Fetching the paper…
Reading the bibliography…
A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data.
A. Raffin, A. Hill, K. R. Traoré, T. Lesort, N. D. Rodríguez, D. Filliat, Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics , CoRR abs/1901.08651 (2019) · 1901
Earlier work this paper cites.
D. Han, K. Doya, J. Tani, Emergence of hierarchy via reinforcement learning using a multiple timescale stochastic RNN , CoRR abs/1901.10113 (2019) · 1901
Earlier work this paper cites.
S. Jain, B. C. Wallace, Attention is not explanation (2019) · 1902
Earlier work this paper cites.
1904
Earlier work this paper cites.
H. Caselles-Dupré, M. Garcia-Ortiz, D. Filliat, Symmetry-based disentangled representation learning requires interaction with environments (2019) · 1904
Earlier work this paper cites.
P. Madumal, T. Miller, L. Sonenberg, F. Vetere, Explainable reinforcement learning through a causal lens (2019) · 1905
Earlier work this paper cites.
R. Hu, A. Rohrbach, T. Darrell, K. Saenko, Language-conditioned graph networks for relational reasoning (2019) · 1905
Earlier work this paper cites.
T. Pierrot, G. Ligner, S. Reed, O. Sigaud, N. Perrin, A. Laterre, D. Kas, K. Beguir, N. de Freitas, Learning compositional neural programs with recursive tree search and planning (2019) · 1905
Earlier work this paper cites.
J. Wang, Y. Zhang, T.-K. Kim, Y. Gu, Shapley q-value: A local reward approach to solve global reward games (2019) · 1907
Earlier work this paper cites.
R. Traoré, H. Caselles-Dupré, T. Lesort, T. Sun, G. Cai, N. Díaz-Rodríguez, D. Filliat, Discorl: Continual reinforcement learning via policy distillation (2019) · 1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
M. Sundararajan, A. Najmi, The many shapley values for model explanation (2019) · 1908
Earlier work this paper cites.
A. B. Arrieta, N. Díaz-Rodríguez, J. D. Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, R. Chatila, F. Herrera, Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai (2019) · 1910
Earlier work this paper cites.
G. Cideron, M. Seurin, F. Strub, O. Pietquin, Self-educated language agent with hindsight experience replay for instruction following (2019) · 1910
Earlier work this paper cites.
T. N. Mundhenk, B. Y. Chen, G. Friedland, Efficient saliency maps for explainable ai (2019) · 1911
Earlier work this paper cites.
P. Sequeira, M. Gervasio, Interestingness elements for explainable reinforcement learning: Understanding agents’ capabilities and limitations (2019) · 1912
Earlier work this paper cites.
G. Rummery, M. Niranjan, On-line q-learning using connectionist systems , Technical Report CUED/F-INFENG/TR 166 (11 1994). URL http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.2539&rep=rep1&type=pdf
1994
Earlier work this paper cites.
Y. LeCun, Y. Bengio, et al., Convolutional networks for images, speech, and time series , The handbook of brain theory and neural networks 3361 (10) (1995) 1995. URL https://www.researchgate.net/publication/2453996_Convolutional_Networks_for_Images_Speech_and_Time-Series
1995
Earlier work this paper cites.
G. N. Tasse, S. James, B. Rosman, A boolean task algebra for reinforcement learning (2020) · 2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
E. Kharitonov, M. Baroni, Emergent language generalization and acquisition speed are not tied to compositionality (2020) · 2004
Earlier work this paper cites.
doi:10.1007/978-0-387-30164-8_417
P. Abbeel, A. Ng, Apprenticeship learning via inverse reinforcement learning, Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 (09 2004) · 2004
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei, Language models are few-shot learners (2020) · 2005
Earlier work this paper cites.
S. Doncieux, N. Bredeche, L. L. Goff, B. Girard, A. Coninx, O. Sigaud, M. Khamassi, N. Díaz-Rodríguez, D. Filliat, T. Hospedales, A. Eiben, R. Duro, Dream architecture: a developmental approach to open-ended learning in robotics (2020) · 2005
Earlier work this paper cites.
arXiv:https://academic.oup.com/bjps/article-pdf/56/4/843/4256158/axi147.pdf
J. Y. Halpern, J. Pearl, Causes and Explanations: A Structural-Model Approach. Part I: Causes , The British Journal for the Philosophy of Science 56 (4) (2005) 843–887 · 2005
Earlier work this paper cites.
M. Matarese, S. Rossi, A. Sciutti, F. Rea, Towards transparency of td-rl robotic systems with a human teacher (2020) · 2005
Earlier work this paper cites.
2006
Earlier work this paper cites.
