Large datasets have become commonplace in NLP research. However, the increased
emphasis on data quantity has made it challenging to assess the quality of data. We …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

We focus on over-the-air (OTA) Federated Learning (FL), which has been suggested
recently to reduce the communication overhead of FL due to the repeated transmissions of …

We present iterative back-translation, a method for generating increasingly better synthetic
parallel data from monolingual data to train neural machine translation systems. Our …

Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of
large language models (LLMs). However, fine-tuned LLMs often become overconfident …

The computerized detection of Parkinson's disease (PD) will facilitate population screening
and frequent monitoring and provide a more objective measure of symptoms, benefiting both …

There has been an increase in the implementation of Artificial Intelligence (AI) in the dairy
industry for Milk Quality Analysis (MQA). However, traditional Machine Learning (ML) …

Diagnosing melanocytic lesions is one of the most challenging areas of pathology with
extensive intra-and inter-observer variability. The gold standard for a diagnosis of invasive …

Selective laser melting (SLM) is known as one of the most promising metal additive
manufacturing technologies, and how to ensure its consistent quality is still a main …

Ensembling deep learning models is a shortcut to promote its implementation in new
scenarios, which can avoid tuning neural networks, losses and training algorithms from …