1 option
The NeurIPS '18 Competition : From Machine Learning to Intelligent Conversations / edited by Sergio Escalera, Ralf Herbrich.
- Format:
- Book
- Series:
- Computer Science (Springer-11645)
- Springer series on challenges in machine learning 2520-131X
- The Springer Series on Challenges in Machine Learning, 2520-131X
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Optical data processing.
- Pattern perception.
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Local Subjects:
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Physical Description:
- 1 online resource (VII, 342 pages) : 130 illustrations.
- Edition:
- First edition 2020.
- Contained In:
- Springer eBooks
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- System Details:
- text file PDF
- Summary:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-29135-8
- 9783030291358
- 9783030291341
- 9783030291365
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.