“There are some things which cannot be learned quickly, and time, which is all we have, must be paid heavily for their acquiring. They are the very simplest things, and because it takes a man’s life to know them the little new that each man gets from life is very costly and the only heritage he has to leave.” - Ernest Hemingway (More…)
News #
I will be updating both good news, bad news and all kinds of news.
List of Selected Papers on Algorithms for Large-Scale Graph Processing.
1/ [ISAAC'11] Goodrich, M. T., Sitchinava, N., & Zhang, Q. (2011, December). Sorting, searching, and simulation in the mapreduce framework. In International Symposium on Algorithms and Computation (pp. 374-383). Springer, Berlin, Heidelberg. 1 2 3 4 5 6 7 8 @inproceedings{goodrich2011sorting, title={Sorting, searching, and simulation in the mapreduce framework}, author={Goodrich, Michael T and Sitchinava, Nodari and Zhang, Qin}, booktitle={International Symposium on Algorithms and Computation}, pages={374--383}, year={2011}, organization={Springer} } 2/ [STOC'14] Andoni, A., Nikolov, A., Onak, K., & Yaroslavtsev, G. (2014, May). Parallel algorithms for geometric graph problems. In Proceedings of the forty-sixth annual ACM symposium on Theory of computing (pp. 574-583).
Reading list & mathematics resources.
Math Reading List
Reading list on Graph Learning - Explainable artificial intelligence (xAI).
XAI-Graph # 2023 # [1] Azzolin, S., Longa, A., Barbiero, P., Liò, P., & Passerini, A. (2022). Global explainability of gnns via logic combination of learned concepts. arXiv preprint arXiv:2210.07147. [2] Miao, S., Luo, Y., Liu, M., & Li, P. (2022). Interpretable Geometric Deep Learning via Learnable Randomness Injection. arXiv preprint arXiv:2210.16966. [3] Liu, Y., Zhang, X., & Xie, S. (2023, February). A Differential Geometric View and Explainability of GNN on Evolving Graphs. In The Eleventh International Conference on Learning Representations.
Mathematics
Study Mathematics # Master of Science in Mathematics @ HCMUS Branches of Mathematics # 1. Foundation of Mathematics # Transition To Pure Rigour Math Set Theory Logic Category Theory Type Theory Homotopy Type Theory Surreal Numbers 2. Number Theory # Algebraic Number Theory Analytic Number Theory 3. Algebra # Abstract Algebra Group Theory Linear Algebra Ring Theory Galois Theory Lie Algebras 4. Combinatorics # Probabilistic methods in Combinatorics Algebraic Combinatorics Graph Theory 5. Geometry Topology # Differential Geometry Algebraic Geometry Algebraic Statistics Topology Algebraic Topology 6. Mathematical analysis # Real Analysis Harmonic Analysis Complex Analysis Functional Analysis Measure Theory ODE PDE Variational Analysis Calculus of Variations Calculus (Single/ Multi-variables) Optimization & Operation Research Dynamical Systems Set-valued Analysis 7. Probability and Statistics # Probability Theory Statistics Statistical Learning Stochastic processes 8. Numerical Analysis # 9. Signal Processing # 10. Mathematics for Computer Science # 11. Mathematical Physics #
Parallel Programming
CUDA C++ Programming Guide (Link) Table of instruction throughputs (Link) PTX Reference Manual (Link) Inline PTX syntax guide (Link) Tensor core instruction data layouts (Link) SASS Instruction List (Link) Compiler Explorer by Matt Godbolt (Link) GPU Mode (Link) Modal GPU Glossary (Link)