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International Conference on Learning Representations (ICLR 2024)

Name: International Conference on Learning Representations (ICLR 2024)
Start: 2024-05-07
End: 2024-05-11

company logo for ICLR ICLR

Artificial Intelligence (AI) - Conference

Time: From May 7, 2024 to May 11, 2024

Event Location: Austria (Vienna)

Key Points:

• The premier gathering of professionals dedicated to representation learning

• Renowned for cutting-edge research on all aspects of deep learning

• Artificial intelligence, statistics and data science, machine vision, gaming

• Computational biology, speech recognition, text understanding, robotics

• From academic & industrial researchers, to entrepreneurs & engineers, to student

Last updated: August 22, 2023

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The Twelfth annual conference is held Tue. May 7th through Sat the 11th, 2024 at the Vienna.

ICLR 2024 Meeting Dates

Virtual Only Pass: Sun May 7th through Thu the 11th
Conference Sessions and Workshops: Tue May 7th through Fri the 10th
Saturday Workshop 1 Day Pass: Sat May 11th

Event Description and Details

About Us

The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.

ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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
optimal transport
theoretical issues in deep learning
societal considerations of representation learning including fairness, safety, privacy, and interpretability, and explainability
visualization or interpretation of learned representations
implementation issues, parallelization, software platforms, hardware
climate, sustainability
applications in audio, speech, robotics, neuroscience, biology, or any other field

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