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Kigali, Rwanda
Conference
ICLR 2023
May 1 - 5, 2023
Kigali, Rwanda

Overview

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.

Sponsorship Details

Gold
Conference website
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Amazon team

Accepted publications

Workshops

ICLR 2023 Workshop on Trustworthy Machine Learning for Healthcare
May 3
Explaining multiclass classifiers with categorical values: A case study in radiography
Luca Franceschi, Cemre Zor, Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias Seeger
2023
Machine learning
ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models
May 4
Modality-aware adaptation of contrastive language-image models
Alexander Long, Ajanthan Thalaiyasingam, Anton van den Hengel
2023
Machine learning
ICLR 2023 Workshop on Successful Domain Generalization
May 4
In this workshop, we aim to work towards answering a single question: what do we need for successful domain generalization? We conjecture that additional information of some form is required for a general purpose learning methods to be successful in the DG setting. The purpose of this workshop is to identify possible sources of such information, and demonstrate how these extra sources of data can be leveraged to construct models that are robust to distribution shift.

Amazon Organizer: Aniket Deshmukh

Website: https://domaingen.github.io/
ICLR 2023 Workshop on Deep Learning for Code (DL4C)
May 5
The second DL4C workshop will provide a vibrant virtual platform for researchers to share their work on deep learning for code, emphasizing recent advances and challenges, for example: HCI for Code, Evaluation for Code, Inference for Code, Responsible AI for Code, and Open Sourcing efforts in deep learning for code.

Amazon organizer: Zijian Wang

Amazon program committee: Ming Tan, Mingyue Shang, Qing Sun, Robert Giaquinto, Siddhartha Jain, Yang Li

Website: https://dl4c.github.io
Semantic prefixes as an auxiliary target for code understanding and generation
Kechen Qin, Fan Yang, Kai-Wei Chang, Prashan Wanigasekara, Xinyue Liu, Chengwei Su, Emre Barut
2023
Conversational AI / Natural-language processing
ICLR 2023 Workshop on Pitfalls of Limited Data and Computation for Trustworthy ML
May 5
ICLR 2023 Workshop on Practical Machine Learning for Developing Countries
May 5
S2vNTM: Semi-supervised VMP neural topic modeling
Weijie Xu, Jay Desai, Srinivasan Sengamedu, "SHS", Xiaoyu Jiang, Francis Iannacci
2023
Conversational AI / Natural-language processing
S2vNTM: Semi-supervised vMF neural topic modeling
Weijie Xu, Jay Desai, Srinivasan Sengamedu, "SHS", Xiaoyu Jiang, Francis Iannacci
2023
Conversational AI / Natural-language processing
ICLR 2023 Tiny Paper
May 5
Learned learning rate schedules for deep neural network training using reinforcement learning
Shreyas Subramanian, Vignesh Ganapathiraman, Aly El Gamal
2023
Machine learning
Efficient learning rate schedules for stochastic non-negative matrix factorization via reinforcement learning
Shreyas Subramanian, Vignesh Ganapathiraman, Aly El Gamal
2023
Machine learning
Bandit sampling for faster neural network training with SGD
Francisco Calderon Rodriguez, Anila Joshi, Vignesh Ganapathiraman
2023
Machine learning

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