Stochastic computing is a collection of techniques that represent continuous values by streams of random bits. Complex computations can then be computed by simple bit-wise operations on the streams. Stochastic computing is distinct from the study of randomized algorithms.
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bits. Complex computations can then be computed by simple bit-wise operations on the streams. Stochasticcomputing is distinct from the study of randomized...
Unconventional computing is computing by any of a wide range of new or unusual methods. It is also known as alternative computing. The term unconventional...
Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear...
better than "true" stochastic gradient descent described, because the code can make use of vectorization libraries rather than computing each step separately...
Hilbert's fifth problem Commutation theorem Hyperfinite type II factor Stochasticcomputing Ultrastrong topology Self-replication von Neumann's theorem von Neumann's...
The stochastic block model is a generative model for random graphs. This model tends to produce graphs containing communities, subsets of nodes characterized...
In machine learning, the term stochastic parrot is a metaphor to describe the theory that large language models, though able to generate plausible language...
In mathematics, a stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number...
subtly incorrect. Stochasticcomputing was introduced by von Neumann in 1953, but could not be implemented until advances in computing of the 1960s. Around...
programming, stochastic dynamic programming represents the problem under scrutiny in the form of a Bellman equation. The aim is to compute a policy prescribing...
Convolution kernel Stochastic kernel, the transition function of a stochastic process Transition kernel, a generalization of a stochastic kernel Pricing kernel...
Neumann (1903–1957), Hungary – Von Neumann computer architecture, Stochasticcomputing, Merge sort algorithm Isaac Newton (1642–1727), UK – reflecting telescope...
values of functions which cannot be computed directly, but only estimated via noisy observations. In a nutshell, stochastic approximation algorithms deal with...
population Diffusion process, a solution to a stochastic differential equation Empirical process, a stochastic process that describes the proportion of objects...
A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) with individual probabilities. Realizations...
population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of...
architecture and worked on linear programming, self-replicating machines, stochasticcomputing), and statistics. Emmy Noether was an influential mathematician known...
price of an asset being computable by "discounting" the future cash flow x ~ i {\displaystyle {\tilde {x}}_{i}} by the stochastic factor m ~ {\displaystyle...
In numerical analysis, stochastic tunneling (STUN) is an approach to global optimization based on the Monte Carlo method-sampling of the function to be...
mathematical finance to compute the sensitivities of financial derivatives. The calculus has applications in, for example, stochastic filtering. Malliavin...
In mathematics, stochastic geometry is the study of random spatial patterns. At the heart of the subject lies the study of random point patterns. This...
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