Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making
even the most complex Machine Learning models available for clients via, eg, a pay-per …

An emerging method to cheaply improve a weaker language model is to finetune it on
outputs from a stronger model, such as a proprietary system like ChatGPT (eg, Alpaca, Self …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Massive developments in mobile wireless telecommunication networks have been made
during the last few decades. At present, mobile users are getting familiar with the latest 5G …

Deep learning models, especially those large-scale and high-performance ones, can be
very costly to train, demanding a considerable amount of data and computational resources …

Training machine learning (ML) models typically involves expensive iterative optimization.
Once the model's final parameters are released, there is currently no mechanism for the …

Obtaining a well-trained model involves expensive data collection and training procedures,
therefore the model is a valuable intellectual property. Recent studies revealed that …

Large language models (LLMs) have witnessed a meteoric rise in popularity among the
general public users over the past few months, facilitating diverse downstream tasks with …

Federated learning models are collaboratively developed upon valuable training data
owned by multiple parties. During the development and deployment of federated models …

Self-supervised models are increasingly prevalent in machine learning (ML) since they
reduce the need for expensively labeled data. Because of their versatility in downstream …