Published
2025
- Anil Radhakrishnan, Sudeshna Sinha, K. Murali, and William L. Ditto. (2025) "Gradient based optimization of Chaogates." Chaos, Solitons & Fractals
Github
Published
2023
- Anshul Choudhary, Anil Radhakrishnan, John F. Lindner, Sudeshna Sinha, and William L. Ditto. (2023) "Neural networks embrace learned diversity." Nature Scientific Reports
Preprint
Github
Published
- Kathleen Marie Russel, William L. Ditto, Anshul Choudhary, Anil Radhakrishnan, and John F. Lindner. (2023) "Diversity Based Deep Learning System."
Patent Pending
Published
2019
- Wenrui Wang, Tao Wang, Viveek P. Amin, Yang Wang, Anil Radhakrishnan, Angie Davidson, Shane R. Allen, T. J. Silvia, Hendrick Ohldag, Davor Balzar, Barry L. Zink, Paul M. Haney, John Q. Xiao, David G. Cahill, Virginia O. Lorenz, and Xin Fan. (2019) "Anomalous spin–orbit torques in magnetic single-layer films." Nature Nanotechnology
Published
2018
- Anil Radhakrishnan and Mattia Checchin. (2018) "Characterizating the Dirty Layer in Superconducting RF Accelerating Cavities." Lee Teng Talks
Published
Non-archival
2025
- Anil Radhakrishnan, John F. Lindner, Scott T. Miller, Sudeshna Sinha, and William L. Ditto. (2025) "When less is more: evolving large neural networks from small ones." arXiv
Preprint
Github
2018
- Shun Akatsuka, Shion Chen, Ben Hooberman, Anil Radhakrishnan, and Matt Zhang. (2018) "Machine learning techniques for soft leptons." ATLAS ML Workshop