David S. Berman

According to our database, David S. Berman authored at least 6 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Explainable AI: definition and attributes of a good explanation for health AI.

Evangelia Kyrimi, Scott McLachlan, Jared M. Wohlgemut, Zane B. Perkins, David A. Lagnado, William Marsh, Alexander Gimson, Ali Shafti, Ari Ercole, Amitava Banerjee, Ben Glocker, Burkhard Schafer, Constantine Gatsonis, Crina Grosan, Danielle Sent, David S. Berman, David Glass, Declan P. O'Regan, Dimitrios Letsios, Dylan Morrissey, Erhan Pisirir, Francesco Leofante, Hamit Soyel, Jon Williamson, Keri Grieman, Kudakwashe Dube, Max Marsden, Myura Nagendran, Nigel Tai, Olga Kostopoulou, Owain Jones, Paul Curzon, Rebecca S. Stoner, Sankalp Tandle, Shalmali Joshi, Somayyeh Mossadegh, Stefan Buijsman, Tim Miller, Vince Istvan Madai

AI Ethics, August, 2025

Grokking vs. Learning: Same Features, Different Encodings.

Dmitry Manning-Coe, Jacopo Gliozzi, Alexander G. Stapleton, Edward Hirst, Giuseppe De Tomasi, Barry Bradlyn, David S. Berman

CoRR, February, 2025

2024

The Inverse of Exact Renormalization Group Flows as Statistical Inference.

David S. Berman, Marc S. Klinger

Entropy, May, 2024

NCoder - A Quantum Field Theory approach to encoding data.

David S. Berman, Marc S. Klinger, Alexander G. Stapleton

CoRR, 2024

2023

Bayesian renormalization.

David S. Berman, Marc S. Klinger, Alexander G. Stapleton

Mach. Learn. Sci. Technol., December, 2023

2022

On the Dynamics of Inference and Learning.

David S. Berman, Jonathan J. Heckman, Marc Klinger

CoRR, 2022