The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations: The Springer Series on Challenges in Machine Learning
Editat de Sergio Escalera, Ralf Herbrichen Limba Engleză Hardback – 30 noi 2019
Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.
The eight run competitions aim at advancing the state of the art in deep reinforcement learning, adversarial learning, and auto machine learning, among others, including new applications for intelligent agents in gaming and conversational settings, energy physics, and prosthetics.
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Specificații
ISBN-13: 9783030291341
ISBN-10: 3030291340
Pagini: 342
Ilustrații: VII, 342 p. 130 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.67 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria The Springer Series on Challenges in Machine Learning
Locul publicării:Cham, Switzerland
ISBN-10: 3030291340
Pagini: 342
Ilustrații: VII, 342 p. 130 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.67 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria The Springer Series on Challenges in Machine Learning
Locul publicării:Cham, Switzerland
Textul de pe ultima copertă
This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics.
Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.
Caracteristici
The NeurIPS Competition evaluates the performance of an entire system rather than a well-isolated task or component, enabling the evaluation of the feasibility of an algorithm extracting key predictive signal from a dataset, as well as the implementation in a running production system Presents a stronger focus on live competitions, including the competition finals at the NeurIPS 2018 conference New data sets and top winning solutions are presented