SiG-DML-L1.3
QUANTIFYING EXPLANATION DRIFT IN AUDIO USING POST-HOC EXPLAINABLE CONTINUAL LEARNING
Joan Imbwaga, Manjunath Mulimani, Okko Räsänen, Tampere University, Kenya
Session:
SiG-DML-L1: Explainable and Interpretable Learning Lecture
Track:
SiG-DML - Signal and Data Analytics for Machine Learning
Location:
Chamber Music Hall
Presentation Time:
Thu, 3 Sep, 11:10 - 11:30 Belgium Time (UTC +2)
Presentation
Discussion
Resources
No resources available.
Session SiG-DML-L1
SiG-DML-L1.1: Learnable Multi-Window Anisotropic Denoising via Gated Fusion and Residual Refinement
Karen Eguiazarian (Egiazarian), Vladimir Katkovnik, Tampere University, Finland, Finland
SiG-DML-L1.2: Low Rank properties and architectures in small Image Classification Neural Networks
Mark Sandler, Queen Mary University of London, United Kingdom
SiG-DML-L1.3: QUANTIFYING EXPLANATION DRIFT IN AUDIO USING POST-HOC EXPLAINABLE CONTINUAL LEARNING
Joan Imbwaga, Manjunath Mulimani, Okko Räsänen, Tampere University, Kenya
SiG-DML-L1.4: INNER LOOP INFERENCE FOR PRETRAINED TRANSFORMERS: UNLOCKING LATENT CAPABILITIES WITHOUT TRAINING
Jonathan Lys, Vincent Gripon, Bastien Pasdeloup, Axel Marmoret, IMT Atlantique, France; Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene, Sony Europe, Germany
SiG-DML-L1.5: SVD-BASED TYPICALITY MAPS FOR OUT-OF-DISTRIBUTION DETECTION IN VISION TRANSFORMERS
Aldo Sean Sartor, Leandro de Souza Rosa, Andriy Enttsel, Mauro Mangia, Riccardo Rovatti, University of Bologna, Italy
SiG-DML-L1.6: COMBINING CONVOLUTION AND DELAY LEARNING IN RECURRENT SPIKING NEURAL NETWORKS
Lúcio Folly Sanches Zebendo, Eleonora Cicciarella, Michele Rossi, University of Padova, Brazil