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NeurIPS 2021 : Neural Information Processing Systems (NIPS)

NeurIPS 2021 : Neural Information Processing Systems (NIPS)

Online

Submission Deadline: Friday 21 May 2021

Conference Dates: Dec 06, 2021 - Dec 06, 2021

Research
Impact Score 42.30

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Ranking & Metrics Impact Score is a novel metric devised to rank conferences based on the number of contributing the best scientists in addition to the h-index estimated from the scientific papers published by the best scientists. See more details on our methodology page.

Research Impact Score: 42.30
Contributing Best Scientists: 1148
H5-index:
Papers published by Best Scientists 2435
Research Ranking (Computer Science) 2
Research Ranking (Neuroscience) 2

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)

Overview

Top Research Topics at Neural Information Processing Systems?

  • Artificial intelligence (45.21%)
  • Machine learning (20.21%)
  • Algorithm (19.19%)

Neural Information Processing Systems is organized to address concerns in the fields of Artificial intelligence, Machine learning, Algorithm, Mathematical optimization and Pattern recognition. The Artificial intelligence study featured in the event draws connections with the study of Computer vision. Topics in Machine learning were tackled in line with various other fields like Training set and Bayesian probability.

Topics in Mathematical optimization explored in Neural Information Processing Systems were investigated in conjunction with research in Function (mathematics), Regret and Applied mathematics.

What are the most cited papers published at the conference?

  • ImageNet Classification with Deep Convolutional Neural Networks (46647 citations)
  • Generative Adversarial Nets (22315 citations)
  • Attention is All you Need (19650 citations)

Research areas of the most cited articles at Neural Information Processing Systems:

Artificial intelligence, Machine learning, Pattern recognition, Algorithm and Mathematical optimization are the main subjects of interest in the most cited publications. The most cited publications explore research in Artificial intelligence and the adjacent study of Computer vision. Classifier (UML), Training set and Data mining are some topics wherein Machine learning research discussed in the conference articles has an impact.

What topics the last edition of the conference is best known for?

  • Artificial intelligence
  • Statistics
  • Machine learning

The previous edition focused in particular on these issues:

The main research concerns discussed in Neural Information Processing Systems are Artificial intelligence, Artificial neural network, Machine learning, Benchmark (computing) and Algorithm. The work on Artificial intelligence tackled in it brings together disciplines like Natural language processing, Computer vision and Pattern recognition. Artificial neural network research presented in it encompasses a variety of subjects, including Speech recognition, Theoretical computer science and Topology.

Neural Information Processing Systems blends together research topics in Machine learning and Benchmarking. The research on Benchmark (computing) discussed in Neural Information Processing Systems draws on the closely related field of Baseline (configuration management). The Algorithm study featured in the conference draws parallels with the field of Backpropagation.

The most cited articles from the last conference are:

  • What Size Net Gives Valid Generalization (1381 citations)
  • ALVINN: An Autonomous Land Vehicle in a Neural Network (1112 citations)
  • Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment (463 citations)

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Research.com

The top authors publishing at Neural Information Processing Systems (based on the number of publications) are:

  • Michael I. Jordan (121 papers) published 8 papers at the last edition, 6 more than at the previous edition,
  • Bernhard Schölkopf (89 papers) published 4 papers at the last edition, 2 less than at the previous edition,
  • Yoshua Bengio (79 papers) published 3 papers at the last edition, 6 less than at the previous edition,
  • Francis Bach (66 papers) published 5 papers at the last edition, 6 less than at the previous edition,
  • Geoffrey E. Hinton (63 papers) published 1 paper at the last edition, 2 less than at the previous edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Research.com

Only papers with recognized affiliations are considered

The top affiliations publishing at Neural Information Processing Systems (based on the number of publications) are:

  • Massachusetts Institute of Technology (760 papers) published 112 papers at the last edition, 27 more than at the previous edition,
  • Carnegie Mellon University (644 papers) published 91 papers at the last edition, 6 more than at the previous edition,
  • Stanford University (627 papers) published 114 papers at the last edition, 24 more than at the previous edition,
  • Google (621 papers) published 196 papers at the last edition, 60 more than at the previous edition,
  • Microsoft (583 papers) published 110 papers at the last edition, 34 more than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Research.com

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

Research.com

During the most recent 2020 edition, 1.92% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 38.27% were posted by at least one author from the top 10 institutions publishing at the conference. Another 15.49% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.13% of all publications and 27.11% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Research.com

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

Research.com

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

Research.com

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Research.com

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Previous Editions

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