Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Widely used alignment techniques, such as reinforcement learning from human feedback
(RLHF), rely on the ability of humans to supervise model behavior-for example, to evaluate …

Most cross-device federated learning (FL) studies focus on the model-homogeneous setting
where the global server model and local client models are identical. However, such …

Vision Transformers convert images to sequences by slicing them into patches. The size of
these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

Abstract Human-Object Interaction (HOI) detection aims to localize human-object pairs and
recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has …

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated
models like OpenAI's ChatGPT, represents a significant advancement in artificial …

Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale
problems in many areas, such DNNs are hard to be deployed in real-world systems due to …

Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …