Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
2004.02353
Cited By

Adaptive Explainable Neural Networks (AxNNs)

v1v2 (latest)

Adaptive Explainable Neural Networks (AxNNs)

Social Science Research Network (SSRN), 2020

5 April 2020

Jie Chen

ArXiv (abs)PDF HTML

Papers citing "Adaptive Explainable Neural Networks (AxNNs)"

13 / 13 papers shown

Dual feature-based and example-based explanation methods

Dual feature-based and example-based explanation methods

A. Konstantinov

Boris V. Kozlov

Stanislav R. Kirpichenko

468

0

0

29 Jan 2024

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the
Survival Models

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models

Danila Eremenko

A. Konstantinov

396

0

0

11 Dec 2023

SurvBeX: An explanation method of the machine learning survival models
based on the Beran estimator

SurvBeX: An explanation method of the machine learning survival models based on the Beran estimatorInternational Journal of Data Science and Analysis (IJDSA), 2023

Danila Eremenko

A. Konstantinov

244

5

0

07 Aug 2023

Probabilistic Time Series Forecasts with Autoregressive Transformation
Models

Probabilistic Time Series Forecasts with Autoregressive Transformation Models

Philipp F. M. Baumann

Torsten Hothorn

411

14

0

15 Oct 2021

Attention-like feature explanation for tabular data

Attention-like feature explanation for tabular dataInternational Journal of Data Science and Analysis (JDSA), 2021

A. Konstantinov

349

6

0

10 Aug 2021

SurvNAM: The machine learning survival model explanation

SurvNAM: The machine learning survival model explanationNeural Networks (NN), 2021

Egor D. Satyukov

A. Konstantinov

259

39

0

18 Apr 2021

Linear Iterative Feature Embedding: An Ensemble Framework for
Interpretable Model

Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model

Jie Chen

132

2

0

18 Mar 2021

The Self-Simplifying Machine: Exploiting the Structure of Piecewise
Linear Neural Networks to Create Interpretable Models

The Self-Simplifying Machine: Exploiting the Structure of Piecewise Linear Neural Networks to Create Interpretable Models

156

1

0

02 Dec 2020

Unwrapping The Black Box of Deep ReLU Networks: Interpretability,
Diagnostics, and Simplification

Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification

279

51

0

08 Nov 2020

Interpretable Machine Learning with an Ensemble of Gradient Boosting
Machines

Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines

A. Konstantinov

215

211

0

14 Oct 2020

Neural Mixture Distributional Regression

Neural Mixture Distributional Regression

Florian Pfisterer

223

6

0

14 Oct 2020

Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy
Inference System

Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System

136

12

0

10 Oct 2020

Supervised Machine Learning Techniques: An Overview with Applications to
Banking

Supervised Machine Learning Techniques: An Overview with Applications to BankingInternational Statistical Review (ISR), 2020

Jie Chen

132

29

0

28 Jul 2020

Page 1 of 1