9th International Conference on Learning Representations

May 4th, 2021
Cyber Security Leadership Summit

 May 4th, 2021  

9th International Conference on Learning Representations

Event submitted on Monday, October 12th 2020, approved by Henry Dalzel

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194 Days Until The Event
May 4th, 2021
Austria » 
Event Website

This event has been tagged as follows:

* This is an online event (webinar)

 Our Review

A highly recommended virtual event on May 4th. ICLR is renowned globally in representation and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science.

- Review written by Henry Dalziel on Monday, October 12th 2020.
- If you would like to edit or ammend facts in my review please either send us a message or connect with me via LinkedIn.

Event Summary

The following description was either submitted by the Conference Organizer on Monday, October 12th 2020, or created by 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
-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

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