December, 09-12, 2022. Exploring the limits of self-supervised learning approaches for speech and audio processing, for example, adverse environment conditions, multiple languages, or generalization across downstream tasks. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. Deep Graph Learning for Circuit Deobfuscation. For research track papers and applied data science track papers. Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, and Chang-TIen Lu. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 9.77%), to appear, 2022. Question answering on business documents. If you are interested, please send a short email to rl4edorg@gmail.com and we can add you to the invitee list. Factorized Deep Generative Models for End-to-End Trajectory Generation with Spatiotemporal Validity Constraints. KDD 2022 Reveals Schedule of Data Mining and Knowledge Discovery Papers Viliam Lisy (Czech Technical University in Prague, viliam.lisy@fel.cvut.cz), Noam Brown (Facebook AI Research, noambrown@fb.com), Martin Schmid (DeepMind, mschmid@google.com), Supplemental Workshop site:http://aaai-rlg.mlanctot.info/. Submissions may consist of up to 4 pages plus one additional page solely for references. Important Dates. Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, and Sai Dinakarrao. At least one author of each accepted submission must register and present their paper at the workshop. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. The third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) builds on the success of previous years PPAI-20 and PPAI-21 to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. Information theoretic quantities (entropy, mutual information, divergence) estimation, Information theoretic methods for out-of-domain generalization and relevant problems (such as robust transfer learning and lifelong learning), Information theoretic methods for learning from limited labelled data, such as few-shot learning, zero-shot learning, self-supervised learning, and unsupervised learning, Information theoretic methods for the robustness of DNNs in AI systems, The explanation of deep learning models (in AI systems) with information-theoretic methods, Information theoretic methods in different AI applications (e.g., NLP, healthcare, robotics, finance). The paper submissions must be in pdf format and use the AAAI official templates. Online and Distributed Robust Regressions with Extremely Noisy Labels. SIGKDD Explorations, Vol. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns.
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kdd 2022 deadline