2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
Do Quantum Models Scale Like LLMs?
David S. Berman, Ying-Jer Kao, Roger G. Melko +1
In this work, we study the neural scaling laws of RydbergGPT, an autoregressive transformer model trained on qubit projective measurement data gathered from interacting Rydberg ato…
Optimal Pruning for Neural Architectures using Fisher Information Distances
David S. Berman, Yen-Yu Fu, Edward Hirst +1
A new scheme for parameter pruning is introduced, derived from the differential-geometric distance in model space. Pruning a parameter sets its value to zero, representing a displa…
AI and the Research-Education Environment of Physics
Savannah Thais, Koji Hashimoto, David S. Berman +6
In the current era of AI transforming the research-education environment of physics, variety of issues and concerns arise. The KITP program "Generative AI for High and Low Energy P…
Towards Worst-Case Guarantees with Scale-Aware Interpretability
Lauren Greenspan, David Berman, Aryeh Brill +9
Neural networks organize information according to the hierarchical, multi-scale structure of natural data. Methods to interpret model internals should be similarly scale-aware, exp…
A path to natural language through tokenisation and transformers
David S. Berman, Alexander G. Stapleton
Natural languages exhibit striking regularities in their statistical structure, including notably the emergence of Zipf's and Heaps' laws. Despite this, it remains broadly unclear…