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cs.LG2026
Neural Global Optimization via Iterative Refinement from Noisy Samples
Qusay Muzaffar, David Levin, Michael Werman
Global optimization of black-box functions from noisy samples is a fundamental challenge in machine learning and scientific computing. Traditional methods such as Bayesian Optimiza…
cs.LG2024
The Fibonacci Network: A Simple Alternative for Positional Encoding
Yair Bleiberg, Michael Werman
Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional E…