Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 필터 해제
Developing hierarchical anticipations via neural network-based event segmentation
Humans can make predictions on various time scales and hierarchical levels. Thereby, the learning of event encodings seems to play a crucial role. In this work we model the development of hierarchical predictions via aut…
Event SegmentationInductive LearningCLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
Supervised learning has traditionally focused on inductive learning by observing labeled examples of a task. In contrast, humans have the ability to learn new concepts from language. Here, we explore training zero-shot c…
Inductive LearningThe worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
Recent arguments that machine learning (ML) is facing a reproducibility and replication crisis suggest that some published claims in ML research cannot be taken at face value. These concerns inspire analogies to the repl…
Causal InferenceInductive LearningMulti-modal Graph Learning for Disease Prediction
Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performance in various biomedical applications. …
Disease PredictionGraph LearningGraph Representation LearningInductive Learning+2PathSAGE: Spatial Graph Attention Neural Networks With Random Path Sampling
Graph Convolutional Networks (GCNs) achieve great success in non-Euclidean structure data processing recently. In existing studies, deeper layers are used in CCNs to extract deeper features of Euclidean structure data. H…
Graph AttentionInductive LearningSparsity-aware neural user behavior modeling in online interaction platforms
Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven u…
Inductive LearningRepresentation LearningTransductive LearningFOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classific…
Inductive LearningSparse Structure Learning via Graph Neural Networks for Inductive Document Classification
Recently, graph neural networks (GNNs) have been widely used for document classification. However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three…
ClassificationDocument ClassificationInductive LearningSentenceHGATE: Heterogeneous Graph Attention Auto-Encoders
Graph auto-encoder is considered a framework for unsupervised learning on graph-structured data by representing graphs in a low dimensional space. It has been proved very powerful for graph analytics. In the real world, …
Graph AttentionGraph Representation LearningInductive LearningLink Prediction+3FOLD-R++: A Toolset for Automated Inductive Learning of Default Theories from Mixed Data
FOLD-R is an automated inductive learning algorithm for learning default rules with exceptions for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for classific…
feature selectionInductive LearningFOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data
FOLD-R is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for classification tasks. We …
feature selectionInductive LearningTowards Open-World Feature Extrapolation: An Inductive Graph Learning Approach
We target open-world feature extrapolation problem where the feature space of input data goes through expansion and a model trained on partially observed features needs to handle new features in test data without further…
Graph LearningGraph Neural NetworkInductive LearningInductive learning for product assortment graph completion
Global retailers have assortments that contain hundreds of thousands of products that can be linked by several types of relationships like style compatibility, "bought together", "watched together", etc. Graphs are a nat…
Inductive LearningHow Neural Processes Improve Graph Link Prediction
Link prediction is a fundamental problem in graph data analysis. While most of the literature focuses on transductive link prediction that requires all the graph nodes and majority of links in training, inductive link pr…
Inductive LearningInductive Link PredictionLink PredictionMeta-Learning+1Rethinking Temperature in Graph Contrastive Learning
Due to not relying on the rare human-labeled information, self-supervised learning, especially contrastive learning, attracted much attention from researchers. It has begun to show its strong advantages on both IID data …
Contrastive LearningInductive LearningSelf-Supervised LearningA Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories
We present a clustering- and demotion-based algorithm called Kmeans-FOLD to induce nonmonotonic logic programs from positive and negative examples. Our algorithm improves upon-and is inspired by-the FOLD algorithm. The F…
ClusteringInductive LearningInductive logic programmingAdaptive Neural Message Passing for Inductive Learning on Hypergraphs
Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes and are not designed for encoding the high…
Inductive LearningTransductive Learning for Unsupervised Text Style Transfer
Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to…
DecoderInductive LearningRetrievalStyle Transfer+3PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery
Correctly capturing intraoperative brain shift in image-guided neurosurgical procedures is a critical task for aligning preoperative data with intraoperative geometry for ensuring accurate surgical navigation. While the …
Graph Neural NetworkInductive LearningPower to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspondence of power flow to message-passing i…
Inductive BiasInductive LearningState Estimation