34 citations · 46 across the 7 of their papers we have counts for
7 papers · 1 filter
PromptEvolver: Prompt Inversion through Evolutionary Optimization in Natural-Language Space
Asaf Buchnick, Aviv Shamsian, Aviv Navon +1
Text-to-image generation has progressed rapidly, but faithfully generating complex scenes requires extensive trial-and-error to find the exact prompt. In the prompt inversion task,…
GradMetaNet: An Equivariant Architecture for Learning on Gradients
Yoav Gelberg, Yam Eitan, Aviv Navon +5
Gradients of neural networks encode valuable information for optimization, editing, and analysis of models. Therefore, practitioners often treat gradients as inputs to task-specifi…
Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization
Aviv Shamsian, Eitan Shaar, Aviv Navon +2
Machine unlearning aims to remove the influence of problematic training data after a model has been trained. The primary challenge in machine unlearning is ensuring that the proces…
Personalized Federated Learning with Gaussian Processes
Idan Achituve, Aviv Shamsian, Aviv Navon +2
Federated learning aims to learn a global model that performs well on client devices with limited cross-client communication. Personalized federated learning (PFL) further extends…
Personalized Federated Learning using Hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya +1
Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models…
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve, Aviv Navon, Yochai Yemini +2
Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially co…