With the continuous growth in the number of parameters of transformer-based pretrained
language models (PLMs), particularly the emergence of large language models (LLMs) with …

Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image
classification. It is a simple residual network that alternates (i) a linear layer in which image …

Transformers have quickly shined in the computer vision world since the emergence of
Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) …

Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Amidst growing concerns over the greenhouse effect, especially its consequential impacts,
establishing effective Carbon Emission Prediction Models (CEPMs) to comprehend and …

Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …

Pruning the parameters of deep neural networks has generated intense interest due to
potential savings in time, memory and energy both during training and at test time. Recent …

Large Language Models (LLMs) have attracted extensive attention due to their remarkable
performance across various tasks. However, the substantial computational and memory …