Erico Tjoa

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All published works

Action	Title	Year	Authors
+ PDF Chat	Self reward design with fine-grained interpretability	2023	Erico Tjoa Cuntai Guan
+ PDF Chat	Quantifying Explainability of Saliency Methods in Deep Neural Networks With a Synthetic Dataset	2022	Erico Tjoa Cuntai Guan
+	Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI	2022	Erico Tjoa Hong Jing Khok Tushar Chouhan Cuntai Guan
+	Convolutional Neural Network Interpretability with General Pattern Theory	2021	Erico Tjoa Cuntai Guan
+	A Modified Convolutional Network for Auto-encoding based on Pattern Theory Growth Function	2021	Erico Tjoa
+	Self Reward Design with Fine-grained Interpretability	2021	Erico Tjoa Cuntai Guan
+	Two Instances of Interpretable Neural Network for Universal Approximations	2021	Erico Tjoa Cuntai Guan
+	A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI	2020	Erico Tjoa Cuntai Guan
+	Quantifying Explainability of Saliency Methods in Deep Neural Networks	2020	Erico Tjoa Cuntai Guan
+	Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network	2020	Erico Tjoa Cuntai Guan
+	Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset	2020	Erico Tjoa Cuntai Guan
+	Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks	2019	Erico Tjoa Heng Guo Yuhao Lu Cuntai Guan

