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NeurIPS 2022: 36th Conference on Neural Information Processing Systems

NeurIPS 2022: 36th Conference on Neural Information Processing Systems

New Orleans , United States

Submission Deadline: Monday 16 May 2022

Conference Dates: Nov 28, 2022 - Dec 09, 2022

Research
Impact Score 2.40

OFFICIAL WEBSITE

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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: 2.40
Contributing Best Scientists: 27
H5-index:
Papers published by Best Scientists 25
Research Ranking (Computer Science) 2
Research Ranking (Neuroscience) 2

Conference Call for Papers

Call For Papers
Abstract submission deadline: Monday, May 16, 2022 01:00 PM PDT

Full paper submission and co-author registration deadline: Thursday, May 19, 2022 01:00 PM PDT

Supplementary materials submission deadline: Thursday, May 26, 2022 01:00 PM PDT

Author notification: Wednesday, September 14, 2022

Camera-ready, poster, and video submission: Wednesday, October 12, 2022 01:00 PM PDT

Submit at: https://openreview.net/group?id=NeurIPS.cc/2022/Conference

The site will start accepting submissions on April 16, 2022.



The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following:

General Machine Learning

Deep Learning (e.g., architectures, generative models, optimization for deep networks)

Reinforcement Learning (e.g., decision and control, planning, hierarchical RL, robotics)

Applications (e.g., speech processing, computer vision, NLP)

Machine Learning for Sciences (e.g. biology, physics, health sciences, social sciences)

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, human-AI interaction, ethics)

Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories.

Authors will be asked to confirm that their submissions accord with the NeurIPS code of conduct.

Formatting instructions: All submissions must be in PDF format. Submissions are limited to nine content pages, including all figures and tables; additional pages containing the NeurIPS paper checklist and references are allowed. The page limit was increased to ensure that authors have space to address the checklist questions. You must format your submission using the NeurIPS 2022 LaTeX style file which includes a “preprint” option for non-anonymous preprints posted online. The maximum file size for submissions is 50MB. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. If your submission is accepted, you will be allowed an additional content page for the camera-ready version. Papers may be rejected without consideration of their merits if they fail to meet the submission requirements, as described in this document.

Double-blind reviewing: All submissions must be anonymized and may not contain any identifying information that may violate the double-blind reviewing policy. This policy applies to any supplementary or linked material as well, including code. If you are including links to any external material, it is your responsibility to guarantee anonymous browsing. Please do not include acknowledgements at submission time. If you need to cite one of your own papers, you should do so with adequate anonymization to preserve double-blind reviewing. For instance, write “In the previous work of Smith et al. [1]…” rather than “In our previous work [1]...”). If you need to cite one of your own papers that is in submission to NeurIPS and not available as a non-anonymous preprint, then include a copy of the cited submission in the supplementary material and write “Anonymous et al. [1] concurrently show...”).

OpenReview: Same as last year, we are using OpenReview to manage submissions. The reviews and author responses will not be public initially. As in previous years, submissions under review will be visible only to their assigned program committee. We will not be soliciting comments from the general public during the reviewing process. Anyone who plans to submit a paper as an author or a co-author will need to create (or update) their OpenReview profile by the full paper submission deadline. The information entered in the profile is critical for ensuring that conflicts of interest are handled properly. Because of the rapid growth of NeurIPS, we request that all authors help with reviewing papers, if asked to do so. We need everyone’s help in maintaining the high scientific quality of NeurIPS.

Abstract Submission: There is a mandatory abstract submission deadline on May 16, 2022 01:00 PM PDT, three days before full paper submissions are due. While it will be possible to edit the title and abstract until the full paper submission deadline, submissions with “placeholder” abstracts that are rewritten for the full submission risk being removed without consideration. This includes titles and abstracts that either provide little or no semantic information (e.g., "We provide a new semi-supervised learning method.") or describe a substantively different claimed contribution. Changes may be made to the author list until the full paper deadline. After that, authors may be reordered, but any additions or removals must be justified in writing and approved on a case-by-case basis by the program chairs.

