14 citations · 16 across the 5 of their papers we have counts for
7 papers
Learnable Spectral Activations
Tamir Shor, Or Litany, Alex Bronstein
Implicit neural representations (INRs) are shaped by the spectral structure induced by their input encodings and activation functions. Existing methods improve fitting primarily by…
Scalable unsupervised alignment of general metric and non-metric structures
Sanketh Vedula, Valentino Maiorca, Lorenzo Basile +2
Aligning data from different domains is a fundamental problem in machine learning with broad applications across very different areas, most notably aligning experimental readouts i…
Demystifying Graph Sparsification Algorithms in Graph Properties Preservation
Yuhan Chen, Haojie Ye, Sanketh Vedula +4
Graph sparsification is a technique that approximates a given graph by a sparse graph with a subset of vertices and/or edges. The goal of an effective sparsification algorithm is t…
Class-Conditioned Transformation for Enhanced Robust Image Classification
Tsachi Blau, Roy Ganz, Chaim Baskin +2
Robust classification methods predominantly concentrate on algorithms that address a specific threat model, resulting in ineffective defenses against other threat models. Real-worl…
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Tsachi Blau, Roy Ganz, Bahjat Kawar +2
Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks. Following the discovery of this vulnerability in real-world…
Deformable Shape Completion with Graph Convolutional Autoencoders
Or Litany, Alex Bronstein, Michael Bronstein +1
The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a sign…
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling
Gautam Pai, Ronen Talmon, Alex Bronstein +1
This paper explores a fully unsupervised deep learning approach for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds. We…