Landmark Detection in Low Resolution Faces with Semi-Supervised Learning. arxiv.org/abs/1907.13255

Wasserstein Robust Reinforcement Learning. arxiv.org/abs/1907.13196

AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

"We should keep in mind the grandeur of the task we are discussing, which is nothing short than the creation of an artificial intelligence smarter than humans. If we succeed, we arguably have also created life itself..."

By Jeff Clune : https://arxiv.org/abs/1905.10985

#ArtificialIntelligence #ArtificialGeneralIntelligence #MetaLearning

DeepMind: Using AI to give doctors a 48-hour head start on life-threatening illness

Blog: https://deepmind.com/blog/predicting-patient-deterioration/
Paper: https://www.nature.com/articles/s41586-019-1390-1

Three great lecture series on the brain


I know three great lecture series on neuroscience. Free for all available on YouTube. These series have let me study complex brain science without a background in STEM.

One on biological psychology and the brain

https://www.youtube.com/playlist?list=PLtXCbh6IFA7QCsei-t8WesusKi8I2LXUJ

And another on brain anatomy using real brain dissections

https://www.youtube.com/playlist?list=PLp9HSlEm97VXyQ32Uwjfz3dpmQ8nl63zJ

The last one focuses on cellular mechanisms of brain function.

https://www.youtube.com/channel/UCmB_Ytx1-QmOE52Ch5QjFIA

It's an amazing time that we live in time period where all these resources are free for all to use. If anybody knows anymore please post in comment section.

Facebook, Carnegie Mellon build first AI that beats pros in 6-player poker
Written by Noam Brown : https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-in-6-player-poker
#ArtificialIntelligence #MachineLearning #Poker

The call for paper for the 2nd edition of the Latinx in AI workshop at
NeurIPS 2019 Conference is out!

Deadline is September 13th, 2019.

Submission instructions here https://www.latinxinai.org/neurips-2019

DROGON: A Causal Reasoning Framework for Future Trajectory Forecast

Chiho Choi, Abhishek Patil, Srikanth Malla : https://arxiv.org/abs/1908.00024

#ArtificialIntelligence #CausalReasoning #Robotics

Real-Time Voice Cloning

GitHub, by Corentin Jemine : https://github.com/CorentinJ/Real-Time-Voice-Cloning

#deeplearning #pytorch #tensorflow #naturallanguageprocessing

PostDoc for Machine Learning in Clinical Neurology



We are looking for a highly motivated and skilled postdoc to apply and develop machine learning methods in clinical neurology. In a collaboration with the Hertie Institute for Clinical Brain Research, the candidate will have several opportunities to work on the prediction of factors and progression of neurodegenerative diseases and epilepsy, using rich longitudinal clinical and molecular data.

The ideal candidate has a PhD in machine learning, physics, math, electrical engineering, or related fields, and a strong background in mathematics, machine learning, statistics, and ideally causal learning, with prior experience in working on life science/clinical data.

We offer a thriving and interactive environment in one of Europe’s leading location for machine learning, and the opportunity to directly work with clinicians and data from the large neurology section of Tübingen’s university hospital.

We particularly encourage women and other underrepresented groups in STEM fields to apply. Please check the How to apply section below.

PhD student in Machine Learning on Inductive Bias Transfer
We are looking for a PhD student to work on quantifying and transferring inductive biases between networks, or neuroscientific data and artificial neuronal networks.

The ideal candidate should have a masters degree in computer science, physics, math, electrical engineering, or related fields, and with a strong background in mathematics and programming. Previous experience in machine learning not necessary but advantageous.

Tübingen is one of the leading locations for machine learning in Europe, offering a scientific inspiring and open enviroment to develop the intelligent systems of tomorrow. The International Max Planck Graduate School for Intelligent Systems is Germany’s largest graduate program for machine learning and related topics.

We particularly encourage women and other underrepresented groups in STEM fields to apply. Please check the How to apply section below.

How to apply
We are always excited to receive applications from people to join our team. If you decide to reach out, please address the following points in your initial email (note that these lists are inclusive; PostDoc applicants have to submit all points below)

General
What are you applying for (lab rotation, research assistant, PhD position, postdoc position, etc.)?
What is your envisioned time frame (start date, end date)?
Do you bring own (partial) funding or will you need to be funded from here?
PhD position and beyond
Curriculum Vitae (clearly stating your education and scientific background)
Statement of motivation why you would like to join the lab (pdf, max 1/2 page)
Links to code you have written (e.g. github or bitbucket)
If applicable, at most two publications or pre-print you (co)-authored
PostDoc
A concise (pdf, max 1/2 page) “research proposal” on a project you would be interested working on. This is not binding but helps to get the conversation started. Applicants answering calls for a specfic project can ignore this.

https://sinzlab.org/openpositions.html

I want to pursue machine learning as a career but not sure if I am qualified. How can I test myself?


answer Andrew Ng :

You are qualified for a career in machine learning! Whatever your current level of knowledge, so long as you keep working hard and keep learning, you can become expert in machine learning and have a great career there.

To anyone interested in the field, start with learning to program. When you’ve mastered the basics of programming, consider the Machine Learning course (Machine Learning | Coursera), then the Deep Learning specialization (deeplearning.ai).

To go even further, read research papers (follow ML leaders on twitter to see what papers they’re excited about). Even better, try to replicate the results in the research papers. Trying to replicating others’ results is a one of the most effective but under-appreciated ways to get good at AI. Also consider activities like online ML competitions, academic conferences, and keep reading books/blogs/papers.

It’s really not a matter of whether you’re qualified do work in machine learning—I’m sure you are! It’s just a matter of getting the learnings to make yourself more and more qualified.

new paper from Andrew Ng

👇👇👇👇

Postdoc Artificial intelligence for ultrasound imaging at Eindhoven University of Technology, Netherlands

Offer Requirements
SPECIFIC REQUIREMENTS
We are looking for candidates who have:
A PhD degree in a relevant area.
Proven experience both in deep learning and in medical imaging.
The ability to contribute to cross-disciplinary collaborations.
Excellent written and oral communication skills in English.
A result-driven and proactive attitude.
The position is open and can be filled at any time, but no later than 1 January 2020.

https://www.marktechpost.com/job/postdoc-artificial-intelligence-for-ultrasound-imaging-at-eindhoven-university-of-technology-netherlands/

OCT Fingerprints: Resilience to Presentation Attacks. arxiv.org/abs/1908.00102

Simultaneous Iris and Periocular Region Detection Using Coarse Annotations. arxiv.org/abs/1908.00069

Deep Non-Rigid Structure from Motion. arxiv.org/abs/1908.00052

Image Captioning with Unseen Objects. arxiv.org/abs/1908.00047

NeurIPS | 2019

Thirty-third Conference on Neural Information Processing Systems - Accepted Competitions :https://neurips.cc/Conferences/2019/CallForCompetitions

#DeepLearning #NeurIPS #NeurIPS2019