Learning Pixel Representations for Generic Segmentation. https://arxiv.org/abs/1909.11735

Revisit Knowledge Distillation: a Teacher-free Framework. https://arxiv.org/abs/1909.11723

Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for... https://arxiv.org/abs/1909.11212

Scale-Equivariant Neural Networks with Decomposed Convolutional Filters. https://arxiv.org/abs/1909.11193

Neural reparameterization improves structural optimization
Hoyer et al.: https://arxiv.org/abs/1909.04240
#MachineLearning #NeuralNetworks #Optimization

Infinite-resolution image generation in the browser
Made by Erlend Gjesteland Ekern using CPPNs, GANs and TensorFlow.js : https://thispicturedoesnotexist.com
#CPPN #GAN #TensorflowJS

Representation Wars: Enacting an Armistice through Active Inference
https://www.researchgate.net/publication/333969028_Representation_Wars_Enacting_an_Armistice_through_Active_Inference

Artificial Design: Modeling Artificial Super Intelligence with Extended General Relativity and Universal Darwinism via Geometrization for Universal Design Automation
https://openreview.net/forum?id=SyxQ_TEFwS

GAMIN: An Adversarial Approach to Black-Box Model Inversion. https://arxiv.org/abs/1909.11835

#weekend_read
Check out this mind-blowing survey on HRL with some really-strong proven hypothesis. Maybe the best till date!
Paper-Title: WHY DOES HIERARCHY (SOMETIMES) WORK SO WELL IN REINFORCEMENT LEARNING?
Link to the paper: https://arxiv.org/pdf/1909.10618.pdf
#GoogleAI #UCB
Four Hypothesis: The four hypotheses may also be categorized as hierarchical training (H1 and H3) and hierarchical exploration (H2 and H4).
(H1) Temporally extended training. High-level actions correspond to multiple environment steps. To the high-level agent, episodes are effectively shorter. Thus, rewards are propagated faster and learning should improve.
(H2) Temporally extended exploration. Since high-level actions correspond to multiple environment steps, exploration in the high-level is mapped to environment exploration which is temporally correlated across steps. This way, an HRL agent explores the environment more efficiently. As a motivating example, the distribution associated with a random (Gaussian) walk is wider when the random noise is temporally correlated.
(H3) Semantic training. High-level actor and critic networks are trained with respect to semantically meaningful actions. These semantic actions are more correlated with future values, and thus easier to learn, compared to training with respect to the atomic actions of the environment. For example, in a robot navigation task it is easier to learn future values with respect to deltas in x-y coordinates rather than robot joint torques.
(H4) Semantic exploration. Exploration strategies (in the simplest case, random action noise) are applied to semantically meaningful actions and are thus more meaningful than the same strategies would be if applied to the atomic actions of the environment. For example, in a robot navigation task, it intuitively makes more sense to explore at the level of x-y coordinates rather than robot joint torques.
TL;DR: A large number of conclusions can be drawn based on empirical analysis. Here are few:-
In terms of the benefits of training, it is clear that training with respect to semantically meaningful abstract actions (H3) has a negligible effect on the success of HRL.
Moreover, temporally extended training (H1) is only important insofar as it enables the use of multi-step rewards, as opposed to training with respect to temporally extended actions.
The main and arguably most surprising, the benefit of the hierarchy is due to exploration. This is evidenced by the fact that temporally extended goal-reaching and agent-switching can enable non-hierarchical agents to solve tasks that otherwise can only be solved.
These results suggest that the empirical effectiveness of hierarchical agents simply reflects the improved exploration that these agents can attain.

Yoshua Bengio brainstorm with students. Very interesting discussion

[https://www.youtube.com/watch?v=g9V-MHxSCcs](https://www.youtube.com/watch?v=g9V-MHxSCcs)

‘Backronym’ May Help To Generate Ideas in Machine Learning by Visualizing Hundreds of Research Papers Together

Website: https://backronym.xyz/graph.html
Paper: https://arxiv.org/pdf/1908.01874v2.pdf
https://www.marktechpost.com/2019/09/29/backronym-may-help-to-generate-ideas-in-machine-learning-by-visualizing-hundreds-of-research-papers-together/

Our preliminary results for keyphrase generation using GANs. This is the first time GANs are used for generating keyphrases. [https://arxiv.org/pdf/1909.12229.pdf](https://t.co/BON7QWOsra?amp=1)

They work pretty well better than some of the popular models for generating keyphrases.

PostDoc position at Yale University

The Yale Center for Medical Informatics seeks a highly motivated Postdoctoral Associate in data science. Individuals with expertise in machine learning, deep learning, or natural language processing in the context of biomedical informatics are highly encouraged to apply. The position is focused on working with healthcare data including text data, administrative data, and image data to develop and test new approaches to study different healthcare pressing issues such as headaches phenotyping.
@ArtificialIntelligenceArticles

The successful candidate will work in close collaboration with the research team developers, participating in all phases of the research project: conceptualization, development of machine learning approaches; design and implementation; management and analysis; writing up results; and presenting findings. This two-year, full-time position (with the potential for extension) offers the opportunity to work alongside a prolific group of biomedical informatics researchers but requires creativity, excellent writing skills, and a strong interest in intellectual collaboration with a multi-disciplinary team. Position beginning Novemebr 2019, materials should be submitted no later than September 30, 2019.