Common Coauthors

Coauthor	Papers Together
Cuntai Guan	11
Heng Guo	1
Tushar Chouhan	1
Hong Jing Khok	1
Yuhao Lu	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization	2016	Ramprasaath R. Selvaraju Abhishek Das Ramakrishna Vedantam Michael Cogswell Devi Parikh Dhruv Batra	6
+ PDF Chat	Evaluating the Visualization of What a Deep Neural Network Has Learned	2016	Wojciech Samek Alexander Binder Grégoire Montavon Sebastian Lapuschkin Klaus‐Robert Müller	5
+	Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)	2017	Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler Fernanda Viégas Rory Sayres	5
+ PDF Chat	Learning Deep Features for Discriminative Localization	2016	Bolei Zhou Aditya Khosla Àgata Lapedriza Aude Oliva Antonio Torralba	5
+	Striving for Simplicity: The All Convolutional Net	2014	Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller	4
+	A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI	2020	Erico Tjoa Cuntai Guan	4
+ PDF Chat	Interpretable Explanations of Black Boxes by Meaningful Perturbation	2017	Ruth Fong Andrea Vedaldi	4
+ PDF Chat	Analyzing Neuroimaging Data Through Recurrent Deep Learning Models	2019	Armin W. Thomas Hauke R. Heekeren Klaus‐Robert Müller Wojciech Samek	3
+ PDF Chat	Unmasking Clever Hans predictors and assessing what machines really learn	2019	Sebastian Lapuschkin Stephan Wäldchen Alexander Binder Grégoire Montavon Wojciech Samek Klaus‐Robert Müller	3
+ PDF Chat	Network Dissection: Quantifying Interpretability of Deep Visual Representations	2017	David Bau Bolei Zhou Aditya Khosla Aude Oliva Antonio Torralba	3
+ PDF Chat	Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification	2019	Fabian Eitel Kerstin Ritter	3
+ PDF Chat	Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis	2019	Hyebin Lee Seong Tae Kim Yong Man Ro	3
+ PDF Chat	Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI	2019	Alejandro Barredo Arrieta Natalia Díaz-Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik Alberto Barbado Salvador García Sergio Gil-López Daniel Molina Richard Benjamins	3
+ PDF Chat	Explaining Explanations: An Overview of Interpretability of Machine Learning	2018	Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal	3
+ PDF Chat	Autofocus Layer for Semantic Segmentation	2018	Yao Qin Konstantinos Kamnitsas Siddharth Ancha Jay Nanavati Garrison W. Cottrell Antonio Criminisi Aditya Nori	3
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee	3
+	Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI	2018	Xiaoxiao Li Nicha C. Dvornek Juntang Zhuang Pamela Ventola James S. Duncan	3
+	Axiomatic Attribution for Deep Networks	2017	Mukund Sundararajan Ankur Taly Qiqi Yan	3
+ PDF Chat	Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology	2018	Heather D. Couture J. S. Marron Charles M. Perou Melissa A. Troester Marc Niethammer	3
+ PDF Chat	CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison	2019	Jeremy Irvin Pranav Rajpurkar Michael Ko Yifan Yu Silviana Ciurea-Ilcus Chris Chute Henrik Marklund Behzad Haghgoo Robyn L. Ball Katie Shpanskaya	3
+	Learning Important Features Through Propagating Activation Differences	2017	Avanti Shrikumar Peyton Greenside Anshul Kundaje	3
+	A Benchmark for Interpretability Methods in Deep Neural Networks	2018	Sara Hooker Dumitru Erhan Pieter-Jan Kindermans Been Kim	3
+	Quantitative Evaluations on Saliency Methods: An Experimental Study	2020	Xiaohui Li Yuhan Shi Haoyang Li Wei Bai Yuanwei Song Caleb Chen Cao Lei Chen	2
+	SmoothGrad: removing noise by adding noise	2017	Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viégas Martin Wattenberg	2
+ PDF Chat	Understanding the role of individual units in a deep neural network	2020	David Bau Jun-Yan Zhu Hendrik Strobelt Àgata Lapedriza Bolei Zhou Antonio Torralba	2
+ PDF Chat	3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation	2016	Özgün Çiçek Ahmed Abdulkadir Soeren S. Lienkamp Thomas Brox Olaf Ronneberger	2
+	Understanding Neural Networks Through Deep Visualization	2015	Jason Yosinski Jeff Clune Anh Mai Nguyen Thomas J. Fuchs Hod Lipson	2
+ PDF Chat	There and Back Again: Revisiting Backpropagation Saliency Methods	2020	Sylvestre-Alvise Rebuffi Ruth Fong Xu Ji Andrea Vedaldi	2
+	Towards Automatic Concept-based Explanations	2019	Amirata Ghorbani James Wexler James Zou Been Kim	2
+	Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models	2017	Wojciech Samek Thomas Wiegand Klaus‐Robert Müller	2
+	SmoothGrad: removing noise by adding noise	2017	Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viégas Martin Wattenberg	2
+	Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks	2016	Anh Mai Nguyen Jason Yosinski Jeff Clune	2
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps	2013	Karen Simonyan Andrea Vedaldi Andrew Zisserman	2
+ PDF Chat	Interpretation of Neural Networks Is Fragile	2019	Amirata Ghorbani Abubakar Abid James Zou	2
+	Striving for Simplicity: The All Convolutional Net	2014	Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller	2
+	Visualizing Deep Neural Network Decisions: Prediction Difference Analysis	2017	Luisa Zintgraf Taco Cohen Tameem Adel Max Welling	2
+	How to Explain Individual Classification Decisions	2009	David Baehrens Timon Schroeter Stefan Harmeling Motoaki Kawanabe Katja Hansen Klaus‐Robert Müller	2
+	Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks	2017	José Oramas Kaili Wang Tinne Tuytelaars	2
+	One weird trick for parallelizing convolutional neural networks	2014	Alex Krizhevsky	2
+	Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks	2019	Erico Tjoa Heng Guo Yuhao Lu Cuntai Guan	2
+	Investigating the influence of noise and distractors on the interpretation of neural networks	2016	Pieter-Jan Kindermans Kristof T. Schütt Klaus‐Robert Müller Sven Dähne	1
+	Pattern Theory: From Representation to Inference	2007	Ulf Grenander Michael I. Miller	1
+ PDF Chat	A Pattern-Theoretic Characterization of Biological Growth	2007	Ulf Grenander Anuj Srivastava Sanjay Saini	1
+ PDF Chat	Quantum-chemical insights from deep tensor neural networks	2017	Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela K. Müller Alexandre Tkatchenko	1
+	“Why Should I Trust You?”: Explaining the Predictions of Any Classifier	2016	Marco Ribeiro Sameer Singh Carlos Guestrin	1
+ PDF Chat	Understanding deep image representations by inverting them	2015	Aravindh Mahendran Andrea Vedaldi	1
+	Hierarchical Question-Image Co-Attention for Visual Question Answering	2016	Jiasen Lu Jianwei Yang Dhruv Batra Devi Parikh	1
+	Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model	2015	Benjamin Letham Cynthia Rudin Tyler H. McCormick David Madigan	1
+	Learning how to explain neural networks: PatternNet and PatternAttribution	2017	Pieter Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller Dumitru Erhan Been Kim Sven Dähne	1