21 citations · 56 across the 25 of their papers we have counts for
12 papers · 1 filter
A Dataset is Worth 1 MB
Elad Kimchi Shoshani, Leeyam Gabay, Yedid Hoshen
A dataset server must often distribute the same large payload to many clients, incurring massive communication costs. Since clients frequently operate on diverse hardware and softw…
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,…
From Zero to Hero: Cold-Start Anomaly Detection
Tal Reiss, George Kour, Naama Zwerdling +2
When first deploying an anomaly detection system, e.g., to detect out-of-scope queries in chatbots, there are no observed data, making data-driven approaches ineffective. Zero-shot…
Unsupervised Model Tree Heritage Recovery
Eliahu Horwitz, Asaf Shul, Yedid Hoshen
The number of models shared online has recently skyrocketed, with over one million public models available on Hugging Face. Sharing models allows other users to build on existing m…
Classifying Nodes in Graphs without GNNs
Daniel Winter, Niv Cohen, Yedid Hoshen
Graph neural networks (GNNs) are the dominant paradigm for classifying nodes in a graph, but they have several undesirable attributes stemming from their message passing architectu…
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…
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge
Yedid Hoshen
In this perspective paper, we argue that the dominant paradigm in anomaly detection cannot scale indefinitely and will eventually hit fundamental limits. This is due to the a no fr…
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
Tal Reiss, Niv Cohen, Yedid Hoshen
Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anom…