NeurIPS 2021 : Neural Information Processing Systems (NIPS)

in Conferences   Posted on March 31, 2021 

Conference Information

Submission Deadline Wednesday 19 May 2021 Proceedings indexed by :
Conference Dates Dec 6, 2021 - Dec 14, 2021
Conference Address Online, Online
Conference & Submission Link https://nips.cc/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Impact Score 33.49
#Contributing Top Scientists 510
#Papers published by Top Scientists 1175
Google Scholar H5-index 198
Number of Editions: 35 (Since 1987)
Guide2Research Overall Ranking: 2
Category Rankings
Computational Theory and Mathematics 1
Machine Learning & Artificial Intelligence 2
Signal Processing 2
Proceedings https://papers.nips.cc/
DBLP Page https://dblp.uni-trier.de/db/conf/nips

Conference Call for Papers

The Thirty-Fifth Annual Conference on Neural Information Processing Systems (NeurIPS 2021) is an interdisciplinary conference that brings together researchers in machine learning, computational neuroscience, statistics, optimization, economics, computer vision, natural language processing, computational biology, and other fields. We invite submissions presenting new and original research on topics including but not limited to the following:

General Machine Learning (e.g., classification, unsupervised learning, transfer learning)
Deep Learning (e.g., architectures, generative models, optimization for deep networks)
Reinforcement Learning (e.g., decision and control, planning, hierarchical RL)
Applications (e.g., speech processing, computational biology, computer vision, NLP)
Probabilistic Methods (e.g., variational inference, causal inference, Gaussian processes)
Optimization (e.g., convex and non-convex optimization)
Neuroscience and Cognitive Science (e.g., neural coding, brain-computer interfaces)
Theory (e.g., control theory, learning theory, algorithmic game theory)
Infrastructure (e.g., datasets, competitions, implementations, libraries)
Social Aspects of Machine Learning (e.g., AI safety, fairness, privacy, interpretability)

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