Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 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 LearningMind The Gap: Deep Learning Doesn't Learn Deeply
This paper aims to understand how neural networks learn algorithmic reasoning by addressing two questions: How faithful are learned algorithms when they are effective, and why do neural networks fail to learn effective a…
Deep LearningInductive LearningDual-channel Heterophilic Message Passing for Graph Fraud Detection
Fraudulent activities have significantly increased across various domains, such as e-commerce, online review platforms, and social networks, making fraud detection a critical task. Spatial Graph Neural Networks (GNNs) ha…
Fraud DetectionInductive BiasInductive LearningStandard Neural Computation Alone Is Insufficient for Logical Intelligence
Neural networks, as currently designed, fall short of achieving true logical intelligence. Modern AI models rely on standard neural computation-inner-product-based transformations and nonlinear activations-to approximate…
Inductive LearningLogical ReasoningInductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback
The increasing level of autonomy of robots poses challenges of trust and social acceptance, especially in human-robot interaction scenarios. This requires an interpretable implementation of robotic cognitive capabilities…
Inductive LearningInductive logic programmingOnline inductive learning from answer sets for efficient reinforcement learning exploration
This paper presents a novel approach combining inductive logic programming with reinforcement learning to improve training performance and explainability. We exploit inductive learning of answer set programs from noisy e…
Inductive LearningInductive logic programmingQ-Learningreinforcement-learning+1Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning
In dynamic decision-making scenarios across business and healthcare, leveraging sample trajectories from diverse populations can significantly enhance reinforcement learning (RL) performance for specific target populatio…
Decision MakingInductive LearningQ-Learningreinforcement-learning+3Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
Despite impressive capability in learning over graph-structured data, graph neural networks (GNN) suffer from adversarial topology perturbation in both training and inference phases. While adversarial training has demons…
image-classificationImage ClassificationInductive LearningGraph Neural Networks for modelling breast biomechanical compression
Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise a…
Computational EfficiencyImage RegistrationInductive LearningImproving Fuzzy Rule Classifier with Brain Storm Optimization and Rule Modification
The expanding complexity and dimensionality in the search space can adversely affect inductive learning in fuzzy rule classifiers, thus impacting the scalability and accuracy of fuzzy systems. This research specifically …
ClassificationInductive LearningA Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation
The goal of generalized few-shot semantic segmentation (GFSS) is to recognize novel-class objects through training with a few annotated examples and the base-class model that learned the knowledge about the base classes.…
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationInductive LearningSegmentation+1High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics
We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts. Our approach constructs temporal bidi…
Autonomous DrivingInductive LearningSub-graph Based Diffusion Model for Link Prediction
Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood. These models work by traversing a forw…
DenoisingInductive LearningLink PredictionPredictionInductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis
This work presents a novel systematic methodology to analyse the capabilities and limitations of Large Language Models (LLMs) with feedback from a formal inference engine, on logic theory induction. The analysis is compl…
Inductive LearningInductive logic programmingFast-and-Frugal Text-Graph Transformers are Effective Link Predictors
We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-attributed knowledge graphs. We demonstrate…
Inductive LearningInductive Link PredictionKnowledge GraphsLink PredictionUnsupervised Graph Representation Learning with Inductive Shallow Node Embedding
Network science has witnessed a surge in popularity, driven by the transformative power of node representation learning for diverse applications like social network analysis and biological modeling. While shallow embeddi…
Graph Representation LearningInductive LearningNode ClassificationRepresentation 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 LearningEnhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
This study aims to overcome the limitations of conventional deep-learning approaches based on convolutional neural networks in complex geometries and unstructured meshes by exploring the potential of Graph U-Nets for uns…
Inductive LearningPredictionSpatio-Temporal ForecastingUnlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features. Although a few empirical studies have yielded evidence regarding…
Inductive LearningFLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
From a telecommunication standpoint, the surge in users and services challenges next-generation networks with escalating traffic demands and limited resources. Accurate traffic prediction can offer network operators valu…
Inductive LearningSpatio-Temporal ForecastingTraffic PredictionTransfer Learning