This paper presents a study on improving Weak-to-Strong Generalization (W2SG) under the framework of superalignment, a concept that ensures high-level AI systems remain consistent with human values when dealing with…
Continue readingThe article discusses the effectiveness of Parameter-Efficient Fine-Tuning (PEFT) in speech processing, with a particular focus on the optimal placement of PEFT methods, their merging strategies, and the use of…
Continue readingThis study focuses on Parameter-Efficient Fine-Tuning (PEFT) in speech processing, exploring optimal placement and merging strategies, as well as the use of ensemble learning. The results reveal that Differentiable Architecture…
Continue readingThe article discusses the use of Parameter-Efficient Fine-Tuning (PEFT) in speech processing. The authors conducted experiments to compare different PEFT methods and their layer-wise placement using Differentiable Architecture Search (DARTS)….
Continue readingThe article presents a study on the issue of model collapse in peer collaborative learning (PCL). The authors analyze the high homogenization between the student and teacher as the cause…
Continue readingThis study presents a new ensemble approach for feature selection in time series forecasting, particularly in non-stationary and limited sample scenarios. The approach utilizes a hierarchical structure to exploit feature…
Continue readingThe article discusses the development of a novel pipeline for automatic weapon detection using an ensemble of convolutional neural networks. This pipeline was created in response to the increasing prevalence…
Continue readingThis paper explores the application of Federated Learning (FL) to object detection as a method to enhance generalizability and compares its performance against a centralized training approach for an object…
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