Tue AM1.L4.3
Variance Predictions in VAMP/UAMP with Right Rotationally Invariant Measurement Matrices for niid Generalized Linear Models
Zilu Zhao, Dirk Slock, EURECOM, France
Session:
Tue AM1.L4: Interpretable and Explainable Deep Learning Lecture
Track:
SiG-DML - Signal and Data Analytics for Machine Learning
Location:
Nautica
Presentation Time:
Tue, 5 Sep, 11:40 - 12:00 Finland Time (UTC +3)
Session Chair:
Nikos Deligiannis, Vrije Universiteit Brussel
Presentation
Discussion
Resources
No resources available.
Session Tue AM1.L4
Tue AM1.L4.1: STOCHASTIC UNROLLED PROXIMAL POINT ALGORITHM FOR LINEAR IMAGE INVERSE PROBLEMS
Brandon Le Bon, INRIA Rennes - Bretagne Atlantique, France; Mikaël Le Pendu, INTERDIGITAL, France; Christine Guillemot, INRIA Rennes - Bretagne Atlantique, France
Tue AM1.L4.2: PRIOR FOR MULTI-TASK INVERSE PROBLEMS IN IMAGE RECONSTRUCTION USING DEEP EQUILIBRIUM MODELS
Samuel Willingham, Inria, France; Mårten Sjöström, Mid Sweden University, Sweden; Christine Guillemot, Inria, France
Tue AM1.L4.3: Variance Predictions in VAMP/UAMP with Right Rotationally Invariant Measurement Matrices for niid Generalized Linear Models
Zilu Zhao, Dirk Slock, EURECOM, France
Tue AM1.L4.4: GRAPH-TIME TREND FILTERING AND UNROLLING NETWORK
Mohammad Sabbaqi, Elvin Isufi, Delft University of Technology, Netherlands
Tue AM1.L4.5: Quantitative Evaluation of Video Explainability Methods via Anomaly Localization
Xinyue Zhang, Boris Joukovsky, Nikos Deligiannis, Vrije Universiteit Brussel, Belgium
Tue AM1.L4.6: HARNESSING THE POWER OF EXPLANATIONS FOR INCREMENTAL TRAINING: A LIME-BASED APPROACH
Arnab Neelim Mazumder, University of Maryland Baltimore County, United States; Niall Lyons, Ashutosh Pandey, Avik Santra, Infineon Technologies, United States; Tinoosh Mohsenin, University of Maryland Baltimore County, United States