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Enhancing Explainability of Neural Networks through Architecture
  Constraints
v1v2 (latest)

Enhancing Explainability of Neural Networks through Architecture Constraints

12 January 2019
Zebin Yang
Aijun Zhang
Agus Sudjianto
    AAML
ArXiv (abs)PDFHTML

Papers citing "Enhancing Explainability of Neural Networks through Architecture Constraints"

14 / 14 papers shown
Title
Quantum Deep Hedging
Quantum Deep Hedging
El Amine Cherrat
S. Raj
Iordanis Kerenidis
Abhishek Shekhar
Ben Wood
...
Pierre Minssen
Ruslan Shaydulin
Yue Sun
Romina Yalovetzky
Marco Pistoia
78
26
0
29 Mar 2023
On marginal feature attributions of tree-based models
On marginal feature attributions of tree-based models
Khashayar Filom
A. Miroshnikov
Konstandinos Kotsiopoulos
Arjun Ravi Kannan
FAtt
57
3
0
16 Feb 2023
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in
  Interpretable Machine Learning
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
Danial Dervovic
Nicolas Marchesotti
Freddy Lecue
Daniele Magazzeni
62
0
0
11 Nov 2022
Posterior Regularized Bayesian Neural Network Incorporating Soft and
  Hard Knowledge Constraints
Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints
Jiayu Huang
Yutian Pang
Yongming Liu
Hao Yan
BDLUQCV
66
15
0
16 Oct 2022
Monotonic Neural Additive Models: Pursuing Regulated Machine Learning
  Models for Credit Scoring
Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring
Dangxing Chen
Weicheng Ye
FaML
65
14
0
21 Sep 2022
GAM(e) changer or not? An evaluation of interpretable machine learning
  models based on additive model constraints
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
Patrick Zschech
Sven Weinzierl
Nico Hambauer
Sandra Zilker
Mathias Kraus
143
14
0
19 Apr 2022
Designing Inherently Interpretable Machine Learning Models
Designing Inherently Interpretable Machine Learning Models
Agus Sudjianto
Aijun Zhang
FaML
53
31
0
02 Nov 2021
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring
Dandan Zhang
Yu Zheng
Qiang Li
Lei Wei
Dongsheng Zhang
Zhengyou Zhang
55
31
0
16 May 2021
Bias, Fairness, and Accountability with AI and ML Algorithms
Bias, Fairness, and Accountability with AI and ML Algorithms
Neng-Zhi Zhou
Zach Zhang
V. Nair
Harsh Singhal
Jie Chen
Agus Sudjianto
FaML
121
9
0
13 May 2021
Generalized Constraints as A New Mathematical Problem in Artificial
  Intelligence: A Review and Perspective
Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective
Bao-Gang Hu
Hanbing Qu
AI4CE
105
1
0
12 Nov 2020
A Systematic Literature Review on the Use of Deep Learning in Software
  Engineering Research
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
Cody Watson
Nathan Cooper
David Nader-Palacio
Kevin Moran
Denys Poshyvanyk
84
116
0
14 Sep 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive
  Models with Structured Interactions
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions
Zebin Yang
Aijun Zhang
Agus Sudjianto
FAtt
170
130
0
16 Mar 2020
Interpretation and Simplification of Deep Forest
Sangwon Kim
Mira Jeong
ByoungChul Ko
FAtt
89
8
0
14 Jan 2020
A Survey of Binary Code Similarity
A Survey of Binary Code Similarity
I. Haq
Juan Caballero
50
138
0
25 Sep 2019
1