2008
Earlier work this paper cites.
doi:10.1049/iet-its.2009.0070
I. Arel, C. Liu, T. Urbanik, A. G. Kohls, Reinforcement learning-based multi-agent system for network traffic signal control , IET Intelligent Transport Systems 4 (2) (2010) 128–135 · 2009
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, G. Monfardini, The graph neural network model , IEEE Transactions on Neural Networks 20 (1) (2009) 61–80. URL https://persagen.com/files/misc/scarselli2009graph.pdf
2009
Earlier work this paper cites.
Y. Bengio, A. Courville, P. Vincent, Representation learning: A review and new perspectives (2012) · 2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
D. P. Kingma, M. Welling, Auto-encoding variational bayes (2013) · 2013
Earlier work this paper cites.
H. Kawano, Hierarchical sub-task decomposition for reinforcement learning of multi-robot delivery mission (2013) 828–835 doi:10.1109/ICRA.2013.6630669
2013
Earlier work this paper cites.
K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps (2013) · 2013
Earlier work this paper cites.
R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation (2013) · 2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks (2014) · 2014
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al., Human-level control through deep reinforcement learning , Nature 518 (7540) (2015) 529. URL https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf
2015
Earlier work this paper cites.
C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, P. Abbeel, Deep spatial autoencoders for visuomotor learning (2015) · 2015
Cited alongside, same era.
R. Jonschkowski, O. Brock, Learning state representations with robotic priors , Autonomous Robots (2015) 407–428. URL https://doi.org/10.1007/s10514-015-9459-7
2015
Cited alongside, same era.
doi:10.13140/2.1.1779.4243
A. Garcez, T. Besold, L. De Raedt, P. Földiák, P. Hitzler, T. Icard, K.-U. Kühnberger, L. Lamb, R. Miikkulainen, D. Silver, Neural-symbolic learning and reasoning: Contributions and challenges , 2015 · 2015
Cited alongside, same era.
Y. Gal, Z. Ghahramani, Dropout as a bayesian approximation: Representing model uncertainty in deep learning (2015) · 2015
Cited alongside, same era.
J. García, Fern, o Fernández, A comprehensive survey on safe reinforcement learning , Journal of Machine Learning Research 16 (42) (2015) 1437–1480. URL http://jmlr.org/papers/v16/garcia15a.html
arXiv:https://doi.org/10.1080/09540091.2017.1310182
A. Theodorou, R. H. Wortham, J. J. Bryson, Designing and implementing transparency for real time inspection of autonomous robots , Connection Science 29 (3) (2017) 230–241 · 2017
Later among the works it cites.
2017
Later among the works it cites.
M. Sundararajan, A. Taly, Q. Yan, Axiomatic attribution for deep networks , in: D. Precup, Y. W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, Vol. 70 of Proceedings of Machine Learning Research, PMLR, International Convention Centre, Sydney, Australia, 2017, pp. 3319–3328. URL http://proceedings.mlr.press/v70/sundararajan17a.html
2017
Later among the works it cites.
J. Foerster, G. Farquhar, T. Afouras, N. Nardelli, S. Whiteson, Counterfactual multi-agent policy gradients (2017) · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
J.-B. Mouret, J. Clune, Illuminating search spaces by mapping elites (2015) · 2015
Cited alongside, same era.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, D. Wierstra, Continuous control with deep reinforcement learning (2015) · 2015
Cited alongside, same era.
Y. Duan, X. Chen, R. Houthooft, J. Schulman, P. Abbeel, Benchmarking deep reinforcement learning for continuous control (2016) · 2016
Cited alongside, same era.
H. Mao, M. Alizadeh, I. Menache, S. Kandula, Resource management with deep reinforcement learning (2016) 50–56 doi:10.1145/3005745.3005750
2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, C. Guestrin, "why should i trust you?": Explaining the predictions of any classifier (2016) · 2016
Cited alongside, same era.
H. van Hoof, N. Chen, M. Karl, P. van der Smagt, J. Peters, Stable reinforcement learning with autoencoders for tactile and visual data , 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016) 3928–3934. URL https://www.ias.informatik.tu-darmstadt.de/uploads/Site/EditPublication/hoof2016IROS.pdf
2016
Cited alongside, same era.
E. Shelhamer, P. Mahmoudieh, M. Argus, T. Darrell, Loss is its own reward: Self-supervision for reinforcement learning (2016) · 2016
Cited alongside, same era.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms (2017) · 2017
Later among the works it cites.
R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction , 2nd Edition, The MIT Press, 2018. URL http://incompleteideas.net/book/the-book-2nd.html
2018
Later among the works it cites.
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, S. Levine, Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation (2018) · 2018
Later among the works it cites.
