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If you are interested in Physics Informed Machine Learning, Neural Operators, Machine Learning Theory, Computational Complexity, Reinforcement Learning and other cool stuff, check out my Reading List below. I will be updating this list as I read more papers and books.
- Kovachki, Nikola, et al. "Neural operator: Learning maps between function spaces." arXiv preprint arXiv:2108.08481 (2021).
- Li, Zongyi, et al. "Fourier neural operator for parametric partial differential equations." arXiv preprint arXiv:2010.08895 (2020).
- Li, Zongyi, et al. "Physics-informed neural operator for learning partial differential equations." arXiv preprint arXiv:2111.03794 (2021).
- Müller, Thomas, et al. "Instant neural graphics primitives with a multiresolution hash encoding." ACM Transactions on Graphics (ToG) 41.4 (2022): 1-15.
- Foret, Pierre, et al. "Sharpness-aware minimization for efficiently improving generalization." arXiv preprint arXiv:2010.01412 (2020).
- Nakkiran, Preetum. 2021. Towards an Empirical Theory of Deep Learning. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.
- Salimans, Tim, et al. "Evolution strategies as a scalable alternative to reinforcement learning." arXiv preprint arXiv:1703.03864 (2017).
- Sutton, R.S. & Barto, A.G., 2018. Reinforcement learning: An introduction, MIT press.
- Bandit Algorithms: Lattimore, Tor, Szepesvári, Csaba
- Coursera - Reinforcement Learning Specialization
- Dean, Walter, "Computational Complexity Theory", The Stanford Encyclopedia of Philosophy (Fall 2021 Edition), Edward N. Zalta (ed.), URL = https://plato.stanford.edu/archives/fall2021/entries/computational-complexity/.