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Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic
  Corrections

Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections

21 February 2018
Xin Zhang
Armando Solar-Lezama
Rishabh Singh
    FAtt
ArXivPDFHTML

Papers citing "Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections"

15 / 15 papers shown
Title
VeriX: Towards Verified Explainability of Deep Neural Networks
VeriX: Towards Verified Explainability of Deep Neural Networks
Min Wu
Haoze Wu
Clark W. Barrett
AAML
42
10
0
02 Dec 2022
Cardinality-Minimal Explanations for Monotonic Neural Networks
Cardinality-Minimal Explanations for Monotonic Neural Networks
Ouns El Harzli
Bernardo Cuenca Grau
Ian Horrocks
FAtt
35
5
0
19 May 2022
On the Robustness of Sparse Counterfactual Explanations to Adverse
  Perturbations
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations
M. Virgolin
Saverio Fracaros
CML
26
36
0
22 Jan 2022
Synthesizing Pareto-Optimal Interpretations for Black-Box Models
Synthesizing Pareto-Optimal Interpretations for Black-Box Models
Hazem Torfah
Shetal Shah
Supratik Chakraborty
S. Akshay
S. Seshia
22
6
0
16 Aug 2021
Fair Normalizing Flows
Fair Normalizing Flows
Mislav Balunović
Anian Ruoss
Martin Vechev
AAML
13
36
0
10 Jun 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Valerie Chen
Jeffrey Li
Joon Sik Kim
Gregory Plumb
Ameet Talwalkar
32
29
0
10 Mar 2021
Robust and Stable Black Box Explanations
Robust and Stable Black Box Explanations
Himabindu Lakkaraju
Nino Arsov
Osbert Bastani
AAML
FAtt
24
84
0
12 Nov 2020
Generative causal explanations of black-box classifiers
Generative causal explanations of black-box classifiers
Matthew R. O’Shaughnessy
Gregory H. Canal
Marissa Connor
Mark A. Davenport
Christopher Rozell
CML
25
73
0
24 Jun 2020
Questioning the AI: Informing Design Practices for Explainable AI User
  Experiences
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
Q. V. Liao
D. Gruen
Sarah Miller
52
703
0
08 Jan 2020
Automated Dependence Plots
Automated Dependence Plots
David I. Inouye
Liu Leqi
Joon Sik Kim
Bryon Aragam
Pradeep Ravikumar
12
1
0
02 Dec 2019
Model Agnostic Contrastive Explanations for Structured Data
Model Agnostic Contrastive Explanations for Structured Data
Amit Dhurandhar
Tejaswini Pedapati
Avinash Balakrishnan
Pin-Yu Chen
Karthikeyan Shanmugam
Ruchi Puri
FAtt
20
82
0
31 May 2019
Leveraging Latent Features for Local Explanations
Leveraging Latent Features for Local Explanations
Ronny Luss
Pin-Yu Chen
Amit Dhurandhar
P. Sattigeri
Yunfeng Zhang
Karthikeyan Shanmugam
Chun-Chen Tu
FAtt
41
37
0
29 May 2019
Explaining Explanations to Society
Explaining Explanations to Society
Leilani H. Gilpin
Cecilia Testart
Nathaniel Fruchter
Julius Adebayo
XAI
24
34
0
19 Jan 2019
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,238
0
24 Jun 2017
Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord
Nal Kalchbrenner
Koray Kavukcuoglu
SSeg
GAN
233
2,547
0
25 Jan 2016
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