Adaptive sampling is a technique used in computational molecular biology to efficiently simulate protein folding when coupled with molecular dynamics simulations. (Wikipedia).

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

Frequency Domain Interpretation of Sampling

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Analysis of the effect of sampling a continuous-time signal in the frequency domain through use of the Fourier transform.

From playlist Sampling and Reconstruction of Signals

Quota Sampling

What is quota sampling? Advantages and disadvantages. General steps and an example of how to find a quote sample. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.

From playlist Sampling

Probability Sampling Methods

What is "Probability sampling?" A brief overview. Four different types, their advantages and disadvantages: cluster, SRS (Simple Random Sampling), Systematic and Stratified sampling. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with

From playlist Sampling

Systematic Sampling

What is systematic sampling? Advantages and disadvantages. How to perform systematic sampling and repeated systematic sampling. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.c

From playlist Sampling

Practical DSP and Oversampling

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Limitations of analog anti-aliasing and anti-imaging filters motivate a practical digital filtering approach in which high rates are used for sampli

From playlist Sampling and Reconstruction of Signals

Quantization and Coding in A/D Conversion

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Real sampling systems use a limited number of bits to represent the samples of the signal, resulting in quantization of the signal amplitude t

From playlist Sampling and Reconstruction of Signals

JUDGMENT and SNOWBALL Non-random Sampling (12-6)

Judgment sampling (a.k.a., expert sampling, authoritative sampling, purposive sampling, judgmental sampling) is a technique in which the sample is selected based on the researcher’s (or other experts’) existing knowledge or professional judgment. It may provide highly accurate findings wit

Snowball Sampling Overview

Brief Introduction to Snowball Sampling. Advantages and disadvantages. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sampling-in-statistics

From playlist Sampling

Neural Voice Cloning

In this video, we take a look at a paper released by Baidu on Neural Voice Cloning with a few samples. The idea is to “clone” an unseen speaker’s voice with only a few sound clips. If you like the video, hit that like button. Ring the bell to stay notified of my videos on Machine Learning

From playlist Deep Learning Research Papers

Research Talk: Mutation Bias and Rates by Deepa Agashe

DISCUSSION MEETING SECOND PREPARATORY SCHOOL ON POPULATION GENETICS AND EVOLUTION ORGANIZERS Deepa Agashe (NCBS-TIFR, India) and Kavita Jain (JNCASR, India) DATE: 20 February 2023 to 24 February 2023 VENUE Madhava Lecture Hall, ICTS Bengaluru We plan an intensive 1-week preparatory school

This paper presents a new benchmark for Visual Task Adaptation (i.e. BERT for images) and investigates several baseline methods for doing so. Abstract: Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets. Yet, the

From playlist General Machine Learning

Pandora's Box with Correlations: Learning and Approximation - Shuchi Chawla

Computer Science/Discrete Mathematics Seminar I Topic: Pandora's Box with Correlations: Learning and Approximation Speaker: Shuchi Chawla Affiliation: University of Wisconsin-Madison Date: April 05, 2021 For more video please visit http://video.ias.edu

From playlist Mathematics

Traditional sampling techniques (grid vs random vs sobol vs latin hypercube)

Welcome to video #1 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video, Sterling introduces the concept of adaptive experimentation and covers t

From playlist Optimization tutorial

Evolutionary Impacts of Biased Mutation Spectra by Deepa Agashe

PROGRAM FIFTH BANGALORE SCHOOL ON POPULATION GENETICS AND EVOLUTION (ONLINE) ORGANIZERS: Deepa Agashe (NCBS, India) and Kavita Jain (JNCASR, India) DATE: 17 January 2022 to 28 January 2022 VENUE: Online No living organism escapes evolutionary change, and evolutionary biology thus conn

Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 7 - Kate Rakelly (UC Berkeley)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Kate Rakelly (UC Berkeley) Guest Lecture in Stanford CS330 http://cs330.stanford.edu/ 0:00 Introduction 0:17 Lecture outline 1:07 Recap: meta-reinforcement lear

From playlist Stanford CS330: Deep Multi-Task and Meta Learning

DDPS | Neural Galerkin schemes with active learning for high-dimensional evolution equations

Title: Neural Galerkin schemes with active learning for high-dimensional evolution equations Speaker: Benjamin Peherstorfer (New York University) Description: Fitting parameters of machine learning models such as deep networks typically requires accurately estimating the population loss

Stanford Webinar: Common Pitfalls of A/B Testing and How to Avoid Them

A Stanford Webinar presented by the Stanford Leadership & Management Science (http://stanford.io/2ppiCxy) and Decision Analysis (http://stanford.io/2pByYDC) graduate certificate programs "Common Pitfalls of A/B Testing and How to Avoid Them" Speaker: Ramesh Johari, Stanford University A/

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

From playlist Probability and Statistics

Convenience Sampling

What is convenience sampling? Advantages and disadvantages of grab sampling. How to analyze data from convenience sampling. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creato

From playlist Sampling

## Related pages

Folding@home | Hidden Markov model | Phase space