A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers
Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features and attributes. This process is relevant when external constraints limit the performance of an industrial predictive system. Under such circumstances, copying enables the retention of original prediction capabilities while adapting to new demands. Previous research has focused on the single-pass implementation for copying. This paper introduces a novel sequential approach that significantly reduces the amount of computational resources needed to train or maintain a copy, leading to reduced maintenance costs for companies using machine learning models in production. The effectiveness of the sequential approach is demonstrated through experiments with synthetic and real-world datasets, showing significant reductions in time and resources, while maintaining or improving accuracy.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Copying Machine Learning Classifiers
We study model-agnostic copies of machine learning classifiers. We develop the theory behind the problem of copying, highlighting its differences with that of learning, and propose a framework to copy the functionality o…
BIG-bench Machine LearningFairnessOn the Copying Behaviors of Pre-Training for Neural Machine Translation
Previous studies have shown that initializing neural machine translation (NMT) models with the pre-trained language models (LM) can speed up the model training and boost the model performance. In this work, we identify a…
Machine TranslationNMTTranslationSteering LLMs toward Korean Local Speech: Iterative Refinement Framework for Faithful Dialect Translation
Standard-to-dialect machine translation remains challenging due to a persistent dialect gap in large language models and evaluation distortions inherent in n-gram metrics, which favor source copying over authentic dialec…
Machine TranslationIncorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Image captioning often requires a large set of training image-sentence pairs. In practice, however, acquiring sufficient training pairs is always expensive, making the recent captioning models limited in their ability to…
DecoderImage CaptioningObject RecognitionSentenceContext and Copying in Neural Machine Translation
Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as…
Machine TranslationTranslation