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The Latest News, Articles, and Events in Signal Processing
Date: 17-20 January 2024 (Hybrid)
Location: Tororo Campus , Kampala Uganda
Date: 18-22 December 2023 (In-person)
Location: Chennai, Tamil Nadu, India
Date: 5-6 January 2024 (In-person)
Location: Tamil Nadu, India
Date: 11-16 December 2023 (In-person)
Location: Coimbatore, Tamil Nadu, India
Date: 11-15 December 2023 (Hybrid)
Location: Bangalore, Karnataka, India
Date: 4-9 December 2023 (Virtual)
Location: Pune, Maharashtra, India
Date: 24-28 November 2023 (Hybrid)
Location: BTKIT Dwarahat, Uttarakhand
Date: 1-2 March 2024 (Hybrid)
Location: Rajkot, Gujarat, India
Date: 3-5 November 2023 (In-person)
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Location: Ernakulam, Kerala, India
Date: 25-27 October 2023 (Hybrid)
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Date: 5-7 October 2023 (In-person)
Location: Kalamassery , Kochi, Kerala
Date: 1-3 October 2023
Location: Kozhikode, Kerala ,India
Date: 18 October 2023
Chapter: North Jersey Chapter
Chapter Chair: Alfredo Tan
Title: Synthetic Aperture Radar (SAR) Signal Processing Challenges and Data Sets for Associated Research
Date: 21 November 2023
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Topic: Differentiable Tools for Digital Twin Networks
Lecture Date: 19 October 2023
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Topic: Reinforcement Learning meets Federated Learning and Distributional Robustness
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Topic: Nonconvex Optimization Meets Low-Rank Matrix Estimation
ME-UYR is under way! We are currently looking for students and mentors who are interested in participating in this mentoring experience. Please find more information in the Call for Student participants and the Call for Mentors.
Date: 12-14 June 2024
Location: Taichung, Taiwan
Inference tasks in signal processing are often characterized by the availability of reliable statistical modeling with some missing instance-specific parameters. One conventional approach uses data to estimate these missing parameters and then infers based on the estimated model. Alternatively, data can also be leveraged to directly learn the inference mapping end to end. These approaches for combining partially known statistical models and data in inference are related to the notions of generative and discriminative models used in the machine learning literature [1] , [2] , typically considered in the context of classifiers.
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