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International Conference on Learning Representations

International Conference on Learning Representations

Kigali , Rwanda

Submission Deadline: Wednesday 21 Sep 2022

Conference Dates: May 01, 2023 - May 05, 2023

Research
Impact Score 31.80

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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: 31.80
Contributing Best Scientists: 757
H5-index:
Papers published by Best Scientists 1458
Research Ranking (Computer Science) 7

Conference Call for Papers

A non-exhaustive list of relevant topics explored at the conference include:

unsupervised, semi-supervised, and supervised representation learning
representation learning for planning and reinforcement learning
representation learning for computer vision and natural language processing
metric learning and kernel learning
sparse coding and dimensionality expansion
hierarchical models
optimization for representation learning
learning representations of outputs or states
implementation issues, parallelization, software platforms, hardware
applications in audio, speech, robotics, neuroscience, computational biology, or any other field
societal considerations of representation learning including fairness, safety, privacy

Overview

Top Research Topics at International Conference on Learning Representations?

  • Artificial intelligence (54.91%)
  • Machine learning (22.30%)
  • Artificial neural network (22.16%)

The discussions in International Conference on Learning Representations mainly cover the fields of Artificial intelligence, Machine learning, Artificial neural network, Algorithm and Deep learning. Natural language processing and Pattern recognition are some topics wherein Artificial intelligence research discussed in International Conference on Learning Representations have an impact. The Pattern recognition research dealing mostly with Convolutional neural network is the focus of it.

International Conference on Learning Representations holds forums on Machine learning that merges themes from other disciplines such as Adversarial system, Inference, Robustness (computer science) and Benchmark (computing). The event facilitates discussions on Artificial neural network that incorporate concepts from other fields like Generalization and Theoretical computer science. Reinforcement learning research featured in International Conference on Learning Representations incorporates concerns from various other topics such as Mathematical optimization and Human–computer interaction.

What are the most cited papers published at the conference?

  • Adam: A Method for Stochastic Optimization (46989 citations)
  • Very Deep Convolutional Networks for Large-Scale Image Recognition (40331 citations)
  • Neural Machine Translation by Jointly Learning to Align and Translate (14865 citations)

Research areas of the most cited articles at International Conference on Learning Representations:

The conference publications explore disciplines such as Artificial intelligence, Machine learning, Artificial neural network, Algorithm and Pattern recognition. Aside from discussions in Artificial intelligence, the conference papers also deal with the subject of Natural language processing which intersects with Word (computer architecture) disciplines. The published papers facilitate discussions on Machine learning that incorporate concepts from other fields like Language model, Inference and Benchmark (computing).

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 International Conference on Learning Representations (based on the number of publications) are:

  • Yoshua Bengio (77 papers) published 11 papers at the last edition, 6 more than at the previous edition,
  • Sergey Levine (69 papers) published 18 papers at the last edition, 4 more than at the previous edition,
  • Aaron Courville (30 papers) published 8 papers at the last edition, 6 more than at the previous edition,
  • Ruslan Salakhutdinov (29 papers) published 5 papers at the last edition, 1 more than at the previous edition,
  • Pieter Abbeel (29 papers) published 6 papers at the last edition, 4 more 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 International Conference on Learning Representations (based on the number of publications) are:

  • Google (549 papers) published 116 papers at the last edition, 28 less than at the previous edition,
  • University of California, Berkeley (231 papers) published 57 papers at the last edition, 16 more than at the previous edition,
  • Carnegie Mellon University (217 papers) published 61 papers at the last edition, 17 more than at the previous edition,
  • Stanford University (208 papers) published 68 papers at the last edition, 24 more than at the previous edition,
  • Massachusetts Institute of Technology (193 papers) published 49 papers at the last edition the same number as 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 2021 edition, 1.45% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 44.91% were posted by at least one author from the top 10 institutions publishing at the conference. Another 13.85% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.25% of all publications and 20.99% 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

Previous Editions

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