An adaptive algorithm is an algorithm that changes its behavior at the time it is run, based on information available and on a priori defined reward mechanism (or criterion). Such information could be the story of recently received data, information on the available computational resources, or other run-time acquired (or a priori known) information related to the environment in which it operates. Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning. In adaptive filtering the LMS is used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal (difference between the desired and the actual signal). For example, , using no additional memory is O(n lg n) but given O(n) memory, it can be O(n) in time. As implemented by the C++ Standard Library, stable_partition is adaptive and so it acquires as much memory as it can get (up to what it would need at most) and applies the algorithm using that available memory. Another example is adaptive sort, whose behavior changes upon the presortedness of its input. An example of an adaptive algorithm in radar systems is the constant false alarm rate (CFAR) detector. In machine learning and optimization, many algorithms are adaptive or have adaptive variants, which usually means that the algorithm parameters such as learning rate are automatically adjusted according to statistics about the optimisation thus far (e.g. the rate of convergence). Examples include adaptive simulated annealing, adaptive coordinate descent, adaptive quadrature, AdaBoost, Adagrad, Adadelta, RMSprop, and Adam. In data compression, adaptive coding algorithms such as Adaptive Huffman coding or Prediction by partial matching can take a stream of data as input, and adapt their compression technique based on the symbols that they have already encountered. In signal processing, the Adaptive Transform Acoustic Coding (ATRAC) codec used in MiniDisc recorders is called "adaptive" because the window length (the size of an audio "chunk") can change according to the nature of the sound being compressed, to try to achieve the best-sounding compression strategy. (Wikipedia).
Introduction to Algorithms - What are they and how are they useful?
#3B1B #SoMe2 This is my submission for this year's SoME, SoME2!! I hope you enjoy, and please feel free to leave any comments. Any feedback is hugely appreciated~! ーーーーーーーーーーーーーーーーーーーーーーー Time Stamps: 00:00 Intro 00:37 Introduction to Algorithms 03:47 Exploring Algorithms - Binary Searc
From playlist Summer of Math Exposition 2 videos
Adaptive Sampling via Sequential Decision Making - András György
The workshop aims at bringing together researchers working on the theoretical foundations of learning, with an emphasis on methods at the intersection of statistics, probability and optimization. Lecture blurb Sampling algorithms are widely used in machine learning, and their success of
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Compute E Solution - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
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(ML 14.6) Forward-Backward algorithm for HMMs
The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).
From playlist Machine Learning
Raúl Tempone: Adaptive strategies for Multilevel Monte Carlo
Abstract: We will first recall, for a general audience, the use of Monte Carlo and Multi-level Monte Carlo methods in the context of Uncertainty Quantification. Then we will discuss the recently developed Adaptive Multilevel Monte Carlo (MLMC) Methods for (i) It Stochastic Differential Equ
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Alina Ene: Adaptive gradient descent methods for constrained optimization
Adaptive gradient descent methods, such as the celebrated Adagrad algorithm (Duchi, Hazan, and Singer; McMahan and Streeter) and ADAM algorithm (Kingma and Ba), are some of the most popular and influential iterative algorithms for optimizing modern machine learning models. Algorithms in th
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Jana Cslovjecsek: Efficient algorithms for multistage stochastic integer programming using proximity
We consider the problem of solving integer programs of the form min {c^T x : Ax = b; x geq 0}, where A is a multistage stochastic matrix. We give an algorithm that solves this problem in fixed-parameter time f(d; ||A||_infty) n log^O(2d) n, where f is a computable function, d is the treed
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Algorithms Explained: What is an Algorithm?
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Rigorous Data Dredging...Data Analysis - Aaron Roth
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Pandora's Box with Correlations: Learning and Approximation - Shuchi Chawla
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NIPS 2011 Domain Adaptation Workshop: Discrepancy and Adaptation
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Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 7 - Kate Rakelly (UC Berkeley)
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Josh Bongard - A xither of xenobots: demolishing dichotomous thinking with synthetic proto-organisms
Recorded 17 February 2022. Josh Bongard of the University of Vermont presents "A xither of xenobots: demolishing dichotomous thinking with synthetic proto-organisms" at IPAM's Mathematics of Collective Intelligence Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops
From playlist Workshop: Mathematics of Collective Intelligence - Feb. 15 - 19, 2022.
The Visual Task Adaptation Benchmark
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From playlist General Machine Learning
Discrete Optimization Under Uncertainty - Sahil Singla
Short talks by postdoctoral members Topic: Discrete Optimization Under Uncertainty. Speaker: Sahil Singla Affiliation: Member, School of Mathematics Date: October 2, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Nexus Trimester - Sidharth Jaggi (The Chinese University of Hong Kong)
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From playlist Plenary talks One World Symposium 2020
Sophia Economou - Problem-tailored variational quantum algorithms - IPAM at UCLA
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Make A Combination Lock - Intro to Algorithms
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From playlist Introduction to Algorithms