Rapid advancements in artificial intelligence (AI) have sparked growing concerns among
experts, policymakers, and world leaders regarding the potential for increasingly advanced …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …

Diffusion models have achieved great success in a range of tasks, such as image synthesis
and molecule design. As such successes hinge on large-scale training data collected from …

Self-supervised learning in computer vision aims to pre-train an image encoder using a
large amount of unlabeled images or (image, text) pairs. The pre-trained image encoder can …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

Backdoor attacks are a kind of insidious security threat against machine learning models.
After being injected with a backdoor in training, the victim model will produce adversary …

Deep neural networks (DNNs) have progressed rapidly during the past decade and have
been deployed in various real-world applications. Meanwhile, DNN models have been …

Backdoor attacks are a kind of emergent training-time threat to deep neural networks
(DNNs). They can manipulate the output of DNNs and possess high insidiousness. In the …

The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …