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Transductive Learning
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In this setting, both a labeled training sample and an (unlabeled) test sample are provided at training time. The goal is to predict only the labels of the given test instances as accurately as possible.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Few-shot Novel Category Discovery
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
Accurate and Scalable Graph Neural Networks via Message Invariance
Generate, Transduct, Adapt: Iterative Transduction with VLMs
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
Single-View Graph Contrastive Learning with Soft Neighborhood Awareness
A Theory for Compressibility of Graph Transformers for Transductive Learning
Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections
GraphRouter: A Graph-based Router for LLM Selections
VLSI Hypergraph Partitioning with Deep Learning
wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech
Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
Anomaly Detection of Tabular Data Using LLMs
Bayesian Circular Regression with von Mises Quasi-Processes
The Benefits and Risks of Transductive Approaches for AI Fairness
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks
Boosting Vision-Language Models with Transduction
Is Transductive Learning Equivalent to PAC Learning?
Label Propagation for Zero-shot Classification with Vision-Language Models
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event Detection
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
Hyperdimensional Representation Learning for Node Classification and Link Prediction
Transductive Learning Is Compact
Low-Rank Graph Contrastive Learning for Node Classification
Active Few-Shot Fine-Tuning
Transductive Active Learning: Theory and Applications
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach
Regularization and Optimal Multiclass Learning
$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.