Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 필터 해제
Inductive detection of Influence Operations via Graph Learning
Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operat…
Graph LearningInductive LearningSpecificityMessage Intercommunication for Inductive Relation Reasoning
Inductive relation reasoning for knowledge graphs, aiming to infer missing links between brand-new entities, has drawn increasing attention. The models developed based on Graph Inductive Learning, called GraIL-based mode…
Inductive LearningKnowledge GraphsRelationInductive Graph Neural Networks for Moving Object Segmentation
Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based method…
Graph Neural NetworkInductive LearningObjectSegmentation+2Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks. Data-driven approaches show great promise in improving CHC solving without the painstaking manual…
Inductive LearningTask-Adaptive Pseudo Labeling for Transductive Meta-Learning
Meta-learning performs adaptation through a limited amount of support set, which may cause a sample bias problem. To solve this problem, transductive meta-learning is getting more and more attention, going beyond the con…
Inductive LearningMeta-LearningBeyond Transformers for Function Learning
The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however it remains unclear whether this is suffi…
Inductive LearningImproving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knwoledge graph (TKG). Most previous TKGC methods only consider predicting the missing links among the entitie…
Few-Shot LearningInductive LearningKnowledge Graph CompletionKnowledge Graphs+3INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aim…
Graph Representation LearningInductive LearningRelationRepresentation LearningPubGraph: A Large-Scale Scientific Knowledge Graph
Research publications are the primary vehicle for sharing scientific progress in the form of new discoveries, methods, techniques, and insights. Unfortunately, the lack of a large-scale, comprehensive, and easy-to-use re…
Community DetectionGraph EmbeddingInductive LearningKnowledge Graph Completion+2Learning Modulo Theories
Recent techniques that integrate \emph{solver layers} into Deep Neural Networks (DNNs) have shown promise in bridging a long-standing gap between inductive learning and symbolic reasoning techniques. In this paper we pre…
Inductive LearningOn-the-Fly Category Discovery
Although machines have surpassed humans on visual recognition problems, they are still limited to providing closed-set answers. Unlike machines, humans can cognize novel categories at the first observation. Novel cat…
DisentanglementInductive LearningNovel Class DiscoveryLearning to Learn Better for Video Object Segmentation
Recently, the joint learning framework (JOINT) integrates matching based transductive reasoning and online inductive learning to achieve accurate and robust semi-supervised video object segmentation (SVOS). However, usin…
Inductive LearningObjectSemantic SegmentationSemi-Supervised Video Object Segmentation+2Distribution Free Prediction Sets for Node Classification
Graph Neural Networks (GNNs) are able to achieve high classification accuracy on many important real world datasets, but provide no rigorous notion of predictive uncertainty. Quantifying the confidence of GNN models is d…
ClassificationConformal PredictionInductive LearningNode Classification+1Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the trai…
Inductive LearningKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving model design has been rapid in research,…
Inductive LearningRecommendation SystemsTransductive LearningWhat do Large Language Models Learn beyond Language?
Large language models (LMs) have rapidly become a mainstay in Natural Language Processing. These models are known to acquire rich linguistic knowledge from training on large amounts of text. In this paper, we investigate…
Inductive LearningGPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data. However, these models usually have at least one of four fundamental limitations: ov…
Graph Neural NetworkInductive LearningFacing Changes: Continual Entity Alignment for Growing Knowledge Graphs
Entity alignment is a basic and vital technique in knowledge graph (KG) integration. Over the years, research on entity alignment has resided on the assumption that KGs are static, which neglects the nature of growth of …
Entity AlignmentInductive LearningKnowledge GraphsVideo Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network
Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce phys…
Inductive LearningMedical Image ClassificationFOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank
FOLD-R++ is a new inductive learning algorithm for binary classification tasks. It generates an (explainable) normal logic program for mixed type (numerical and categorical) data. We present a customized FOLD-R++ algorit…
Binary ClassificationInductive LearningLearning-To-RankVocal Bursts Type Prediction