Design and Analysis of Algorithms

By Dexter Campbell Kozen. Lecture Notes in Computer Science, 2009

1,917 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/4907d96e2029c2cd0359a5e030bbf7e2bb3418da

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Citations in the corpus

Finite Discrete Gabor Analysis (2007, methodology): “A generalization of this technique is the so called time-variant ltering, which recently has grown more and more important [110].”
DEVELOPING A PREDICTIVE APPROACH TO KNOWLEDGE (2015, methodology): “This means we can form an incremental update rule for ht using a least mean squared (LMS) rule (due to Widrow & Stearns, 1985): ∆ht+1 = αh [ (δλρt − (h>t x(St)))x(St) ] , 20 where αh > 0 is a step size parameter.”
Basic definition and properties of Bessel multipliers (2005, methodology): “Keywords: Bessel sequences, Bessel multiplier, Bessel norm, Riesz bases, Riesz multipliers, discrete expansion, tensor product 1 Introduction The application of signal processing algorithms like adaptive or time variant filters are numerous [18].”
Algorithms for Reinforcement Learning (2010, methodology): “Using the Sherman-Morrison formula, one can derive an incremental version of LSTD, analogously to how the recursive least-squares (RLS) method is derived in adaptive filtering Widrow and Stearns (1985).”
On the influence of momentum acceleration on online learning (2016, background): “Motivated by these useful acceleration properties in the deterministic context, momentum terms have been subsequently introduced into stochastic optimization algorithms as well (Polyak, 1987; Proakis, 1974; Sharma et al., 1998; Shynk and Roy, June 1988; Roy and Shynk, 1990; Tugay and Tanik, 1989; Be”
The cerebellar microcircuit as an adaptive filter: experimental and computational evidence (2010, background): “The versatility of adaptive filters corresponds to the many uses of adaptive filters in engineering contexts (for example, see Ref”
Adaptation, Learning, and Optimization over Networks (2014, background): “121) leads to the complex form of the least-meansquares (LMS) algorithm [107, 207, 263]: wi = wi−1 + μui [d(i) − uiwi−1], i ≥ 0 (3.”
Accelerating Stochastic Gradient Descent (2017, background): “In the signal processing literature, where SGD goes by Least Mean Squares (LMS) (Widrow and Stearns, 1985), there have been efforts that date to several decades (Proakis, 1974; Roy and Shynk, 1990; Sharma et al., 1998) which study accelerated LMS methods (stochastic variants of CG/HB) in the same…”
Stochastic Gradient Descent as Approximate Bayesian Inference (2017, methodology): “This was done in the context of least mean square ﬁlters (Widrow and Stearns, 1985).”
A Variational Analysis of Stochastic Gradient Algorithms (2016, methodology): “Note the optimal scalar learning rate of 2 / Tr( BB (cid:62) ) can also be derived based on stability arguments, as was done in the context of least mean square ﬁlters (Widrow and Stearns, 1985).”
Neural Network Toolbox™ User's Guide (2015, background): “The network shown above is referred to in the digital signal processing field as a finite impulse response (FIR) filter [WiSt85].”