We saw excellent progress with enterprise acceptance of machine learning across a wide swath of industries and problem domains. In terms of pure research, I had a good time tracking the acceleration of progress in the area of machine learning. In this article, we'll take a tour of my top pick of papers that I found intriguing and useful. In my attempt to stay current with the field's research progress, the directions represented here are very promising. I hope you enjoy the results as much as I have. Overfitting & underfitting and stable training are important challenges in machine learning. Current approaches for these issues are mixup, SamplePairing, and BC learning. This paper states the hypothesis that mixing many images together can be more effective than just two.

The field of machine learning has continued to accelerate through 2019, moving at light speed with compelling new results coming out of academia and the research arms of large tech firms like Google, Microsoft, Yahoo, Facebook and many more. It's a daunting task for the down-in-the-trenches data scientist to keep pace. I advise my data science students at UCLA to be up on the latest research results in order to keep ahead of the pack. I recount how industry luminary Andrew Ng keeps his head above water by toting around a file of research papers (so when he has a free moment, like riding on an Uber, he can consume part of a paper). It does take time to add the research realm to your everyday duties, but I think it's fun to know what technologies are fertile areas of research.

Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in the modern machine learning practice. The bias-variance trade-off implies that a model should balance under-fitting and over-fitting: rich enough to express underlying structure in data, simple enough to avoid fitting spurious patterns. However, in the modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered over-fit, and yet they often obtain high accuracy on test data.

While usually you would use world-based algorithms, the team suggests that that method is at fault due to the fact that they treat all possible worlds equally, despite the negative effects some may cause, and that they do not well-utilize the consistency among possible worlds that is there. The team introduces a representative possible world-based consistent clustering algorithm for this type of uncertain data, with results showing better than other state-of-the-art algorithms.

Machine Learning and the data science industry is always changing. To keep you updated on the most recent discoveries, we've compiled the 5 most exciting machine learning research pieces that expand what we thought we knew about machine learning and the industries to which it relates. Fairness in machine learning has been a heavy topic of discussion since the beginnings of the technology, but now, in a paper by Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, and Ed H. Chi we have some theoretical models to ensure fairness across different applications of one machine learning model. They frame this issue as "domain adaptation problems: how can we use what we have learned in a source domain to debias in a new target domain, without directly debiasing on the target domain as if it is a completely new problem?" In the paper, they also offer "a modeling approach to transfer to data-sparse target domains… [and] empirical results validating the theory and showing that these modeling approaches can improve fairness metrics with less data" In a recent paper by Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, and Stefano Ermon, they explore "which uncertainties are needed for model-based reinforcement learning and argues that good uncertainties must be calibrated."