4 papers · 1 filter
Stress Testing Unlearning Algorithms
Noam Diamant, Neta Glazer, Ethan Fetaya
Recently, machine unlearning, the removal of specific training data influence from a model, has gained increasing attention. In large language models (LLMs), unlearning is particul…
Diverse Sampling in Diffusion Models with Divergence-Free Particle Guidance
Gal Vinograd, Idan Achituve, Ethan Fetaya
Modern generative models can produce high-quality samples, but independent samples under the same condition often yields highly similar outputs. Particle-based methods increase div…
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,…
Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?
Guy Heller, Ethan Fetaya
Bayesian learning via Stochastic Gradient Langevin Dynamics (SGLD) has been suggested for differentially private learning. While previous research provides differential privacy bou…