Titlebook: Knowledge Science, Engineering and Management; 17th International C Cungeng Cao,Huajun Chen,Yonghao Wang Conference proceedings 2024 The Ed

我要威脅

fastness

Meta-pruning: Learning to?Prune on?Few-Shot Learningtting risks, we propose a new meta-learning method termed Meta-Pruning, which diverges from traditional pruning methods by treating pruning as a learnable task and training the model to discern and select beneficial network connections for new tasks. We propose to set the corresponding learning rate

宴會

syncope

記憶法

DSCVSR: A Lightweight Video Super-Resolution for?Arbitrary Magnificationdeep learning introduces a large number of parameters, which can result in a large resource overhead, the model cannot be deployed on edge devices. Therefore, in this paper, we design a lightweight video super-resolution model, named Depthwise Separable Convolutional Video Super-Resolution (DSCVSR),

Spinous-Process

Programming Knowledge Tracing with?Context and?Structure Integrationassignment tasks. Previous approaches typically focused on either the structural or contextual representation of code to model PKT. However, relying solely on one type of these code representations may fail to capture the subtle differences in the student-submitted code, leading to inferior tracing

玷污

轉(zhuǎn)換

相容

User Story Classification with?Machine Learning and?LLMspted to classify different aspects of user stories in the past. However, classifying the . class has been largely overlooked. To this aim, we present three pipelines. The first two pipelines rely on standard machine learning methods. They differ in how they represent features, i.e. bag-of-word vs. e

裁決