The intense democratization of toolkits coupled with the breakneck speed at which research is unfolding in Deep learning, the literature landscape might seem chaotic and cacophonous at times.

Hence, I truly appreciate when the well cited authors in a specific vertical of research invest time and effort to author good overview/survey/review/meta papers. Besides the obvious good of providing a comprehensive bird’s-eye view of the field, they serve 5 crucial purposes that are oft ignored.

1) These are high quality invitation notes to researchers from a different domain to contribute

2) They serve as collection of important open problems waiting to be solved

3) Immensely helpful in faster, better and up-to-date teaching course design

4) Setting the agenda for the research directions in the near future

5) Eases the burden of lengthy citation lists, especially for short communication papers.

This year, I chanced upon 7 such papers that I am sharing with the ML community here.

Happy year end reading!

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List:

1. Advances and Open Problems in Federated Learning, https://arxiv.org/pdf/1912.04977.pdf
2. Deep learning for time series classification: a review, https://arxiv.org/pdf/1809.04356.pdf
3. Optimization for deep learning: theory and algorithms, https://arxiv.org/pdf/1912.08957.pdf
4. Normalizing Flows: An Introduction and Review of Current Methods, https://arxiv.org/pdf/1908.09257.pdf
5. Normalizing Flows for Probabilistic Modeling and Inference, https://arxiv.org/pdf/1912.02762.pdf
6. Fantastic Generalization Measures and Where to Find Them, https://arxiv.org/pdf/1912.02178.pdf
7. Neural Style Transfer: A Review, https://arxiv.org/pdf/1705.04058.pdf

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Cheat-sheet for print:

https://preview.redd.it/4y22qaqp9t741.png?width=1792&format=png&auto=webp&s=ff3aa1c76374530983cb3ae561ff777afe57db69