Minimal Sufficient Representations for Self-interpretable Deep Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Tan, Zhiyao, Li, Liu, Lin, Huazhen |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Generative adversarial learning with optimal input dimension and its adaptive generator architecture
by: Tan, Zhiyao, et al.
Published: (2024)
by: Tan, Zhiyao, et al.
Published: (2024)
DeepSuM: Deep Sufficient Modality Learning Framework
by: Gao, Zhe, et al.
Published: (2025)
by: Gao, Zhe, et al.
Published: (2025)
Learning Functional Graphs with Nonlinear Sufficient Dimension Reduction
by: Kim, Kyongwon, et al.
Published: (2026)
by: Kim, Kyongwon, et al.
Published: (2026)
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
by: Kim, Hwanwoo, et al.
Published: (2025)
by: Kim, Hwanwoo, et al.
Published: (2025)
Probabilistic Modelling is Sufficient for Causal Inference
by: Mlodozeniec, Bruno, et al.
Published: (2025)
by: Mlodozeniec, Bruno, et al.
Published: (2025)
Structure Maintained Representation Learning Neural Network for Causal Inference
by: Sun, Yang, et al.
Published: (2025)
by: Sun, Yang, et al.
Published: (2025)
FlowSDR: Sufficient Dimension Reduction via Conditional Normalizing Flows
by: Dong, Yuexiao, et al.
Published: (2026)
by: Dong, Yuexiao, et al.
Published: (2026)
Golden Ratio-Based Sufficient Dimension Reduction
by: Yang, Wenjing, et al.
Published: (2024)
by: Yang, Wenjing, et al.
Published: (2024)
Deep Deterministic Nonlinear ICA via Total Correlation Minimization with Matrix-Based Entropy Functional
by: Li, Qiang, et al.
Published: (2025)
by: Li, Qiang, et al.
Published: (2025)
Enhancing Sufficient Dimension Reduction via Hellinger Correlation
by: Hong, Seungbeom, et al.
Published: (2024)
by: Hong, Seungbeom, et al.
Published: (2024)
The Topology and Geometry of Neural Representations
by: Lin, Baihan, et al.
Published: (2023)
by: Lin, Baihan, et al.
Published: (2023)
Contrastive Network Representation Learning
by: Dong, Zihan, et al.
Published: (2025)
by: Dong, Zihan, et al.
Published: (2025)
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
by: Thompson, Ryan, et al.
Published: (2023)
by: Thompson, Ryan, et al.
Published: (2023)
Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation
by: Behrens, Max, et al.
Published: (2026)
by: Behrens, Max, et al.
Published: (2026)
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data
by: Kim, Sehwan, et al.
Published: (2025)
by: Kim, Sehwan, et al.
Published: (2025)
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
by: Chen, Xuexin, et al.
Published: (2024)
by: Chen, Xuexin, et al.
Published: (2024)
An Empirical Study: Extensive Deep Temporal Point Process
by: Lin, Haitao, et al.
Published: (2021)
by: Lin, Haitao, et al.
Published: (2021)
DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses
by: Kim, Kyum, et al.
Published: (2025)
by: Kim, Kyum, et al.
Published: (2025)
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
by: Lin, Jiahe, et al.
Published: (2025)
by: Lin, Jiahe, et al.
Published: (2025)
Generalized Data Thinning Using Sufficient Statistics
by: Dharamshi, Ameer, et al.
Published: (2023)
by: Dharamshi, Ameer, et al.
Published: (2023)
Representation-Enhanced Neural Knowledge Integration with Application to Large-Scale Medical Ontology Learning
by: Liu, Suqi, et al.
Published: (2024)
by: Liu, Suqi, et al.
Published: (2024)
Newfluence: Boosting Model interpretability and Understanding in High Dimensions
by: Zou, Haolin, et al.
Published: (2025)
by: Zou, Haolin, et al.
Published: (2025)
Bagged Polynomial Regression and Neural Networks
by: Klosin, Sylvia, et al.
Published: (2022)
by: Klosin, Sylvia, et al.
Published: (2022)
Adaptive Physics-Guided Neural Network
by: Shulman, David, et al.
Published: (2024)
by: Shulman, David, et al.
Published: (2024)
Learning Treatment Representations for Downstream Instrumental Variable Regression
by: Lin, Shiangyi, et al.
Published: (2025)
by: Lin, Shiangyi, et al.
Published: (2025)
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
by: Sainsbury-Dale, Matthew, et al.
Published: (2023)
by: Sainsbury-Dale, Matthew, et al.
Published: (2023)
Factor Augmented Tensor-on-Tensor Neural Networks
by: Zhou, Guanhao, et al.
Published: (2024)
by: Zhou, Guanhao, et al.
Published: (2024)
Training of Spiking Neural Networks with Expectation-Propagation
by: Yao, Dan, et al.
Published: (2025)
by: Yao, Dan, et al.
Published: (2025)
Causal Estimation of Exposure Shifts with Neural Networks
by: Tec, Mauricio, et al.
Published: (2023)
by: Tec, Mauricio, et al.
Published: (2023)
Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
by: Li, Teng, et al.
Published: (2026)
by: Li, Teng, et al.
Published: (2026)
An interpretable neural network-based non-proportional odds model for ordinal regression
by: Okuno, Akifumi, et al.
Published: (2023)
by: Okuno, Akifumi, et al.
Published: (2023)
Wahkon: A Statistically Principled Deep RKHS Superposition Network
by: Chen, Yongkai, et al.
Published: (2026)
by: Chen, Yongkai, et al.
Published: (2026)
Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data
by: Wang, Linshanshan, et al.
Published: (2025)
by: Wang, Linshanshan, et al.
Published: (2025)
Calibration Prediction Interval for Non-parametric Regression and Neural Networks
by: Wu, Kejin, et al.
Published: (2025)
by: Wu, Kejin, et al.
Published: (2025)
Doubly Robust Conditional Independence Testing with Generative Neural Networks
by: Zhang, Yi, et al.
Published: (2024)
by: Zhang, Yi, et al.
Published: (2024)
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
by: Richards, Jordan, et al.
Published: (2022)
by: Richards, Jordan, et al.
Published: (2022)
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
by: Rahman, Md Musfiqur, et al.
Published: (2024)
by: Rahman, Md Musfiqur, et al.
Published: (2024)
Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
by: Peddi, Pranay Kumar, et al.
Published: (2025)
by: Peddi, Pranay Kumar, et al.
Published: (2025)
Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks
by: Chi, Chien-Ming
Published: (2025)
by: Chi, Chien-Ming
Published: (2025)
Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
by: Paik, Seunghoon, et al.
Published: (2023)
by: Paik, Seunghoon, et al.
Published: (2023)
Similar Items
-
Generative adversarial learning with optimal input dimension and its adaptive generator architecture
by: Tan, Zhiyao, et al.
Published: (2024) -
DeepSuM: Deep Sufficient Modality Learning Framework
by: Gao, Zhe, et al.
Published: (2025) -
Learning Functional Graphs with Nonlinear Sufficient Dimension Reduction
by: Kim, Kyongwon, et al.
Published: (2026) -
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
by: Kim, Hwanwoo, et al.
Published: (2025) -
Probabilistic Modelling is Sufficient for Causal Inference
by: Mlodozeniec, Bruno, et al.
Published: (2025)