Dropout is one of the most popular regularization methods in the scholarly domain for
preventing a neural network model from overfitting in the training phase. Develo** an …

Large foundation models, including large language models, vision transformers, diffusion,
and large language model based multimodal models, are revolutionizing the entire machine …

Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs
more efficient by removing redundant information in the processed tokens. While different …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

We present a novel framework to overcome the limitations of equivariant architectures in
learning functions with group symmetries. In contrary to equivariant architectures, we use an …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent responses to various prompts or …

Training a linear classifier or lightweight model on top of pretrained vision model outputs so-
called'frozen features' leads to impressive performance on a number of downstream few …

Abstract Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their
deployments on resource-constrained devices remain challenging due to high …

Abstract The emergence of Vision Transformers (ViTs) has marked a significant shift in the
field of computer vision, presenting new methodologies that challenge traditional …