A BREAKTHROUGH IN MACHINE LEARNING IN 2021
Top 4 Important Machine Learning and Deep Learning Papers You Should Read in 2021
Curated from hundreds of high-quality ML research papers, these are the ones that stood out the most.
Important Machine Learning and Deep Learning Papers in 2021
Machine Learning suddenly became one of the most critical domains of Computer Science and just about anything related to Artificial Intelligence.
Every company is applying Machine Learning and developing products that take advantage of this domain to solve their problems more efficiently.
Every year, 1000s of research papers related to Machine Learning are published in popular publications like NeurIPS, ICML, ICLR, ACL, and MLDS.
The criteria are using citation counts from three academic sources: scholar.google.com; academic.microsoft.com; and semanticscholar.org.
“Key research papers in natural language processing, conversational AI, computer vision, reinforcement learning, and AI ethics are published yearly”
Almost all of the papers provide some level of findings in the Machine Learning field. However, three papers particularly stood, which provided some real breakthrough in the field of Machine Learning, particularly in the Neural Network domain.
Deep Residual Learning for Image Recognition
Abstract:
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those…
