No. 3: Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches
All Papers
CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot
PDFTowards High-Level Probabilistic Reasoning with Lifted Inference
PDFCommonsense Reasoning Based on Betweenness and Direction in Distributional Models
PDFDimensionality Reduction via Program Induction
PDFLearning Probabilistic Logic Models with Human Advice
PDFFoxPSL: An Extended and Scalable PSL Implementation
PDFTowards Learning a Knowledge Base of Actions from Experiential Microblogs
PDFA Unified Semantic Embedding: Relating Taxonomies and Attributes
PDFProbabilistic Region Connection Calculus
PDFCombining Vector Space Embeddings with Symbolic Logical Inference over Open-Domain Text
PDFDistributional-Relational Models: Scalable Semantics for Databases
PDFNeural Relational Learning Through Semi-Propositionalization of Bottom Clauses
PDFDistributional Semantic Features as Semantic Primitives — Or Not
PDFLatent Predicate Networks: Concept Learning with Probabilistic Context-Sensitive Grammars
PDFEnriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems
PDFTowards Extracting Faithful and Descriptive Representations of Latent Variable Models
PDFCompositional Vector Space Models for Knowledge Base Inference
PDFTowards Ontologies in Variation
PDFTowards A Model Theory for Distributed Representations
PDFNeural-Symbolic Learning and Reasoning: Contributions and Challenges
PDFProbability Distributions over Structured Spaces
PDFLearning Distributed Word Representations for Natural Logic Reasoning
PDFOn Approximate Reasoning Capabilities of Low-Rank Vector Spaces
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