Mathematical Perspective of Machine Learning
arXiv:2007.01503v19 citations
Originality Synthesis-oriented
AI Analysis
It addresses foundational issues for researchers, but appears incremental as it reviews existing perspectives without new empirical findings.
The paper examines theoretical challenges in machine learning, including function approximation, gradient descent, network architecture limitations, and a mathematical approach to RNNs, without presenting specific results or numbers.
We take a closer look at some theoretical challenges of Machine Learning as a function approximation, gradient descent as the default optimization algorithm, limitations of fixed length and width networks and a different approach to RNNs from a mathematical perspective.