5 papers
Unsupervised Ensemble Learning Through Deep Energy-based Models
Ariel Maymon, Yanir Buznah, Uri Shaham
Unsupervised ensemble learning emerged to address the challenge of combining multiple learners' predictions without access to ground truth labels or additional data. This paradigm…
Provable Speech Attributes Conversion via Latent Independence
Jonathan Svirsky, Ofir Lindenbaum, Uri Shaham
While signal conversion and disentangled representation learning have shown promise for manipulating data attributes across domains such as audio, image, and multimodal generation,…
Learning Shared Representations from Unpaired Data
Amitai Yacobi, Nir Ben-Ari, Ronen Talmon +1
Learning shared representations is a primary area of multimodal representation learning. The current approaches to achieve a shared embedding space rely heavily on paired samples f…
Generalizable Spectral Embedding with an Application to UMAP
Nir Ben-Ari, Amitai Yacobi, Uri Shaham
Spectral Embedding (SE) is a popular method for dimensionality reduction, applicable across diverse domains. Nevertheless, its current implementations face three prominent drawback…
Generalizable and Robust Spectral Method for Multi-view Representation Learning
Amitai Yacobi, Ofir Lindenbaum, Uri Shaham
Multi-view representation learning (MvRL) has garnered substantial attention in recent years, driven by the increasing demand for applications that can effectively process and anal…