3 citations · 6 across the 6 of their papers we have counts for
6 papers
Learned Diffractive Optics for Quantum-Optimal Inference
Matthew J. Filipovich, Alexander Duplinskii, A. I. Lvovsky
Quantum mechanics sets the ultimate bounds on photon-limited sensing, yet practical measurements attaining these bounds are known only in special cases. This is particularly the ca…
Diffractive neural networks for mode-sorting with flexible detection regions
Kaden Bearne, Alexander Duplinskiy, Matthew J. Filipovich +1
Mode-sorting is a procedure that decomposes a light field into a basis of transverse modes, directing each mode into a separate spatial location, allowing the constituent mode inte…
TorchOptics: An open-source Python library for differentiable Fourier optics simulations
Matthew J. Filipovich, A. I. Lvovsky
TorchOptics is an open-source Python library for differentiable Fourier optics simulations, developed using PyTorch to enable GPU-accelerated tensor computations and automatic diff…
Streamlined optical training of large-scale modern deep learning architectures with direct feedback alignment
Ziao Wang, Kilian Müller, Matthew Filipovich +12
Modern deep learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly c…
Role of Spatial Coherence in Diffractive Optical Neural Networks
Matthew J. Filipovich, Aleksei Malyshev, A. I. Lvovsky
Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks,…
Scaling Laws Beyond Backpropagation
Matthew J. Filipovich, Alessandro Cappelli, Daniel Hesslow +1
Alternatives to backpropagation have long been studied to better understand how biological brains may learn. Recently, they have also garnered interest as a way to train neural net…