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The workshop welcomes the submission of work on, but not limited to, the following research directions. With the rapid development of advanced techniques on the intersection between information theory and machine learning, such as neural network-based or matrix-based mutual information estimator, tighter generalization bounds by information theory, deep generative models and causal representation learning, information theoretic methods can provide new perspectives and methods to deep learning on the central issues of generalization, robustness, explainability, and offer new solutions to different deep learning related AI applications.This workshop aims to bring together both academic researchers and industrial practitioners to share visions on the intersection between information theory and deep learning, and their practical usages in different AI applications. Submission site:https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, Ali Etemad (Queens University, ali.etemad@queensu.ca), Ali Etemad (Queens University, ali.etemad@queensu.ca), Ahmad Beirami (Facebook AI, ahmad.beirami@gmail.com), Akane Sano (Rice University, akane.sano@rice.edu), Aaqib Saeed (Philips Research & University of Cambridge, aqibsaeed@protonmail.com), Alireza Sepas-Moghaddam (Socure, alireza.sepasm@socure.com), Mathilde Caron (Inria & Facebook AI, mathilde@fb.com), Pritam Sarkar (Queens University & Vector Institute, pritam.sarkar@queensu.ca), Huiyuan Yang (Rice University, hy48@rice.edu), Supplemental website:https://hcssl.github.io/AAAI-22/. FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers. ISBN: 978-981-16-6053-5. Knowledge representation for business documents. 2085-2094, Aug 2016. Important Dates. Accepted contributions will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. in the proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), (acceptance rate: 26%), pp. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. All submissions must be in PDF format and formatted according to the new Standard AAAI Conference Proceedings Template. We invite researchers to submit either full-length research papers (8 pages) or extended abstracts (2 pages) describing novel contributions and preliminary results, respectively, to the topics above; a more extensive list of topics is available on the Workshop website. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, and Lingfei Wu. We encourage all the teams who participated in the challenge to join the workshop. Technology has transformed over the last few years, turning from futuristic ideas into todays reality. Attendance is open to all. 1, 2022: Call For Paper: The Undergraduate Consortium at SIGKDD 2022 is available at, Mar.