Recent advancements in large vision-language models (VLMs) have shown promise in addressing multimodal tasks by combining the reasoning capabilities of large language models (LLMs) with visual encoders like ViT. However, despite their strong performance on tasks...

The advent of large language models (LLMs) has ushered in a new era in computational linguistics, significantly extending the frontier beyond traditional natural language processing to encompass a broad spectrum of general tasks. Through their deep understanding and...

When building machine learning (ML) models using preexisting datasets, experts in the field must first familiarize themselves with the data, decipher its structure, and determine which subset to use as features. So much so that a basic barrier, the great range of data...

Computer vision researchers often focus on training powerful encoder networks for self-supervised learning (SSL) methods. These encoders generate image representations, but researchers frequently ignore the predictive part of the model after pretraining despite its...

Recent studies have highlighted the efficacy of Selective State Space Layers, also known as Mamba models, across various domains, such as language and image processing, medical imaging, and data analysis. These models offer linear complexity during training and fast...

Medical artificial intelligence (AI) is rapidly evolving, aiming to harness the vast potential of large language models (LLMs) to revolutionize healthcare delivery. These technological advancements promise to enhance diagnosis accuracy, tailor treatment plans, and...