The successful fellow will receive health insurance coverage and full access to the Yale University libraries.



Application Instructions

Candidates are requested to submit

· Curriculum Vitae or Resume

· 2 names of references

To: Recruiter ([email protected])
@ArtificialIntelligenceArticles
Yale University is an Affirmative Action/Equal Opportunity Employer and welcomes applications from women, members of minority groups, persons with disabilities and protected veterans.

Research Software Engineer in Machine Learning Applied to Finance, University of Oxford

Research Software Engineer in Machine Learning Applied to Finance
Department of Engineering Science, Oxford

We are seeking a full-time Research Software Engineer to work on applied Machine Learning and Software Development for Finance. The post involves the continued development of machine learning and financial simulation algorithms as well as liaison with industry partners (our collaborators in Man Group) to enable proof of concept deployment of algorithms on extensive data sets.

The postholder will have experience in producing software for data-centric machine learning. The role will focus as much on creating robust, reliable solutions as it will on innovation, suiting a candidate who is motivated by creating end to end solutions to address real-world problems.

You should possess a first degree in engineering, physics, computer science, mathematics, statistics or similar. Experience in practical applications of machine learning as well as expertise and experience in computer programming are essential. You should have a track record of experience and the ability to work well both independently and as part of a team.

You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application. Only applications received before 12.00 midday on 24 October 2019 can be considered.


More information can be found


https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form

TensorFlow 2.0 has been released! @ArtificialIntelligenceArticles
Release 2.0.0: https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0
#ArtificialIntelligence #DeepLearning #TensorFlow

Hamiltonian Graph Networks with ODE Integrators
Sanchez-Gonzalez et al.: https://arxiv.org/abs/1909.12790
#Hamiltonian #MachineLearning #Physics

Probabilistic Forecasting using Deep Generative Models. https://arxiv.org/abs/1909.11865

Spring 2019 Full Stack Deep Learning Bcootcamp
Hands-on program for developers familiar with the basics of deep learning

https://fullstackdeeplearning.com/march2019

Data Scientist Internship, France
Airbnb is committed to working with the best and brightest people from the broadest talent pool possible. We believe a diversity of ideas fosters innovation and engagement, and allows us to attract the best people, and to develop the best products, services and solutions. Qualified individuals from all walks of life are encouraged to apply.

Founded in August of 2008 and based in San Francisco, California, Airbnb is a trusted community marketplace for people to list, discover, and book unique travel experiences around the world. Whether an apartment for a night, a castle for a week, or a villa for a month, Airbnb allows people to Belong Anywhere through unique travel experiences at any price point, in more than 34,000 cities and over 191 countries. We promote a culture of curiosity, humanity, and creativity through our product, brand, and, most importantly, our people. In Nov 2016, Airbnb launched a new service, ‘Experiences’.
We are seeking a passionate, positive, bright, forward-thinking and entrepreneurial person to help boost the Kids and Families segment in the experiences business

To be eligible for an internship you must be currently studying within third level education or have graduated within this calendar year

Key Missions

Analysis:

Deep dive analysis into Airbnb’s vast data to uncover opportunities.
Partner with data scientists and analysts to learn and help “tell the story” behind the data.
Business Strategy:

Partnering and learning with business leadership to prioritize areas of opportunity to drive growth.
Evaluate and define business metrics.
Business Execution:

Communicate state of the business to stakeholders.
Digest and interpret deep dive analysis.
Decision Tools:

Democratize data by building and socializing decision tools (dashboards, reports).
Build key data sets/pipelines to empower operational and exploratory analysis.
REQUIREMENTS :

Currently pursuing a PhD, MS or BS in CS, Math, Statistics, Physics, Economics or other quantitative field.
Ability to think and execute at multiple altitudes: from strategy and vision to execution.
Interpersonal skills with demonstrated ability to influence outcomes and communicate technical content to general audiences, including ability to “story tell” with data.
Ability to write and mentor code development in SQL and Python (or R).
Experience with dashboard design and data viz tools (i.e. Tableau) is a plus.
Experience with building data pipelines is a plus.
Fluency in French and English is required.
****Please submit your CV in English*****

https://careers.airbnb.com/positions/1840514/

FourthBrain fellowship for 6 Months Residential # MachineLearning Training

landing.ai ,deeplearning.ai #deeplearning

The FourthBrain fellowship is a full-time, six-month residential training program with the goal of training strong software developers to be junior machine learning engineers. It starts in the fall 2019.
@ArtificialIntelligenceArticles
What is the cost of the program?

There are no upfront costs. You will pay back the program cost through an income share agreement - a portion of your future income over 5 years. In addition, they will provide you with housing and onsite meals, for the entirety of the program.

Applicants are expected to have the following background:@ArtificialIntelligenceArticles

-Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program in Python or a similarly modern advanced computer language.
@ArtificialIntelligenceArticles
-Familiarity and novice-level competencies/working knowledge with probability theory, multivariable calculus, and linear algebra

When does the program start, and where is it?

The program starts in the Fall of 2019 with the time being split between Silicon Valley and Puerto Rico.

Please contact programme sponsor for more information.
@ArtificialIntelligenceArticles
Apply:

https://jobs.lever.co/landing/24339f87-6fa7-4df9-a71b-04baa99f74ee/