Relational Reasoning
148 papers with code • 1 benchmarks • 12 datasets
The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.
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Latest papers with no code
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering
Earth vision research typically focuses on extracting geospatial object locations and categories but neglects the exploration of relations between objects and comprehensive reasoning.
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents.
Large Language Models can Learn Rules
In the deduction stage, the LLM is then prompted to employ the learned rule library to perform reasoning to answer test questions.
A Novel Neural-symbolic System under Statistical Relational Learning
A key objective in field of artificial intelligence is to develop cognitive models that can exhibit human-like intellectual capabilities.
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs.
Lifted Inference beyond First-Order Logic
We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc.
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models
Specifically, we extract relevant commonsense knowledge in inputs as references to align model behavior with human knowledge.
LightPath: Lightweight and Scalable Path Representation Learning
Next, we propose a relational reasoning framework to enable faster training of more robust sparse path encoders.
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes
The task of inductive link prediction in (discrete) attributed multigraphs infers missing attributed links (relations) between nodes in new test multigraphs.
Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?
In this paper, our aim is to briefly survey and articulate the logical and philosophical foundations of using (first-order) logic to represent (probabilistic) knowledge in a non-technical fashion.