This page compiles advice offered by researchers and tech professionals regarding different aspects of a career in computer science and artificial intelligence.
Experiments can take a long time... It is therefore critical to not fall into a slow iteration cycle too early in the course of a short-term project... When analyzing results, be hungry for useful information.
Advice for Short-Term Machine Learning Research Projects — Tim Rocktäschel, Jakob Foerster and Greg Farquhar (08/29/2018)
A good ML practitioner needs to know how to choose an algorithm for a particular application and how to monitor and respond to feedback obtained from experiments... If you have time to tune only one hyperparameter, tune the learning rate... on a logarithmic scale.
Deep Learning Practical Methodology — Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
Quite often, you’ll need to come up with your own diagnostics... If you’re working on one important ML application for months/years, it’s very valuable for you personally to get an intuitive understanding of what works and what doesn’t work in your problem.
Advice for Applying Machine Learning — Andrew Ng (2012)
Many widely adopted neural NLP algorithms were built on top of the core ideas in traditional NLP models... Familiarize yourself with machine learning fundamentals... Be mind-open and keep track of the related problems in other domains such as computer vision and data mining.
Advice for Beginners in Natural Language Processing — Jiwei Li (03/14/2019)
Calendars convert time to space. They make the finiteness of time apparent. In a way that physical space constraints are apparent... It is easier to measure how wrong your time estimates are than it is to fix them... Replanning is part of the plan.
Calendar. Not to-do lists. — Devi Parikh (04/25/2018)
I tend... to imagine that the person who wrote this paper is very early in their career... and the last thing I want to do is to discourage that person from continuing to try... What we really want is for the scientific field to move forward... and for important new information that's going to affect the way people do things to get out there.
On Writing Quality Peer Reviews — Noah A. Smith (01/07/2019)
Your comments should begin by summarizing the main ideas of the submission and relating these ideas to previous work... You should then summarize the strengths and weaknesses of the submission, focusing on each of the following four criteria: Quality, Clarity, Originality and Significance.
NeurIPS Reviewer, AC & SAC Guidelines — The NeurIPS Program Committee (2018)
Be prepared with multiple potential projects... be sure to know what the goal is, what is needed to accomplish it... Some undergraduates may prefer more direction... Your primary goal should be to ensure frequent communication... understand the approach and the implementation, at least at some level.
(Graduate Student) Collaborating with Undergraduates in Research — Michael D. Ernst (01/28/2019)
Whenever appropriate, papers on machine learning will... be evaluated on the basis of... 1) Novelty of algorithm; 2) Novelty of application/problem; 3) Difficulty of application; 4) Quality of results; 5) Insight conveyed... Application papers should... describe work that has direct relevance to, and addresses the full complexity of, solving a non-trivial problem.
Guidelines for Writing A Good NeurIPS Paper — The NeurIPS 2006 Program Committee (2013)
Your purpose is to communicate specific ideas, and everything about your paper should contribute to this goal... you should give away the punchline... Make your writing crisp and to the point... Avoid puffery, self-congratulation, and value judgments: give the facts and let the reader judge.
How to Write A Technical Paper — Michael D. Ernst (11/10/2018)