Cover Image

Visual Perceptual to Conceptual First-Order Rule Learning Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gao, Kun, Soldà, Davide, Eiter, Thomas, Inoue, Katsumi
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentiable Rule Induction from Raw Sequence Inputs
by: Gao, Kun, et al.
Published: (2026)

Variable Assignment Invariant Neural Networks for Learning Logic Programs
by: Phua, Yin Jun, et al.
Published: (2024)

Neural Decision-Propagation for Answer Set Programming
by: Eiter, Thomas, et al.
Published: (2026)

Neural Logic Networks for Interpretable Classification
by: Perreault, Vincent, et al.
Published: (2025)

Can Transformers Learn to Verify During Backtracking Search?
by: Phua, Yin Jun, et al.
Published: (2026)

LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification
by: Andrews, Thomas, et al.
Published: (2025)

Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures
by: Hersche, Michael, et al.
Published: (2024)

Disentangling Neural Disjunctive Normal Form Models
by: Baugh, Kexin Gu, et al.
Published: (2025)

CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
by: Buffelli, Davide, et al.
Published: (2024)

Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
by: Konstantinov, Andrei V., et al.
Published: (2024)

Conceptual Belief-Informed Reinforcement Learning
by: Gu, Xingrui, et al.
Published: (2024)

Creativity of Deep Learning: Conceptualization and Assessment
by: Basalla, Marcus, et al.
Published: (2020)

Simultaneous Training of First- and Second-Order Optimizers in Population-Based Reinforcement Learning
by: Pfeiffer, Felix, et al.
Published: (2024)

Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning
by: Jin, Yu, et al.
Published: (2025)

Dimer-Enhanced Optimization: A First-Order Approach to Escaping Saddle Points in Neural Network Training
by: Hu, Yue, et al.
Published: (2025)

Understanding Expressivity of GNN in Rule Learning
by: Qiu, Haiquan, et al.
Published: (2023)

Refining Adaptive Zeroth-Order Optimization at Ease
by: Shu, Yao, et al.
Published: (2025)

Interpretable Representation Learning for Additive Rule Ensembles
by: Behzadimanesh, Shahrzad, et al.
Published: (2025)

Fuzzy Rule-based Differentiable Representation Learning
by: Zhang, Wei, et al.
Published: (2025)

OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations
by: Shu, Yao, et al.
Published: (2024)

Learning Concepts Definable in First-Order Logic with Counting
by: van Bergerem, Steffen
Published: (2019)

Asymptotically Stable Quaternion-valued Hopfield-structured Neural Network with Periodic Projection-based Supervised Learning Rules
by: Wang, Tianwei, et al.
Published: (2025)

Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
by: Yang, Zhengxin, et al.
Published: (2024)

Sign-Symmetry Learning Rules are Robust Fine-Tuners
by: Berriche, Aymene, et al.
Published: (2025)

Learning Interpretable Rules for Scalable Data Representation and Classification
by: Wang, Zhuo, et al.
Published: (2023)

Spectral Higher-Order Neural Networks
by: Peri, Gianluca, et al.
Published: (2026)

Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure
by: Di Gioia, Davide
Published: (2026)

Rule by Rule: Learning with Confidence through Vocabulary Expansion
by: Nössig, Albert, et al.
Published: (2024)

Budgeted Broadcast: An Activity-Dependent Pruning Rule for Neural Network Efficiency
by: Meirovitch, Yaron, et al.
Published: (2025)

Interpretable Classification via a Rule Network with Selective Logical Operators
by: Wei, Bowen, et al.
Published: (2024)

Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks
by: Isaac, Ebenezer R. H. P., et al.
Published: (2025)

Beyond First-Order: Training LLMs with Stochastic Conjugate Subgradients and AdamW
by: Zhang, Di, et al.
Published: (2025)

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
by: Bigelow, Eric, et al.
Published: (2026)

Spectral Geometry for Deep Learning: Compression and Hallucination Detection via Random Matrix Theory
by: Ettori, Davide
Published: (2026)

Multitask Kernel-based Learning with First-Order Logic Constraints
by: Diligenti, Michelangelo, et al.
Published: (2023)

Learning Semantic Association Rules from Internet of Things Data
by: Karabulut, Erkan, et al.
Published: (2024)

Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
by: Daniele, Alessandro, et al.
Published: (2022)

Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
by: Liu, Tianyu, et al.
Published: (2024)

AE SemRL: Learning Semantic Association Rules with Autoencoders
by: Karabulut, Erkan, et al.
Published: (2024)

CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
by: Zha, Yaohua, et al.
Published: (2026)