36 citations · 36 across the 7 of their papers we have counts for
7 papers
A Quantum Roadmap for Softmax Attention: Exact Born-Rule Analogs for Softmax Attention on the Probability Simplex
Eric A. F. Reinhardt, Adam J. Hauser
The attention mechanism forms the foundation of many modern AI models such as the Transformer. In one subclass of problems where attention is used, inputs and outputs are bound to…
Resource-Efficient QUBO Formulation for Anchored Currency Arbitrage
Eric A. F. Reinhardt, Adam J. Hauser
Currency arbitrage (CA) involves trading currencies in cycles to exploit discrepancies in market valuations. Quadratic unconstrained binary optimization (QUBO) involves minimizing…
GNN For Muon Particle Momentum estimation
Vishak K Bhat, Eric A. F. Reinhardt, Sergei Gleyzer
Due to a high rate of overall data generation relative to data generation of interest, the CMS experiment at the Large Hadron Collider uses a combination of hardware- and software-…
Continual Learning via Ensemble-Based Depth-Wise Masked Autoencoders for Data Quality Monitoring in High-Energy Physics
Dale Julson, Eric Reinhardt, Andrii Krutsylo +5
Machine learning (ML) techniques have been demonstrated to improve the accuracy and efficiency of anomaly detection (AD) when compared to conventional methods. This has led to the…
Sinusoidal Approximation Theorem for Kolmogorov-Arnold Networks
Sergei Gleyzer, Hanh Nguyen, Dinesh P. Ramakrishnan +1
The Kolmogorov-Arnold representation theorem states that any continuous multivariable function can be exactly represented as a finite superposition of continuous single variable fu…
Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric A F Reinhardt, Talal Ahmed Chowdhury +2
Neural Quantum States (NQS) are a class of variational wave functions parametrized by neural networks (NNs) to study quantum many-body systems. In this work, we propose \texttt{Sin…
SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation Functions
Eric A. F. Reinhardt, P. R. Dinesh, Sergei Gleyzer
Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses lea…