This work is an update of my previous paper on the same topic published a few years ago
(Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative …

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …

How much does a machine learning algorithm leak about its training data, and why?
Membership inference attacks are used as an auditing tool to quantify this leakage. In this …

This paper demonstrates that language models are strong structure-based protein
designers. We present LM-Design, a generic approach to reprogramming sequence-based …

Good models require good training data. For overparameterized deep models, the causal
relationship between training data and model predictions is increasingly opaque and poorly …

As the deployment of pre-trained language models (PLMs) expands, pressing security
concerns have arisen regarding the potential for malicious extraction of training data, posing …

Membership Inference Attacks (MIA) aim to infer whether a target data record has been
utilized for model training or not. Prior attempts have quantified the privacy risks of language …

The past few years have seen impressive progress in the development of deep generative
models capable of producing high-dimensional, complex, and photo-realistic data. However …

Denoising diffusion probabilistic models (DDPMs) have recently been shown to accurately
generate complicated data such as images, audio, or time series. Experimental and clinical …

In this work, we study the training of diffusion probabilistic models through a series of
hypotheses and carefully designed experiments. We call our key finding the memorization …