Large pre-trained transformers are $\textit {show-stealer} $ in modern-day deep learning,
and it becomes crucial to comprehend the parsimonious patterns that exist within them as …

Large pre-trained transformers have been receiving explosive attention in the past few
years, due to their acculturation for numerous downstream applications via fine-tuning, but …

The ever-increasing large language models (LLMs), though opening a potential path for the
upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards …

This article does not propose any novel algorithm or new hardware for sparsity. Instead, it
aims to serve the" common good" for the increasingly prosperous Sparse Neural Network …

Pre-training followed by fine-tuning is widely adopted among practitioners. The performance
can be improved by “model soups” via exploring various hyperparameter configurations …

Accurately clustering high-dimensional measurements is vital for adequately analyzing
scientific data. Deep learning machinery has remarkably improved clustering capabilities in …

As the demand for sophisticated language models (LMs) continues to grow, the necessity to
deploy them efficiently across federated and edge environments becomes increasingly …

Deep ensembles have emerged as a powerful technique for improving predictive
performance and enhancing model robustness across various applications by leveraging …

Large-scale Transformer models (TM) have demonstrated outstanding performance across
various tasks. However, their considerable parameter size restricts their applicability …

In the era of pre-trained LLMs, understanding their intrinsic sparse patterns becomes
paramount, especially in the context of their scalability and efficiency. Recently, Jaiswal et …