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Inductive Programming
Inductive programming is a branch of program synthesis that is based on inductive inference where a recursive, declarative program is constructed...
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Rational Closure Extension in SPO-Representable Inductive Inference Operators
The class of inductive inference operators that extend rational closure, as introduced by Lehmann or via Pearl’s system Z, exhibits desirable...
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Approximations of System W Between c-Inference, System Z, and Lexicographic Inference
Inductive inference operators have been introduced to formalize the process of completing a conditional belief base to a full inference relation. In...
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Inductive Lottery Ticket Learning for Graph Neural Networks
Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale...
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Generalizable inductive relation prediction with causal subgraph
Inductive relation prediction is an important learning task for knowledge graph reasoning that aims to infer new facts from existing ones. Previous...
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A survey of inductive knowledge graph completion
Knowledge graph completion (KGC) can enhance the completeness of the knowledge graph (KG). Traditional transductive KGC assumes that all entities and...
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Neighboring relation enhanced inductive knowledge graph link prediction via meta-learning
Inductive link prediction over knowledge graphs(KGs) aims to perform inference over a new graph with unseen entities. In contrast to transductive...
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Relational message passing with mutual information maximization for inductive link prediction
Inductive link prediction (ILP) in knowledge graphs (KG) is gaining significant attention, focusing on predicting missing triples involving unseen...
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Inductive reasoning for significant concept and pattern discovery in cognitive IoT
Recent research on the Internet of Things (IoT) focuses on the insertion of cognition into its system architecture and design, which introduces a new...
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A Sequent Calculus for Generalized Inductive Definitions
Inductive definitions are ubiquitous in mathematics and computer science, and play an important role in knowledge representation. To date, several...
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Inductive Structure Consistent Hashing
Semantic-preserving hashing enhances multimedia retrieval by transferring knowledge from original data to hash codes, preserving both visual and...
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Leap: Inductive Link Prediction via Learnable Topology Augmentation
Link prediction is a crucial task in many downstream applications of graph machine learning. To this end, Graph Neural Network (GNN) is a widely used...
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Computable Relations Mapping with Horn Clauses for Inductive Program Synthesis
Inductive Logic Programming (ILP) systems use logic programming languages like Prolog as computational models. Expressivity of these languages is...
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Learning with Enriched Inductive Biases for Vision-Language Models
Vision-Language Models, pre-trained on large-scale image-text pairs, serve as strong foundation models for transfer learning across a variety of...
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Lightweight image super-resolution reconstruction based on mixed attention and global inductive bias network
Convolutional neural network has significantly advanced the field of image super-resolution reconstruction in recent years. The insufficient ability...
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Reevaluation of Inductive Link Prediction
Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the...
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Spatial entropy as an inductive bias for vision transformers
Recent work on Vision Transformers (VTs) showed that introducing a local inductive bias in the VT architecture helps reducing the number of samples...
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Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants
Essential tasks for the verification of probabilistic programs include bounding expected outcomes and proving termination in finite expected runtime....
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Causal Inference in NARS
Humans engage in causal inference almost every day, however, the term ‘causation’ is still quite ambiguous, and few AI systems provide a...
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Inductive Type-aware Reasoning over Knowledge Graphs
The primary objective of reasoning over Knowledge Graphs (KGs) is to derive novel facts based on existing ones. Inductive reasoning models have...