Position Paper Neurips


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Advances in Neural Information Processing Systems 13


Advances in Neural Information Processing Systems 13

Author: Todd K. Leen

language: en

Publisher: Bradford Book

Release Date: 2001-05-11


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The proceedings of the 2000 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2000 conference.

The NeurIPS '18 Competition


The NeurIPS '18 Competition

Author: Sergio Escalera

language: en

Publisher: Springer Nature

Release Date: 2019-11-29


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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. 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.

Transfer Learning


Transfer Learning

Author: Qiang Yang

language: en

Publisher: Cambridge University Press

Release Date: 2020-02-13


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This in-depth tutorial for students, researchers, and developers covers foundations, plus applications ranging from search to multimedia.