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Learned Compressive Representations for Single-Photon 3D Imaging

Related publications (30)

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

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PMLR2024

Topics in statistical physics of high-dimensional machine learning

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

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and momentum parameter

\beta

that allows u ...

2024