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Showing 1 to 4 of 4 for “"probabilistic computing"”.
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Accelerating Probabilistic Computing with a Stochastic Processing Unit
… learning becomes a more important workload for computing systems than ever before. Probabilistic computing is a popular approach in statistical machine learning, which solves problems by iteratively generating samples from parameterized distributions. As an alternative to Deep Neural Networks, …
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Photonic probabilistic machine learning using quantum vacuum noise
Probabilistic machine learning is an emerging paradigm which harnesses controllable random sources to encode uncertainty and enable statistical modeling. The pure randomness of quantum vacuum noise, fluctuation of electromagnetic fields even in the absence of a photon, has been utilized for high …
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Natively probabilistic computation
I introduce a new set of natively probabilistic computing abstractions, including probabilistic generalizations of Boolean circuits, backtracking search and pure Lisp. I show how these tools let one compactly specify probabilistic generative models, generalize and parallelize widely used sampling …
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Superparamagnetic Tunnel Junctions for Reliable True Randomness and Efficient Probabilistic Machine Learning
… potential to enable complex probability-based computing algorithms, accelerate machine learning tasks, and enhance hardware security. Recently, superparamagnetic tunnel junctions (sMTJs) have been widely explored for such purposes, leading to the development of limited-scale sMTJ-based systems. …