Predictive maintenance advocates the use of artificial intelligence to analyze big data and
provides support for monitoring health conditions and planning maintenance activities in …

Deploying large language models (LLMs) of several billion parameters can be impractical in
most industrial use cases due to constraints such as cost, latency limitations, and hardware …

Abstract Knowledge distillation has been widely used to compress a large teacher network
into a smaller student network. Conventional approaches require the training dataset that …

As the demand for efficient and lightweight models in image classification grows, knowledge
distillation has emerged as a promising technique to transfer expertise from complex teacher …

The target-oriented robust optimization (TORO) approach converts the original objectives to
system targets and instead maximizes an uncertainty budget or robustness index. Machine …

Data hallucination generates additional training examples for novel classes to alleviate the
data scarcity problem in few‐shot learning (FSL). Existing hallucination‐based FSL methods …

Deploying large language models (LLMs) of several billion parameters can be impractical in
most industrial use cases due to constraints such as cost, latency limitations, and hardware …