Cover Image

Compositional Function Networks: A High-Performance Alternative to Deep Neural Networks with Built-in Interpretability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Li, Fang
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Structural Compositional Function Networks: Interpretable Functional Compositions for Tabular Discovery
by: Li, Fang
Published: (2026)

FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series
by: Su, Qiqi, et al.
Published: (2023)

Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey
by: Khan, Arham, et al.
Published: (2024)

Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning
by: Fang, Ronglong, et al.
Published: (2024)

A Neural Network Alternative to Tree-based Models
by: Raieli, Salvatore, et al.
Published: (2024)

Factor Graph-based Interpretable Neural Networks
by: Li, Yicong, et al.
Published: (2025)

Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024)

A Comprehensive Survey on Self-Interpretable Neural Networks
by: Ji, Yang, et al.
Published: (2025)

Interpretable Neural Networks with Random Constructive Algorithm
by: Nan, Jing, et al.
Published: (2023)

Interpreting Neural Networks through Mahalanobis Distance
by: Oursland, Alan
Published: (2024)

Training Neural Networks for Modularity aids Interpretability
by: Golechha, Satvik, et al.
Published: (2024)

Interpretable Graph Neural Networks for Tabular Data
by: Alkhatib, Amr, et al.
Published: (2023)

The Interpretable and Effective Graph Neural Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)

Discovering the Representation Bottleneck of Graph Neural Networks
by: Wu, Fang, et al.
Published: (2022)

Certifying Global Robustness for Deep Neural Networks
by: Li, You, et al.
Published: (2024)

SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
by: Fang, Lanting, et al.
Published: (2024)

A Theoretical Analysis of Compositional Generalization in Neural Networks: A Necessary and Sufficient Condition
by: Li, Yuanpeng
Published: (2025)

Proximity-Informed Calibration for Deep Neural Networks
by: Xiong, Miao, et al.
Published: (2023)

Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
by: Li, Xin-Chun, et al.
Published: (2024)

Wormhole Dynamics in Deep Neural Networks
by: Lai, Yen-Lung, et al.
Published: (2025)

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
by: Montazerin, Mansooreh, et al.
Published: (2025)

Relational Composition in Neural Networks: A Survey and Call to Action
by: Wattenberg, Martin, et al.
Published: (2024)

Efficient and Interpretable Neural Networks Using Complex Lehmer Transform
by: Ataei, Masoud, et al.
Published: (2025)

Attention Consistency Regularization for Interpretable Early-Exit Neural Networks
by: Zhao, Yanhua
Published: (2026)

Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients
by: Wetzel, Sebastian Johann
Published: (2024)

A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
by: Li, Pang, et al.
Published: (2024)

Low-bit Model Quantization for Deep Neural Networks: A Survey
by: Liu, Kai, et al.
Published: (2025)

Deep Neural Networks via Complex Network Theory: a Perspective
by: La Malfa, Emanuele, et al.
Published: (2024)

A Functional Perspective on Knowledge Distillation in Neural Networks
by: Mason-Williams, Israel, et al.
Published: (2025)

B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
by: Arya, Shreyash, et al.
Published: (2024)

Deep Neural Network Benchmarks for Selective Classification
by: Pugnana, Andrea, et al.
Published: (2024)

Meta-Learning Loss Functions for Deep Neural Networks
by: Raymond, Christian
Published: (2024)

Anomaly Detection Based on Critical Paths for Deep Neural Networks
by: Zhao, Fangzhen, et al.
Published: (2025)

Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
by: Lukyanov, Kirill, et al.
Published: (2025)

GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
by: Ranasinghe, Nisal, et al.
Published: (2024)

Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
by: Wetzel, Sebastian J., et al.
Published: (2024)

Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
by: Deng, Huiqi, et al.
Published: (2025)

Learning Dynamics of Deep Learning -- Force Analysis of Deep Neural Networks
by: Ren, Yi
Published: (2025)

Quantitative CLTs in Deep Neural Networks
by: Favaro, Stefano, et al.
Published: (2023)

Dispelling the Curse of Singularities in Neural Network Optimizations
by: Cao, Hengjie, et al.
Published: (2026)