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.
Oct-14-2021, 06:16:03 GMT