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Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks.
The mechanics of human saccadic eye movement
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Gunnar Farnebäck · 2003
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Task and context determine where you look
Constantin A Rothkopf, Dana H Ballard, and Mary M Hayhoe · 2007
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Interactions between working memory, attention and eye movements
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Adadelta: an adaptive learning rate method
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Saccades to future ball location reveal memory-based prediction in a virtual-reality interception task
Gabriel Diaz, Joseph Cooper, Constantin Rothkopf, and Mary Hayhoe · 2013
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Methods for comparing scanpaths and saliency maps: strengths and weaknesses
Olivier Le Meur and Thierry Baccino · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Hierarchical modular optimization of convolutional networks achieves representations similar to macaque it and human ventral stream
Daniel L Yamins, Ha Hong, Charles Cadieu, and James J DiCarlo · 2013
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Defending yarbus: Eye movements reveal observers’ task
Ali Borji and Laurent Itti · 2014
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Predicting human visuomotor behaviour in a driving task
Leif Johnson, Brian Sullivan, Mary Hayhoe, and Dana Ballard · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Salient object detection: A benchmark
Ali Borji, Ming-Ming Cheng, Huaizu Jiang, and Jia Li · 2015
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The dependence of effective planning horizon on model accuracy
Nan Jiang, Alex Kulesza, Satinder Singh, and Richard Lewis · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Where should saliency models look next?
Zoya Bylinskii, Adrià Recasens, Ali Borji, Aude Oliva, Antonio Torralba, and Frédo Durand · 2016
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Natural speech reveals the semantic maps that tile human cerebral cortex
Alexander G Huth, Wendy A De Heer, Thomas L Griffiths, Frédéric E Theunissen, and Jack L Gallant · 2016
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Visualizing deep convolutional neural networks using natural pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Learning to predict where to look in interactive environments using deep recurrent q-learning
Sajad Mousavi, Michael Schukat, Enda Howley, Ali Borji, and Nasser Mozayani · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Human attention in visual question answering: Do humans and deep networks look at the same regions?
Abhishek Das, Harsh Agrawal, Larry Zitnick, Devi Parikh, and Dhruv Batra · 2017
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Seeing it all: Convolutional network layers map the function of the human visual system
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, and Bertrand Thirion · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Modeling latent attention within neural networks
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson LS Wong, and Michael L Littman · 2017
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Dynamic interaction between reinforcement learning and attention in multidimensional environments
Yuan Chang Leong, Angela Radulescu, Reka Daniel, Vivian DeWoskin, and Yael Niv · 2017
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Agil: Learning attention from human for visuomotor tasks
Ruohan Zhang, Zhuode Liu, Luxin Zhang, Jake A Whritner, Karl S Muller, Mary M Hayhoe, and Dana H Ballard · 2018
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Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning
Akanksha Atrey, Kaleigh Clary, and David Jensen · 2019
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What do different evaluation metrics tell us about saliency models?
Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, and Frédo Durand · 2019
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How do drivers allocate their potential attention? driving fixation prediction via convolutional neural networks
Tao Deng, Hongmei Yan, Long Qin, Thuyen Ngo, and BS Manjunath · 2019
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Hyperbolic discounting and learning over multiple horizons
William Fedus, Carles Gelada, Yoshua Bengio, Marc G Bellemare, and Hugo Larochelle · 2019
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Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Paying attention to descriptions generated by image captioning models
Hamed R Tavakoli, Rakshith Shetty, Ali Borji, and Jorma Laaksonen · 2017
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Explain your move: Understanding agent actions using focused feature saliency
Piyush Gupta, Nikaash Puri, Sukriti Verma, Sameer Singh, Dhruv Kayastha, Shripad Deshmukh, and Balaji Krishnamurthy · 2019
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Understanding and visualizing deep visual saliency models
Sen He, Hamed R Tavakoli, Ali Borji, Yang Mi, and Nicolas Pugeault · 2019
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Human attention in image captioning: Dataset and analysis
Sen He, Hamed R Tavakoli, Ali Borji, and Nicolas Pugeault · 2019
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Drlviz: Understanding decisions and memory in deep reinforcement learning
Theo Jaunet, Romain Vuillemot, and Christian Wolf · 2019
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Visualization of deep reinforcement learning using grad-cam: How ai plays atari games?
Ho-Taek Joo and Kyung-Joong Kim · 2019
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Human \ \backslash textit { \{ vs } \} machine attention in neural networks: A comparative study
Qiuxia Lai, Wenguan Wang, Salman Khan, Jianbing Shen, Hanqiu Sun, and Ling Shao · 2019
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Deep neuroethology of a virtual rodent
Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, and Bence Ölveczky · 2019
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Exploring expertise through visualizing agent policies and human strategies in open-ended games
Steven Moore and John C Stamper · 2019
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Towards interpretable reinforcement learning using attention augmented agents
Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, and Danilo Jimenez Rezende · 2019
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Free-lunch saliency via attention in atari agents
Dmitry Nikulin, Anastasia Ianina, Vladimir Aliev, and Sergey Nikolenko · 2019
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Explain your move: Understanding agent actions using specific and relevant feature attribution
Nikaash Puri, Sukriti Verma, Piyush Gupta, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, and Sameer Singh · 2019
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An atari model zoo for analyzing, visualizing, and comparing deep reinforcement learning agents
Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G Bellemare, Jeff Clune, et al · 2019
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Reinforcement learning interpretation methods: A survey
Alnour Alharin, Thanh-Nam Doan, and Mina Sartipi · 2020
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Agent57: Outperforming the atari human benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, and Charles Blundell · 2020
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Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments
Logan Cross, Jeff Cockburn, Yisong Yue, and John P O’Doherty · 2020
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Explainability in deep reinforcement learning
Alexandre Heuillet, Fabien Couthouis, and Natalia Díaz-Rodríguez · 2020
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Explainable reinforcement learning: A survey
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Understanding more about human and machine attention in deep neural networks
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Efficiently guiding imitation learning algorithms with human gaze
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Self-supervised discovering of causal features: Towards interpretable reinforcement learning
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Analyzing visual representations in embodied navigation tasks
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Human gaze assisted artificial intelligence: A review
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Atari-head: Atari human eye-tracking and demonstration dataset
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Benchmarking perturbation-based saliency maps for explaining deep reinforcement learning agents
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Introducing and assessing the explainable ai (xai) method: Sidu
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