Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 필터 해제
A survey of dynamic graph neural networks
Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, most research focuses on static graphs, n…
Inductive LearningSurveyCNN2GNN: How to Bridge CNN with GNN
Although the convolutional neural network (CNN) has achieved excellent performance in vision tasks by extracting the intra-sample representation, it will take a higher training expense because of stacking numerous convol…
Graph LearningInductive LearningSolving the Tree Containment Problem Using Graph Neural Networks
Tree Containment is a fundamental problem in phylogenetics useful for verifying a proposed phylogenetic network, representing the evolutionary history of certain species. Tree Containment asks whether the given phylogene…
Graph Neural NetworkInductive LearningVariational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph st…
Graph Representation LearningInductive LearningNode ClassificationRepresentation LearningFlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data. The dataset comprises bone marrow samples from 30 patients, with each cell characterized b…
BenchmarkingInductive LearningMulti-class ClassificationTransductive LearningCan LLMs Compute with Reasons?
Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small…
Inductive LearningLanguage ModelingLanguage ModellingTwo-stage Joint Transductive and Inductive learning for Nuclei Segmentation
AI-assisted nuclei segmentation in histopathological images is a crucial task in the diagnosis and treatment of cancer diseases. It decreases the time required to manually screen microscopic tissue images and can resolve…
Image SegmentationInductive LearningMedical Image SegmentationSegmentation+2Information-Theoretic Generalization Bounds for Transductive Learning and its Applications
In this paper, we establish generalization bounds for transductive learning algorithms in the context of information theory and PAC-Bayes, covering both the random sampling and the random splitting setting. First, we sho…
Generalization BoundsGraph LearningInductive LearningTransductive LearningTransductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few-shot classification. Few-shot classific…
ClassificationInductive LearningTransductive LearningGraph data modelling for outcome prediction in oropharyngeal cancer patients
Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. Since patient data is not readily available as a graph, most existing m…
Computed Tomography (CT)Inductive LearningLOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning
Current high-performance semantic segmentation models are purely data-driven sub-symbolic approaches and blind to the structured nature of the visual world. This is in stark contrast to human cognition which abstracts vi…
Inductive LearningSegmentationSemantic ParsingSemantic SegmentationTowards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm
Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners' cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning s…
cognitive diagnosisDecoderDiagnosticInductive LearningInductive Learning of Declarative Domain-Specific Heuristics for ASP
Domain-specific heuristics are a crucial technique for the efficient solving of problems that are large or computationally hard. Answer Set Programming (ASP) systems support declarative specifications of domain-specific …
Inductive LearningInductive logic programmingModeling infant object perception as program induction
Infants expect physical objects to be rigid and persist through space and time and in spite of occlusion. Developmentists frequently attribute these expectations to a "core system" for object recognition. However, it is …
AttributeInductive LearningObjectObject Recognition+1$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training.…
ClassificationInductive LearningNode Classificationopen-set classification+1Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer
Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable approach to learning to solve CSPs in an end…
Inductive LearningSequential Attention Source Identification Based on Feature Representation
Snapshot observation based source localization has been widely studied due to its accessibility and low cost. However, the interaction of users in existing methods does not be addressed in time-varying infection scenario…
DecoderGraph AttentionInductive LearningObject Topological Character Acquisition by Inductive Learning
Understanding the shape and structure of objects is undoubtedly extremely important for object recognition, but the most common pattern recognition method currently used is machine learning, which often requires a large …
Inductive LearningObjectObject RecognitionHuman-like Few-Shot Learning via Bayesian Reasoning over Natural Language
A core tension in models of concept learning is that the model must carefully balance the tractability of inference against the expressivity of the hypothesis class. Humans, however, can efficiently learn a broad range o…
Few-Shot LearningInductive LearningLanguage ModelingLanguage ModellingInGram: Inductive Knowledge Graph Embedding via Relation Graphs
Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assu…
Entity EmbeddingsGraph EmbeddingInductive knowledge graph completionInductive Learning+5