Tips for Publishing Research Code

This repository represents several important tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations). These recommendations have been gathered based on analysis of more than 200 Machine Learning repositories, these recommendations facilitate reproducibility and correlate with GitHub stars - for more details, see our our blog post.

https://github.com/paperswithcode/releasing-research-code
#research_paper #machine_learning #neurIPS

The Notorious Difficulty of Comparing Human and Machine Perception

Abstract: With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed towards comparing information processing in humans and machines. These works have the potential to deepen our understanding of the inner mechanisms of human perception and to improve machine learning. Drawing robust conclusions from comparison studies, however, turns out to be difficult. Here, we highlight common shortcomings that can easily lead to fragile conclusions. First, if a model does achieve high performance on a task similar to humans, its decision-making process is not necessarily human-like. Moreover, further analyses can reveal differences. Second, the performance of neural networks is sensitive to training procedures and architectural details. Thus, generalizing conclusions from specific architectures is difficult. Finally, when comparing humans and machines, equivalent experimental settings are crucial in order to identify innate differences. Addressing these shortcomings alters or refines the conclusions of studies. We show that, despite their ability to solve closed-contour tasks, our neural networks use different decision-making strategies than humans. We further show that there is no fundamental difference between same-different and spatial tasks for common feed-forward neural networks and finally, that neural networks do experience a "recognition gap" on minimal recognizable images. All in all, care has to be taken to not impose our human systematic bias when comparing human and machine perception.

https://arxiv.org/abs/2004.09406
#machine_learning

TensorFlow Probability: Learning with confidence

TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, and ML researchers/practitioners who want to encode domain knowledge to understand data and make predictions with uncertainty estimates. In this talk we focus on the "layers" module and demonstrate how TFP "distributions" fit naturally with Keras to enable estimating aleatoric and/or epistemic uncertainty.

Website: https://www.tensorflow.org/probability

Introduction Video: https://www.youtube.com/watch?v=BrwKURU-wpk
#tensorflow #machine_learning

Mathematics for Machine Learning

Summary: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

https://mml-book.github.io/book/mml-book.pdf
#machine_learning #mathematics

Yann LeCuN advice for an undergraduate student who aspires to become a Machine Learning Scientist in the field of Deep Learning

(0) take all the continuous math and physics class you can possibly take. If you have the choice between “iOS programming” and “quantum mechanics”, take “quantum mechanics”. In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.
(1) Take an AI-related problem you are passionate about.
(2) think about it on your own
(3) once you have formed your own idea of it, start reading the literature on the problem
(4) you will find that (a) your ideas were probably a bit naive but (b) your view of the problem is slightly different from what was done before.
(5) Find a professor in your school that can help you make your ideas concrete. It might be difficult. Professors are busy and don’t have much time for undergrads. The ones with the most free time are the very junior, the very senior, and the ones who are not very active in research.
(6) If you don’ find a professor with spare time, hook up with a postdoc or PhD student in his/her lab.
(7) ask the professor if you can attend his/her lab meetings and seminars or sit in his/her class.
(8) Before you graduate, try to write a paper about your research or release a piece of open source code.
(9) Now apply to PhD programs. Forget about the “ranking” of the school for now. Find a reputable professor who works on topics that you are interested in. Pick a person whose papers you like or admire.
(10) Apply to several PhD programs in the schools of the above-mentioned professors and mention in your letter that you’d like to work with that professor but would be open to work with others.
(11) ask your undergrad professor to write a recommendation letter for you. It’s maximally efficient if your undergrad professor is known by your favorite PhD advisor.
(12) if you don’t get accepted in one of your favorite PhD programs, get a job at Facebook or Google and try to get a gig as an engineer assisting research scientists at FAIR or Google Brain.
(13) publish a papers with the research scientists in question. Then re-apply to PhD programs and ask the FAIR or Google scientists you work with to write a recommendation letter for you.

https://www.quora.com/What%E2%80%99s-your-advice-for-undergraduate-student-who-aspires-to-be-a-research-scientist-in-deep-learning-or-related-field-one-day
#machine_learning

Machine Learning & Computational Statistics Course

Course Intro: This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice.

https://davidrosenberg.github.io/ml2016/#home
#machine_learning #statistics #course