Supplementary material: Authors may submit up to 100MB of supplementary material, such as appendices, proofs, derivations, data, or source code; all supplementary materials must be in PDF or ZIP format. Supplementary material should be material created by the authors that directly supports the submission content. Like submissions, supplementary material must be anonymized. Looking at supplementary material is at the discretion of the reviewers.

We encourage authors to upload their code and data as part of their supplementary material in order to help reviewers assess the quality of the work. Check the policy as well as code submission guidelines and templates for further details.

Ethics review: Reviewers and ACs may flag submissions for ethics review. Flagged submissions will be sent to an ethics review committee for comments. Comments from ethics reviewers will be considered by the primary reviewers and AC as part of their deliberation. They will also be visible to authors, who will have an opportunity to respond. Ethics reviewers do not have the authority to reject papers, but in extreme cases papers may be rejected by the program chairs on ethical grounds, regardless of scientific quality or contribution.

Paper checklist: In order to improve the rigor and transparency of research submitted to and published at NeurIPS, authors are required to complete a paper checklist, which is included in the tex template. The paper checklist is intended to help authors reflect on a wide variety of issues relating to responsible machine learning research, including reproducibility, transparency, research ethics, and societal impact. The checklist must appear in the submitted PDF, immediately after references, but does not count towards the page limit.

Preprints: The existence of non-anonymous preprints (on arXiv or other online repositories, personal websites, social media) will not result in rejection. If you choose to use the NeurIPS style for the preprint version, you must use the “preprint” option rather than the “final” option. Reviewers will be instructed not to actively look for such preprints, but encountering them will not constitute a conflict of interest. Authors may submit anonymized work to NeurIPS that is already available as a preprint (e.g., on arXiv) without citing it.

Dual submissions: Submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to other peer-reviewed venues with proceedings may not be submitted to NeurIPS. (Work that has appeared in non-archival workshops, such as workshops at NeurIPS/ICML, may be submitted.) NeurIPS coordinates with other conferences to identify dual submissions. The NeurIPS policy on dual submissions applies for the entire duration of the reviewing process. Slicing contributions too thinly is discouraged. The reviewing process will treat any other submission by an overlapping set of authors as prior work. If publishing one would render the other too incremental, both may be rejected.

Author responses: Authors will have one week to view and respond to initial reviews. Author responses may not contain any identifying information that may violate the double-blind reviewing policy. After the initial response period, authors will be able to respond to any further reviewer/AC questions and comments by posting on the submission’s forum page. The program chairs reserve the right to solicit additional reviews after the initial author response period. These reviews will become visible to the authors as they are added to OpenReview, and authors will have a chance to respond to them.

After the notification deadline, accepted and opted-in rejected papers will be made public and open for non-anonymous public commenting. Their anonymous reviews, meta-reviews, and author responses will also be made public. Authors of rejected papers will have two weeks after the notification deadline to opt in to make their deanonymized rejected papers public in OpenReview. These papers are not counted as NeurIPS publications and will be shown as rejected in OpenReview.

After the initial author response, we will allow for a rolling discussion with the authors. This discussion will be made public later for papers that become public.

Authors may submit revisions of their paper during the discussion period, but the reviewers and ACs are not required to read them.

Publication of accepted submissions: Reviews, meta-reviews, and any discussion with the authors will be made public for accepted papers (but reviewer, area chair, and senior area chair identities will remain anonymous). Camera-ready papers will be due in advance of the conference. All camera-ready papers must include a funding disclosure. We strongly encourage accompanying code and data to be submitted with accepted papers when appropriate, as per the code submission policy. Authors will be allowed to make minor changes for a short period of time after the conference.

Other Tracks: Similarly to earlier years, NeurIPS 2022 will host multiple tracks, such as datasets and benchmarks, tutorials as well as workshops, in addition to the main track for which this call for papers is intended. See the conference homepage for updates and calls for participation in these tracks.

Experiments: As in past years, the program chairs will be measuring the quality and effectiveness of the review process via randomized controlled experiments. All experiments are independently reviewed and approved by an Institutional Review Board (IRB).

Frequently asked questions can be found here (https://neurips.cc/Conferences/2022/PaperInformation/NeurIPS-FAQ).

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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