mlsanityData to 7 Oct 2026

Randomized Algorithms for Scientific Computing (RASC)

arXiv 2104.11079 ↗First posted 19 Apr 2021cs.AI cs.CEcited by 3

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Abstract (arXiv)

Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. This report summarizes the outcomes of that workshop, "Randomized Algorithms for Scientific Computing (RASC)," held virtually across four days in December 2020 and January 2021.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 148 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

24 of 148 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  4. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  5. 2015Automatic differentiation in machine learning: a surveycited by 846
  6. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  7. 2017Stable Architectures for Deep Neural Networkscited by 340
  8. 2014Sketching as a Tool for Numerical Linear Algebracited by 285
  9. 2020Networks beyond pairwise interactions: structure and dynamicscited by 127
  10. 2018Distributed Differential Privacy via Shufflingcited by 115
  11. 2018An Online Plug-and-Play Algorithm for Regularized Image Reconstructioncited by 70
  12. 2019Scalable Semidefinite Programmingcited by 36
12 more
  1. 2020Solving Tall Dense Linear Programs in Nearly Linear Timecited by 21
  2. 2020Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernelscited by 17
  3. 2019Low-Rank Tucker Approximation of a Tensor From Streaming Datacited by 15
  4. 2020A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problemscited by 12
  5. 2019Recursive Sketches for Modular Deep Learningcited by 11
  6. 2014Path Sampling: A Fast and Provable Method for Estimating 4-Vertex Subgraph Countscited by 8
  7. 2020Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priorscited by 8
  8. 2014FAST-PPR: Scaling Personalized PageRank Estimation for Large Graphscited by 6
  9. 2018A stochastic Levenberg-Marquardt method using random models with complexity resultscited by 5
  10. 2020Error Estimation for Sketched SVD via the Bootstrapcited by 5
  11. 2017Community detection with spiking neural networks for neuromorphic hardwarecited by 3
  12. 2017Randomized Truncated SVD Levenberg-Marquardt Approach to Geothermal Natural State and History Matchingcited by 2

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 1.

3 papers.

  1. 2023Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximationcited by 7
  2. 2021Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebracited by 0
  3. 2025The Ubiquitous Sparse Matrix-Matrix Productscited by 0