2 citations · 2 across the 2 of their papers we have counts for
5 papers
Learning Eigenstructures of Unstructured Data Manifolds
Roy Velich, Arkadi Piven, David Bensaïd +3
We introduce a novel framework that directly learns a spectral basis for shape and manifold analysis from unstructured data, eliminating the need for traditional operator selection…
SingLoRA: Low Rank Adaptation Using a Single Matrix
David Bensaïd, Noam Rotstein, Roy Velich +2
Low-Rank Adaptation (LoRA) has significantly advanced parameter-efficient fine-tuning of large pretrained models. LoRA augments the pre-trained weights of a model by adding the pro…
Neural Descriptors: Self-Supervised Learning of Robust Local Surface Descriptors Using Polynomial Patches
Gal Yona, Roy Velich, Ron Kimmel +1
Classical shape descriptors such as Heat Kernel Signature (HKS), Wave Kernel Signature (WKS), and Signature of Histograms of OrienTations (SHOT), while widely used in shape analysi…
Pathways on the Image Manifold: Image Editing via Video Generation
Noam Rotstein, Gal Yona, Daniel Silver +3
Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow…
Deep Signatures -- Learning Invariants of Planar Curves
Roy Velich, Ron Kimmel
We propose a learning paradigm for numerical approximation of differential invariants of planar curves. Deep neural-networks' (DNNs) universal approximation properties are utilized…