ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1802.00541
  4. Cited By
Causal Learning and Explanation of Deep Neural Networks via Autoencoded
  Activations

Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

2 February 2018
M. Harradon
Jeff Druce
Brian E. Ruttenberg
    BDL
    CML
ArXivPDFHTML

Papers citing "Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations"

11 / 11 papers shown
Title
Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Yuxiao Cheng
Xinxin Song
Ziqian Wang
Qin Zhong
Kunlun He
J. Suo
OOD
CML
107
0
0
04 Feb 2025
Theoretical Impediments to Machine Learning With Seven Sparks from the
  Causal Revolution
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Judea Pearl
CML
61
330
0
11 Jan 2018
Relating Input Concepts to Convolutional Neural Network Decisions
Relating Input Concepts to Convolutional Neural Network Decisions
Ning Xie
Md Kamruzzaman Sarker
Derek Doran
Pascal Hitzler
M. Raymer
FAtt
52
15
0
21 Nov 2017
Fine-Grained Car Detection for Visual Census Estimation
Fine-Grained Car Detection for Visual Census Estimation
Timnit Gebru
J. Krause
Yilun Wang
Duyun Chen
Jia Deng
Li Fei-Fei
110
117
0
07 Sep 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
232
19,796
0
07 Oct 2016
End to End Learning for Self-Driving Cars
End to End Learning for Self-Driving Cars
Mariusz Bojarski
D. Testa
Daniel Dworakowski
Bernhard Firner
B. Flepp
...
Urs Muller
Jiakai Zhang
Xin Zhang
Jake Zhao
Karol Zieba
SSL
63
4,153
0
25 Apr 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
746
16,828
0
16 Feb 2016
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
201
18,922
0
20 Dec 2014
Understanding Deep Image Representations by Inverting Them
Understanding Deep Image Representations by Inverting Them
Aravindh Mahendran
Andrea Vedaldi
FAtt
99
1,959
0
26 Nov 2014
Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
214
7,252
0
20 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
389
15,825
0
12 Nov 2013
1