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Date: 2 December 2024
Chapter: Hong Kong Chapter
Chapter Chair: Yik Chung Wu
Title: Harnessing the Power of Deep Learning for Urban Sound Sensing and Noise Mitigation
Inspired by the capabilities of transformer models, we introduce a novel method named Multivariate Time-Series Imputation with Transformers (MTSIT). This entails an unsupervised autoencoder model featuring a transformer encoder, leveraging unlabeled observed data for simultaneous reconstruction and imputation of multivariate time-series.
Applications are invited for postdoctoral researcher positions in the general area of optimization and learning of network systems. Competitive financial supports will be provided.
Candidates with a clear interest in the general area of network systems are encouraged to apply.
Specific areas of research include:
Date: 11 November 2024
Chapter: Croatia Chapter
Chapter Chair: Tomislav Petkovic
Title: Deep Generative AI
Date: 6 August 2024
Time: 1:00 PM ET (New York Time)
Presenter(s): Dr. Shinji Watanabe, Dr. Abdelrahman Mohamed
Dr. Karen Livescu, Dr. Hung-yi Lee, Dr. Tara Sainath,
Dr. Katrin Kirchhoff & Dr. Shang-Wen Li
Manuscript Due: 30 January 2025
Publication Date: September 2025
Date: 3 July 2024
Time: 10:00 AM ET (New York Time)
Speaker(s): Prof. (Kit) Kai-Kit Wong
Manuscript Due: 1 November 2024
Publication Date: November 2025
Date: 2 July 2024
Chapter: Slovenia Chapter
Chapter Chair: Andrej Trost
Title: Biasness & Fairness, Worth Considering in AI
Date: 10 July 2024
Time: 10:00 AM ET (New York Time)
Speaker(s): Prof. A. Chockalingam
Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning approaches have been studied for motion artifact reduction. Unfortunately, most existing models are trained in a supervised manner, requiring paired motion-corrupted and motion-free images, or are based on a strict motion-corruption model, which limits their use for real-world situations.