Call for contribution

We are pleased to invite you to contribute to the International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. Topics of interest for DLP 2023 include but are not limited to deep learning based network architecture design, large-scale deep learning training framework, high-performance online inference engines, or toolkits that help break the black box of deep learning models, such as

• Large-scale user response prediction modeling
• Representation learning for high-dimensional sparse data
• Embedding techniques, manifold learning, and dictionary learning
• User behavior understanding
• Large-scale recommendation and retrieval system
• Model compression for industrial application
• Scalable, distributed, and parallel training system for deep learning
• High throughput and low latency real-time serving system
• Applications of transfer learning, meta learning for sparse data
• Auto machine learning, auto feature selection
• Explainable deep learning for high-dimensional data
• Data augmentation, and anomaly detection for high-dimensional sparse data
• Generative adversarial network for sparse data
• Large language model-enhanced recommender systems
• Other challenges encountered in real-world applications

Submission and Formatting Instructions

Submissions are limited to a total of 9 (nine) pages in a double-column format, including all content and references. Submissions must be in PDF format and formatted according to the latest ACM Conference Proceedings Template. Short papers are also welcomed. Reviews are not double-blind, and author names and affiliations should be listed.

All submissions can be made through EasyChair. We plan to archive the accepted papers (For example, on Springer).

Important Dates

• Paper submission: August 3th, 2023
• Notications: August 27th, 2023
• Camera ready: September 10th, 2023

Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.