5 citations · 6 across the 9 of their papers we have counts for
10 papers
Discovering Hidden Gems in Model Repositories
Jonathan Kahana, Eliahu Horwitz, Yedid Hoshen
Public repositories host millions of fine-tuned models, yet community usage remains disproportionately concentrated on a small number of foundation checkpoints. We investigate whet…
We Should Chart an Atlas of All the World's Models
Eliahu Horwitz, Nitzan Kurer, Jonathan Kahana +2
Public model repositories now contain millions of models, yet most models remain undocumented and effectively lost. In this position paper, we advocate for charting the world's mod…
Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights
Jonathan Kahana, Or Nathan, Eliahu Horwitz +1
With the increasing numbers of publicly available models, there are probably pretrained, online models for most tasks users require. However, current model search methods are rudim…
Learning on Model Weights using Tree Experts
Eliahu Horwitz, Bar Cavia, Jonathan Kahana +1
The number of publicly available models is rapidly increasing, yet most remain undocumented. Users looking for suitable models for their tasks must first determine what each model…
Deep Linear Probe Generators for Weight Space Learning
Jonathan Kahana, Eliahu Horwitz, Imri Shuval +1
Weight space learning aims to extract information about a neural network, such as its training dataset or generalization error. Recent approaches learn directly from model weights,…
Dataset Size Recovery from LoRA Weights
Mohammad Salama, Jonathan Kahana, Eliahu Horwitz +1
Model inversion and membership inference attacks aim to reconstruct and verify the data which a model was trained on. However, they are not guaranteed to find all training samples…
Recovering the Pre-Fine-Tuning Weights of Generative Models
Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen
The dominant paradigm in generative modeling consists of two steps: i) pre-training on a large-scale but unsafe dataset, ii) aligning the pre-trained model with human values via fi…
Improving Zero-Shot Models with Label Distribution Priors
Jonathan Kahana, Niv Cohen, Yedid Hoshen
Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible. Supervised machine learning methods provide a highly accurate s…
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
Niv Cohen, Jonathan Kahana, Yedid Hoshen
Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.…
A Contrastive Objective for Learning Disentangled Representations
Jonathan Kahana, Yedid Hoshen
Learning representations of images that are invariant to sensitive or unwanted attributes is important for many tasks including bias removal and cross domain retrieval. Here, our o…