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Significance Tests for Neural Networks
v1v2v3 (latest)

Significance Tests for Neural Networks

16 February 2019
Enguerrand Horel
K. Giesecke
ArXiv (abs)PDFHTML

Papers citing "Significance Tests for Neural Networks"

26 / 26 papers shown
The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
A. Arratia
A. Cabaña
Ernesto Mordecki
Gerard Rovira-Parra
71
0
0
15 Sep 2025
AICO: Feature Significance Tests for Supervised Learning
AICO: Feature Significance Tests for Supervised Learning
K. Giesecke
Enguerrand Horel
Chartsiri Jirachotkulthorn
296
0
0
29 Jun 2025
Statistical tuning of artificial neural network
Statistical tuning of artificial neural network
Mohamad Yamen AL Mohamad
Hossein Bevrani
Ali Akbar Haydari
97
0
0
24 Sep 2024
Attribution Methods in Asset Pricing: Do They Account for Risk?
Attribution Methods in Asset Pricing: Do They Account for Risk?
Dangxing Chen
Yuan Gao
FAtt
338
3
0
12 Jul 2024
Full Bayesian Significance Testing for Neural Networks
Full Bayesian Significance Testing for Neural NetworksAAAI Conference on Artificial Intelligence (AAAI), 2024
Zehua Liu
Zimeng Li
Jingyuan Wang
Yue He
BDL
348
13
0
24 Jan 2024
Feedforward neural networks as statistical models: Improving
  interpretability through uncertainty quantification
Feedforward neural networks as statistical models: Improving interpretability through uncertainty quantification
Andrew McInerney
Kevin Burke
AI4CE
160
1
0
14 Nov 2023
Can I Trust the Explanations? Investigating Explainable Machine Learning
  Methods for Monotonic Models
Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models
Dangxing Chen
FAttLRM
149
2
0
23 Sep 2023
Neural Additive Models for Location Scale and Shape: A Framework for
  Interpretable Neural Regression Beyond the Mean
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the MeanInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Anton Thielmann
René-Marcel Kruse
Thomas Kneib
Benjamin Säfken
280
27
0
27 Jan 2023
A Mapping of Assurance Techniques for Learning Enabled Autonomous
  Systems to the Systems Engineering Lifecycle
A Mapping of Assurance Techniques for Learning Enabled Autonomous Systems to the Systems Engineering LifecycleInternational Conference on Applied Algorithms (ICAA), 2022
Christian Ellis
Maggie B. Wigness
L. Fiondella
238
2
0
30 Dec 2022
A Sieve Quasi-likelihood Ratio Test for Neural Networks with
  Applications to Genetic Association Studies
A Sieve Quasi-likelihood Ratio Test for Neural Networks with Applications to Genetic Association Studies
Xiaoxi Shen
Chang Jiang
L. Sakhanenko
Qing Lu
126
4
0
16 Dec 2022
Interpretable Selective Learning in Credit Risk
Interpretable Selective Learning in Credit RiskResearch In International Business and Finance (RIBAF), 2022
Dangxing Chen
Weicheng Ye
Jiahui Ye
FaML
243
20
0
21 Sep 2022
Monotonic Neural Additive Models: Pursuing Regulated Machine Learning
  Models for Credit Scoring
Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit ScoringInternational Conference on AI in Finance (ICAF), 2022
Dangxing Chen
Weicheng Ye
FaML
235
17
0
21 Sep 2022
Consistency of Neural Networks with Regularization
Consistency of Neural Networks with Regularization
Xiaoxi Shen
Jinghang Lin
186
0
0
22 Jun 2022
Deep Partial Least Squares for Empirical Asset Pricing
Deep Partial Least Squares for Empirical Asset PricingSocial Science Research Network (SSRN), 2022
M. Dixon
Nicholas G. Polson
Kemen Goicoechea
158
4
0
20 Jun 2022
An Interpretable Neural Network for Parameter Inference
An Interpretable Neural Network for Parameter Inference
Johann Pfitzinger
276
0
0
10 Jun 2021
Statistical inference for generative adversarial networks and other
  minimax problems
Statistical inference for generative adversarial networks and other minimax problemsScandinavian Journal of Statistics (Scand. J. Stat.), 2021
Mika Meitz
GAN
219
6
0
21 Apr 2021
On the Computational Intelligibility of Boolean Classifiers
On the Computational Intelligibility of Boolean ClassifiersInternational Conference on Principles of Knowledge Representation and Reasoning (KR), 2021
Gilles Audemard
S. Bellart
Louenas Bounia
F. Koriche
Jean-Marie Lagniez
Pierre Marquis
291
70
0
13 Apr 2021
Consistent Feature Selection for Analytic Deep Neural Networks
Consistent Feature Selection for Analytic Deep Neural Networks
Vu C. Dinh
L. Ho
FAtt
628
53
0
16 Oct 2020
Variable Selection via Thompson Sampling
Variable Selection via Thompson Sampling
Yi Liu
Veronika Rockova
386
20
0
01 Jul 2020
Neural Networks and Value at Risk
Neural Networks and Value at RiskSocial Science Research Network (SSRN), 2020
Alexander Arimond
Damian Borth
Andreas G. F. Hoepner
M. Klawunn
S. Weisheit
167
8
0
04 May 2020
Industrial Forecasting with Exponentially Smoothed Recurrent Neural
  Networks
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks
M. Dixon
AI4TS
154
17
0
09 Apr 2020
Fundamental Issues Regarding Uncertainties in Artificial Neural Networks
Fundamental Issues Regarding Uncertainties in Artificial Neural Networks
N. Thacker
C. Twining
P. Tar
S. Notley
V. Ramesh
UQCV
77
1
0
25 Feb 2020
Asymptotic Properties of Neural Network Sieve Estimators
Asymptotic Properties of Neural Network Sieve Estimators
Xiaoxi Shen
Chang Jiang
Lyudamila Sakhanenko
Qing Lu
253
21
0
03 Jun 2019
Computationally Efficient Feature Significance and Importance for
  Machine Learning Models
Computationally Efficient Feature Significance and Importance for Machine Learning Models
Enguerrand Horel
K. Giesecke
FAtt
227
11
0
23 May 2019
Deep Fundamental Factor Models
Deep Fundamental Factor Models
M. Dixon
Nicholas G. Polson
232
7
0
18 Mar 2019
Sensitivity based Neural Networks Explanations
Sensitivity based Neural Networks Explanations
Enguerrand Horel
Virgile Mison
T. Xiong
K. Giesecke
L. Mangu
AAMLXAIFAtt
162
21
0
03 Dec 2018
1
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