Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

15 February 2017

Papers citing "Visualizing Deep Neural Network Decisions: Prediction Difference Analysis"

50 / 328 papers shown

Title
Software and application patterns for explanation methods Maximilian Alber 17 11 0 09 Apr 2019
Visualization of Convolutional Neural Networks for Monocular Depth Estimation Junjie Hu Yan Zhang Takayuki Okatani MDE 17 83 0 06 Apr 2019
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents Christian Rupprecht Cyril Ibrahim C. Pal 13 32 0 02 Apr 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation Marco Ancona Cengiz Öztireli Markus Gross FAtt TDI 14 223 0 26 Mar 2019
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet Wieland Brendel Matthias Bethge SSL FAtt 15 557 0 20 Mar 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 20 1,282 0 10 Mar 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 27 11 0 01 Mar 2019
Capacity allocation through neural network layers Jonathan Donier 9 3 0 22 Feb 2019
Capacity allocation analysis of neural networks: A tool for principled architecture design Jonathan Donier 14 4 0 12 Feb 2019
Learning Decision Trees Recurrently Through Communication Stephan Alaniz Diego Marcos Bernt Schiele Zeynep Akata 17 16 0 05 Feb 2019
Visual Rationalizations in Deep Reinforcement Learning for Atari Games L. Weitkamp Elise van der Pol Zeynep Akata 8 27 0 01 Feb 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 17 445 0 27 Jan 2019
Learning Global Pairwise Interactions with Bayesian Neural Networks Tianyu Cui Pekka Marttinen Samuel Kaski BDL 11 17 0 24 Jan 2019
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells Vignesh Sankar Devinder Kumar David A Clausi Graham W. Taylor Alexander Wong 11 22 0 15 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 21 1,415 0 14 Jan 2019
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan M. Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 8 50 0 18 Dec 2018
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery Xiaoxiao Li Nicha Dvornek Yuan Zhou Juntang Zhuang P. Ventola James S. Duncan 132 19 0 14 Dec 2018
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs Daniel Neil Joss Briody A. Lacoste Aaron Sim Páidí Creed Amir Saffari GNN 66 35 0 01 Dec 2018
Rank Projection Trees for Multilevel Neural Network Interpretation J. Warrell Hussein Mohsen M. Gerstein FAtt 14 0 0 01 Dec 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 23 102 0 28 Nov 2018
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration M. Cavallo Çağatay Demiralp 6 55 0 28 Nov 2018
Deformable ConvNets v2: More Deformable, Better Results Xizhou Zhu Han Hu Stephen Lin Jifeng Dai ObjD 22 1,980 0 27 Nov 2018
Data Augmentation using Random Image Cropping and Patching for Deep CNNs Ryo Takahashi Takashi Matsubara K. Uehara 9 326 0 22 Nov 2018
How You See Me Rohit Gandikota Deepak Mishra OOD 14 0 0 20 Nov 2018
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions Denis A. Gudovskiy Alec Hodgkinson Takuya Yamaguchi Yasunori Ishii Sotaro Tsukizawa FAtt 14 13 0 19 Nov 2018
An Overview of Computational Approaches for Interpretation Analysis Philipp Blandfort Jörn Hees D. Patton 19 2 0 09 Nov 2018
Explaining Deep Learning Models - A Bayesian Non-parametric Approach Wenbo Guo Sui Huang Yunzhe Tao Xinyu Xing Lin Lin BDL 11 47 0 07 Nov 2018
What evidence does deep learning model use to classify Skin Lesions? Xiaoxiao Li Junyan Wu Eric Z. Chen Hongda Jiang 11 9 0 02 Nov 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 11 128 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 12 1,926 0 08 Oct 2018
Diagnosing Convolutional Neural Networks using their Spectral Response V. Stamatescu Mark D Mcdonnell 14 3 0 08 Oct 2018
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees Klaus Broelemann Gjergji Kasneci 14 20 0 25 Sep 2018
Ensemble learning with 3D convolutional neural networks for connectome-based prediction Meenakshi Khosla K. Jamison Amy Kuceyeski M. Sabuncu 3DV 6 88 0 11 Sep 2018
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI Xiaoxiao Li Nicha Dvornek Juntang Zhuang P. Ventola James S. Duncan 26 70 0 23 Aug 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 22 1,071 0 31 Jul 2018
Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks Dasom Seo Kanghan Oh Il-Seok Oh FAtt 17 23 0 31 Jul 2018
Computationally Efficient Measures of Internal Neuron Importance Avanti Shrikumar Jocelin Su A. Kundaje FAtt 11 29 0 26 Jul 2018
Grounding Visual Explanations Lisa Anne Hendricks Ronghang Hu Trevor Darrell Zeynep Akata FAtt 6 225 0 25 Jul 2018
Explaining Image Classifiers by Counterfactual Generation C. Chang Elliot Creager Anna Goldenberg D. Duvenaud VLM 11 264 0 20 Jul 2018
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract) Lisa Anne Hendricks Kaylee Burns Kate Saenko Trevor Darrell Anna Rohrbach 14 477 0 02 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 16 670 0 28 Jun 2018
Quantum-chemical insights from interpretable atomistic neural networks Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller AI4CE 15 31 0 27 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 15 145 0 14 Jun 2018
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records Bum Chul Kwon Min-Je Choi J. Kim E. Choi Young Bin Kim Soonwook Kwon Jimeng Sun Jaegul Choo 25 251 0 28 May 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models Jacob R. Kauffmann K. Müller G. Montavon DRL 28 96 0 16 May 2018
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models Hendrik Strobelt Sebastian Gehrmann M. Behrisch Adam Perer Hanspeter Pfister Alexander M. Rush VLM HAI 23 239 0 25 Apr 2018
Opening the black box of neural nets: case studies in stop/top discrimination Thomas Roxlo M. Reece 8 22 0 24 Apr 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 14 217 0 20 Mar 2018
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence Dong Huk Park Lisa Anne Hendricks Zeynep Akata Anna Rohrbach Bernt Schiele Trevor Darrell Marcus Rohrbach 35 418 0 15 Feb 2018
TSViz: Demystification of Deep Learning Models for Time-Series Analysis Shoaib Ahmed Siddiqui Dominique Mercier Mohsin Munir Andreas Dengel Sheraz Ahmed FAtt AI4TS 24 82 0 08 Feb 2018