Abstract Large Language Models (LLMs) have achieved unprecedented breakthroughs in
various natural language processing domains. However, the enigmatic``black-box''nature of …

Classification models categorize objects into given classes, guided by training samples with
input features and labels. In practice, however, labels can be corrupted by human error or …

Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised
models due to its effect in hurting the generalization performance of deep neural networks …

Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios
where collected training data can contain incorrect or corrupted labels. Most existing …

Commonsense reasoning based on knowledge graphs (KGs) is a challenging task that
requires predicting complex questions over the described textual contexts and relevant …

The expanding model size and computation of deep neural networks (DNNs) have
increased the demand for efficient model deployment methods. Quantization-aware training …

The creation of large-scale datasets annotated by humans inevitably introduces noisy
labels, leading to reduced generalization in deep-learning models. Sample selection-based …