Component Classification
1개 벤치마크 · 논문 27편 · 이 태스크의 논문 보기 →
Benchmarks
CDCP
Most implemented
Modeling of spatially embedded networks via regional spatial graph convolutional networks
Principal Component Classification
Ammunition Component Classification Using Deep Learning
Papers
When More Modalities Hurt: Modality Dropout for Heavy-Duty Vehicle Engine Diagnostics
Heavy-duty vehicle diagnostics generate three disconnected data modalities: unstructured multi- lingual service complaints, high-dimensional sensor telemetry with over 80% missing values, and Diagnostic Trouble Codes (DT…
Component ClassificationFrom Argument Components to Graphs: A Multi-Agent Debate with Confidence Gating for Argument Relations
Large Language Models (LLMs) are increasingly assessed and utilized in the field of Argument Mining (AM), thanks to their strong general reasoning capabilities. However, standard training-free models often miss sophistic…
Component ClassificationArgument MiningEnhancing Science Classroom Discourse Analysis through Joint Multi-Task Learning for Reasoning-Component Classification
Analyzing the reasoning patterns of students in science classrooms is critical for understanding knowledge construction mechanism and improving instructional practice to maximize cognitive engagement, yet manual coding o…
Component ClassificationMulti-Task LearningData AugmentationMulti-Agent Dialectical Refinement for Enhanced Argument Classification
Argument Mining (AM) is a foundational technology for automated writing evaluation, yet traditional supervised approaches rely heavily on expensive, domain-specific fine-tuning. While Large Language Models (LLMs) offer a…
Component ClassificationArgument MiningArgument Mining as a Text-to-Text Generation Task
Argument Mining(AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently,…
Component ClassificationRelation ClassificationText GenerationArgument MiningWrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore foc…
Component ClassificationRepresentation LearningData AugmentationCode Generation