Papers Transductive Learning
“Transductive Learning” 태그가 달린 논문 135편 · 필터 해제
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction
Accurately predicting drug-target interactions (DTIs) is pivotal for advancing drug discovery and target validation techniques. While machine learning approaches including those that are based on Graph Neural Networks (G…
Drug DiscoveryGraph LearningInductive LearningTransductive LearningFew-shot Novel Category Discovery
The recently proposed Novel Category Discovery (NCD) adapt paradigm of transductive learning hinders its application in more real-world scenarios. In fact, few labeled data in part of new categories can well alleviate th…
ClusteringFew-Shot LearningTransductive LearningTowards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited insight into long-range interactions. Curre…
Graph Representation LearningNode ClassificationRepresentation LearningTransductive LearningAccurate and Scalable Graph Neural Networks via Message Invariance
Message passing-based graph neural networks (GNNs) have achieved great success in many real-world applications. For a sampled mini-batch of target nodes, the message passing process is divided into two parts: message pas…
GPUTransductive LearningGenerate, Transduct, Adapt: Iterative Transduction with VLMs
Transductive zero-shot learning with vision-language models leverages image-image similarities within the dataset to achieve better classification accuracy compared to the inductive setting. However, there is little work…
AttributeTransductive LearningZero-Shot LearningOptimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
We delve into the challenge of semi-supervised node classification on the Contextual Stochastic Block Model (CSBM) dataset. Here, nodes from the two-cluster Stochastic Block Model (SBM) are coupled with feature vectors, …
Node ClassificationregressionStochastic Block ModelTransductive LearningSingle-View Graph Contrastive Learning with Soft Neighborhood Awareness
Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augmentations, the potential for information lo…
Contrastive LearningSemantic SimilaritySemantic Textual SimilarityTransductive LearningA Theory for Compressibility of Graph Transformers for Transductive Learning
Transductive tasks on graphs differ fundamentally from typical supervised machine learning tasks, as the independent and identically distributed (i.i.d.) assumption does not hold among samples. Instead, all train/test/va…
Transductive LearningPredictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse
Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than their heterosexual and cisgender counte…
Transductive LearningUMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models
Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data to adapt to downstream tasks, which could…
Test-time AdaptationTransductive LearningTransductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections
This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned pho…
ManagementSelf-Supervised LearningTransductive LearningGraphRouter: A Graph-based Router for LLM Selections
The rapidly growing number and variety of Large Language Models (LLMs) present significant challenges in efficiently selecting the appropriate LLM for a given query, especially considering the trade-offs between performa…
Transductive LearningVLSI Hypergraph Partitioning with Deep Learning
Partitioning is a known problem in computer science and is critical in chip design workflows, as advancements in this area can significantly influence design quality and efficiency. Deep Learning (DL) techniques, particu…
Deep Learninggraph partitioninghypergraph partitioningTransductive Learningwav2graph: A Framework for Supervised Learning Knowledge Graph from Speech
Knowledge graphs (KGs) enhance the performance of large language models (LLMs) and search engines by providing structured, interconnected data that improves reasoning and context-awareness. However, KGs only focus on tex…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)DecoderKnowledge Graphs+5Transductive Active Learning with Application to Safe Bayesian Optimization
Safe Bayesian optimization (Safe BO) is the task of learning an optimal policy within an unknown environment, while ensuring that safety constraints are not violated. We analyze Safe BO under the lens of a generalization…
Active LearningBayesian OptimizationPredictionReinforcement Learning (RL)+3Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
Graph node classification with few labeled nodes presents significant challenges due to limited supervision. Conventional methods often exploit the graph in a transductive learning manner. They fail to effectively utiliz…
Graph Neural NetworkMultiview LearningNode ClassificationTransductive LearningAnomaly Detection of Tabular Data Using LLMs
Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning. In this paper, we study the problem of using LLMs to detect tabular anomalies and show that pre-trained LL…
Anomaly DetectionLong-Context UnderstandingMathematical ReasoningTransductive LearningBayesian Circular Regression with von Mises Quasi-Processes
The need for regression models to predict circular values arises in many scientific fields. In this work we explore a family of expressive and interpretable distributions over circle-valued random functions related to Ga…
Gaussian ProcessesregressionTransductive LearningThe Benefits and Risks of Transductive Approaches for AI Fairness
Recently, transductive learning methods, which leverage holdout sets during training, have gained popularity for their potential to improve speed, accuracy, and fairness in machine learning models. Despite this, the comp…
FairnessHoldout SetTransductive LearningGraph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks
Graph neural networks (GNNs) have become instrumental in diverse real-world applications, offering powerful graph learning capabilities for tasks such as social networks and medical data analysis. Despite their successes…
Graph LearningInductive LearningTransductive Learning