4 papers
Spatially Grounded Concept-Based Image Classification
Ran Eisenberg, Amit Rozner, Ethan Fetaya +1
Deep neural networks can achieve high accuracy while relying on evidence that is hard to inspect or misaligned with the intended task. Concept Bottleneck Models (CBMs) expose human…
Learning Permutation from Structure Without Supervision
Ran Eisenberg, Ofir Lindenbaum
Many learning problems require uncovering a hidden ordering that reveals structure in unordered data, such as monotonicity in sorting or spatial continuity in jigsaw reconstruction…
Self Supervised Correlation-based Permutations for Multi-View Clustering
Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum
Combining data from different sources can improve data analysis tasks such as clustering. However, most of the current multi-view clustering methods are limited to specific domains…
Conditional Deep Canonical Time Warping
Afek Steinberg, Ran Eisenberg, Ofir Lindenbaum
Temporal alignment of sequences is a fundamental challenge in many applications, such as computer vision and bioinformatics, where local time shifting needs to be accounted for. Mi…