most important aspects of our products, users, and business.
Define objectives and success measures for various product areas.
Organizing findings into powerful stories/narratives that have the broadest possible application.
Partnering with key product owners/decision makers to drive data-driven decision making.
Author and automate reports for business partners.
Work with discrepancy and ambiguity between different data sources and product areas.
Data engineering:

Designing, automating, and optimizing data workflows (mainly using Python in Airflow).
Ability to work with complex/ambiguous datasets
Machine learning:

Working knowledge of machine learning/statistical modeling.
Requirements for the position include:

You must be able to work from one of the following locations: Bay Area (SF or Sunnyvale), Los Angeles, Irvine, Seattle, NYC, Boston, Austin, or Boulder.
BA/BS or 4 years of equivalent experience
Great interpersonal communication skills
Ability to work with data using Python or R
Comfortable with SQL
Comfortable with Kaggle Notebooks and various visualization tools
Interested in learning about Kubernetes and scalable data engineering
Ability to work independently with minimal supervision
Employment is through one of Google's staffing partners and this is a contract role.

If interested, please send your resume and a brief introduction to your background and interests to [email protected]

Postdoc opening at KAUST Vision-CAIR Group starting Fall 2020-Spring 2021


Vision-CAIR group at KAUST (https://cemse.kaust.edu.sa/vision-cair) is inviting top postdoctoral researchers with experience with computer vision/ AI/ machine learning starting Fall 2020/Spring 2021. Posdoc researchers at the Vision-CAIR group are expected to develop principled understanding and computational approaches in the following research themes: A) learning efficiency /computational creativity (zero/few-shot/long-tail learning of visual tasks. This also includes efficient generative models that are capable of generating and understanding unseen art/fashion/3d design ) B) continual learning (e.g. alleviating catastrophic forgetting in various learning settings including recognition RL), C) vision and language (this overlaps with the former themes as we integrate 2D/3D vision with language in most of the work different these themes). This is a video, about imagination inspired AI where I describe what is Imagination Inspired AI which relates to A and C themes, part of the research we do at Vision-CAIR group among other topics https://www.youtube.com/watch?v=Z1dytXfbFkM. This is also a presentation that covers continual learning (theme: B) methods that we have worked on https://www.dropbox.com/s/sdtbmptu7a2chks/deepcontinuallearning.pdf.



Scope

You will be part of the Visual Computing Center at KAUST, according to csranking.org among the top 10 world-wide in Computer Vision & Graphics. @ArtificialIntelligenceArticles
Excellent total compensation, including accommodation, family health benefits, moving expenses, and a substantial tax-free annual salary. Tuition fees, travel to conferences, and research equipment will be covered.
Mentoring and collaborating with KAUST grad students and visiting students/researchers
You will have access to world-class compute equipment of close to a thousand GPU.
Housing Expenses is completely or at least partially covered till a limit.
KAUST community is very diverse with more than 100 nationalities from all over the globe and school is located in the beautiful city of Thuwal on the shores of the red sea.
@ArtificialIntelligenceArticles

Requirements

a PhD degree with excellent academic credentials
relevant research skills and experience in computer vision, machine/deep learning, or optimization, demonstrated by high quality publications
good verbal communication (for daily interaction)
good written communication (for daily interaction)
clarity of thought / systematic thinking
attention to detail
good professional writing skills (e.g., able to write a reasonable abstract or a paper draft)
reliability / accountability
good coding skills


If you are interested, please feel free to send me an email with your CV with your full list of publications ([email protected] , [email protected])



Best,

Mohamed Elhoseiny

Assistant Professor of Computer Science, KAUST

Visiting Faculty, Stanford University, Computer Science Department.

https://www.mohamed-elhoseiny.com/ (personal website)

https://cemse.kaust.edu.sa/vision-cair (group website)

ML and NLP Postdocs at Northwestern University Medical School (working with >8 million patient data)
We are recruiting multiple postdoctoral fellows at Northwestern University’s Feinberg School of Medicine. The postdoctoral fellow is expected to conduct research under guidance from Dr. Yuan Luo, Associate Professor and Chief AI Scientist, Northwestern University Clinical and Translational Sciences Institute. Our group website: https://labs.feinberg.northwestern.edu/lyg/. The fellow will also have the opportunities to work closely with top-notch clinicians from Northwestern Memorial Hospital, and clinical, genetic and imaging data of >8 million patients from Northwestern Medicine Enterprise Data Warehouse.

The successful candidate will have PhD in EECS, Biomedical Informatics, IEMS, Physics or related fields with solid programming skills. Experiences in one or more of the following areas are desirable: Machine Learning (ML) and/or Natural Language Processing (NLP) and/or time series analysis and/or -omic analysis. The candidate should demonstrate good communication skills and ability to work in a collaborative environment, to coordinate and supervise part of the research project.

We offer a competitive salary and an initial appointment of 12 months, starting 2019/2020. Extension of the postdoctoral position for up to 3 years is possible. Northwestern University is an exceptional research institution that has a world-class medical school and is an emerging hub in biomedical AI; our department is located in downtown Chicago, one of the most vibrant cities in the US. Be part of a prestigious institution that offers great benefits, and enjoy our lakefront working environment.

Please send your application to Yuan Luo <[email protected]>, which should include:
- Curriculum vitae
- List of publications (attach a copy of one of your strongest papers)
- Contact details for 2 to 3 references

Northwestern University is an Equal Opportunity/Affirmative Action Employer.