"AdaBoosted Deep Ensembles: Getting Maximum Performance Out of Small Training Datasets"

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Book	Author(s)	Year	Publisher
Caution: small ensembles	Edward Koren	1983	Pantheon Books
Maximum performance	Laurence Englemohr Morehouse	1977	Simon and Schuster
Jazz arranging and performance practice: a guide for small ensembles	Paul E. Rinzler	1989	Scarecrow Press
Deep strengths: getting to the heart of high performance	Price Pritchett	2008	McGraw-Hill
Maximum interval training	John Cissik	2015	Human Kinetics
Maximum Interval Training	John Cissik; Jay Dawes	2015	Human Kinetics
Maximum sports performance	James F. Fixx	1985	Random House
Breakthrough: maximum sports training	Michael Spino	1984	Pocket Books
Piloting for maximum performance	Lewis Bjork	1996	McGraw-Hill
Small ensembles in elementary music education	Phyllis J. Perry	1949
Getting the Best Out of Performance Management in Your School	Chris Baker; Kate Everall; Franklin Hartle	2001	Routledge
Maximum Vocal Performance (Vocal Coach)	Chris Beatty	March 1992	Star Song Communications Group
Out of Deep Waters	Gail May	October 23, 2003	Pleasant Word-A Division of WinePress Publishing
Out of earth's deep	Edward Leighton	1977	Hale;The Crowood Press
Out of the deep	Noel Davidson	2003	Ambassador
Out of the deep	Gloria Skurzynski	2008	National Geographic Society
Out of the deep	Gloria Skurzynski	2002	National Geographic Society
Escapade: music for large and small ensembles /	Spaniola, Joseph T.,
Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets	Le Lu; Yefeng Zheng; Gustavo Carneiro; Lin Yang	1st ed. 2017	Springer International Publishing
Getting Out of Debt	Howard Lape Dayton	April 1986	Tyndale House Publishers
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
AdaBoosted Deep Ensembles: Getting Maximum Performance Out of Small Training Datasets	Syed M. S. Reza; John A. Butman; Deric M. Park; Dzung L. Pham; Snehashis Roy	Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings	Mingxia Liu; Pingkun Yan; Chunfeng Lian; Xiaohuan Cao	1st ed. 2020	Springer International Publishing
Getting datasets		Mastering Python Data Visualization	Kirthi Raman	2015	Packt Publishing
Small Ensembles	Robert Toft	Recording Classical Music	Robert Toft	2019	Routledge
Small ensembles		The world of music	Hugo D. Marple	©1975	Allyn and Bacon
Small ensembles		The story of musical organizations	Robert W. Surplus, George Overlie	1963	Lerner Publications Co.
Training of Deep Learning Models Using Synthetic Datasets	Zdzisław Kowalczuk; Jan Glinko	Intelligent and Safe Computer Systems in Control and Diagnostics	Zdzislaw Kowalczuk	1st ed. 2023	Springer International Publishing
Getting the Most out of Training	Malcolm C Cross; David Glass	A Beginner's Guide to Training in Counselling & Psychotherapy	Robert Bor; Stephen Palmer	December 2001	SAGE Publications Ltd
Maximum-Entropy Ensembles of Graphs	Tiziano Squartini; Diego Garlaschelli	Maximum-Entropy Networks: Pattern Detection, Network Reconstruction and Graph Combinatorics	Tiziano Squartini; Diego Garlaschelli	1st ed. 2017	Springer International Publishing
Supervised Greedy Layer-Wise Training for Deep Convolutional Networks with Small Datasets	Diego Rueda-Plata; Raúl Ramos-Pollán; Fabio A. González	Computational Collective Intelligence: 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I	Manuel Núñez; Ngoc Thanh Nguyen; David Camacho; Bogdan Trawiński	1st ed. 2015	Springer International Publishing
Training Datasets and Test Datasets		The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code	Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat	2020	Packt Publishing
Training Datasets and Test Datasets		The Deep Learning with Keras Workshop: An Interactive Approach to Understanding Deep Learning with Keras, 2nd Edition	Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat	2020	Packt Publishing
Comparing the Performance of Classical and Deep Learning Methods on Small Image Datasets	Vassili Kovalev	Pattern Recognition and Information Processing: 15th International Conference, PRIP 2021, Minsk, Belarus, September 21–24, 2021, Revised Selected Papers	Alexander V. Tuzikov; Alexei M. Belotserkovsky; Marina M. Lukashevich	1st ed. 2022	Springer International Publishing
Small vocal ensembles		Singing in the schools.	Music Educators National Conference (U.S.). Committee on Literature and Interpretation of Music for Choral Organizations.	1958	[s.n.]
Small vocal ensembles.		Selective music lists, 1968.	Music Educators National Conference (U.S.)	1968
Small vocal ensembles		Singing in the schools: Small vocal ensembles. Assembly singing. Choral music in the junior high school, and its relation to the adolescent with particular reference to boys\' voices	Music Educators National Conference (U.S.)	©1958	Music Educators' National Conference
Organizing small ensembles		Choral techniques	Gordon H. Lamb	©1974	W.C. Brown Co
Handling Small Datasets		Practical Deep Learning: A Python-Based Introduction	Ron Kneusel	2021	No Starch Press
Deep Knowledge Tracing on Skills with Small Datasets	Ange Tato; Roger Nkambou	Intelligent Tutoring Systems: 18th International Conference, ITS 2022, Bucharest, Romania, June 29 – July 1, 2022, Proceedings	Scott Crossley; Elvira Popescu	1st ed. 2022	Springer International Publishing
Getting the Maximum Out of Your Business School		Hacks for Life and Career: A Millennial’s Guide to Making it Big	Sandeep Das	November 2020	SAGE Response
Getting maximum instructional mileage out of classroom assessment		The truth about testing: an educator's call to action /	Popham, W. James.	©2001.	Association for Supervision and Curriculum Development,
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