G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. Yuan, X. Xie, Z. Li, Drn: A deep reinforcement learning framework for news recommendation (2018) 167–176 doi:10.1145/3178876.3185994
2018
Later among the works it cites.
R. R. Hoffman, S. T. Mueller, G. Klein, J. Litman, Metrics for explainable ai: Challenges and prospects (2018) · 2018
Later among the works it cites.
V. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. Reichert, T. Lillicrap, E. Lockhart, M. Shanahan, V. Langston, R. Pascanu, M. Botvinick, O. Vinyals, P. Battaglia, Relational deep reinforcement learning (2018) · 2018
Later among the works it cites.
2018
Later among the works it cites.
T. Haarnoja, V. Pong, A. Zhou, M. Dalal, P. Abbeel, S. Levine, Composable deep reinforcement learning for robotic manipulation , 2018 IEEE International Conference on Robotics and Automation (ICRA) (May 2018) · 2018
Later among the works it cites.
B. Lütjens, M. Everett, J. P. How, Safe reinforcement learning with model uncertainty estimates (2018) · 2018
Later among the works it cites.
A. Raffin, A. Hill, R. Traoré, T. Lesort, N. Díaz-Rodríguez, D. Filliat, S-rl toolbox: Environments, datasets and evaluation metrics for state representation learning (2018) · 2018
Later among the works it cites.
doi:10.1016/j.cobeha.2018.12.010
M. Garnelo, M. Shanahan, Reconciling deep learning with symbolic artificial intelligence: representing objects and relations , Current Opinion in Behavioral Sciences 29 (2019) 17–23 · 2018
Later among the works it cites.
doi:10.1016/j.neunet.2018.07.006
T. Lesort, N. Díaz-Rodríguez, J.-F. Goudou, D. Filliat, State representation learning for control: An overview , Neural Networks 108 (2018) 379–392 · 2018
Later among the works it cites.
doi:10.3389/fnbot.2018.00059
S. Doncieux, D. Filliat, N. Díaz-Rodríguez, T. Hospedales, R. Duro, A. Coninx, D. M. Roijers, B. Girard, N. Perrin, O. Sigaud, Open-ended learning: A conceptual framework based on representational redescription , Frontiers in Neurorobotics 12 (2018) 59 · 2018
Later among the works it cites.
I. Higgins, D. Amos, D. Pfau, S. Racaniere, L. Matthey, D. Rezende, A. Lerchner, Towards a definition of disentangled representations (2018) · 2018
Later among the works it cites.
A. Achille, T. Eccles, L. Matthey, C. Burgess, N. Watters, A. Lerchner, I. Higgins, Life-long disentangled representation learning with cross-domain latent homologies , in: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), Advances in Neural Information Processing Systems 31, Curran Associates, Inc., 2018, pp. 9873–9883. URL http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies.pdf
2018
Later among the works it cites.
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulcehre, F. Song, A. Ballard, J. Gilmer, G. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, R. Pascanu, Relational inductive biases, deep learning, and graph networks (2018) · 2018
Later among the works it cites.
A. Zhang, H. Satija, J. Pineau, Decoupling dynamics and reward for transfer learning (2018) · 2018
Later among the works it cites.
M. Chevalier-Boisvert, L. Willems, S. Pal, Minimalistic gridworld environment for openai gym, https://github.com/maximecb/gym-minigrid (2018)
2018
Later among the works it cites.
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, B. Kim, Sanity checks for saliency maps (2018) · 2018
Later among the works it cites.
Q. Zhang, S.-C. Zhu, Visual interpretability for deep learning: a survey (2018) · 2018
Later among the works it cites.
N. Díaz-Rodríguez, V. Lomonaco, D. Filliat, D. Maltoni, Don’t forget, there is more than forgetting: new metrics for continual learning (2018) · 2018
Later among the works it cites.
P. Dhar, R. V. Singh, K.-C. Peng, Z. Wu, R. Chellappa, Learning without memorizing (2018) · 2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
O. M. Andrychowicz, B. Baker, M. Chociej, R. Józefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al., Learning dexterous in-hand manipulation , The International Journal of Robotics Research (2019) 027836491988744 doi:10.1177/0278364919887447
2019
Later among the works it cites.
D. Gunning, D. W. Aha, Darpa’s explainable artificial intelligence program , AI Magazine 40 (2) (2019) 44–58. URL https://search.proquest.com/openview/df03d4be3ad9847da3e414fb57bc1f10
2019
Later among the works it cites.
doi:10.1007/s11263-019-01228-7
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization , International Journal of Computer Vision (Oct 2019) · 2019
Later among the works it cites.
P. Sequeira, E. Yeh, M. T. Gervasio, Interestingness elements for explainable reinforcement learning through introspection (2019). URL https://explainablesystems.comp.nus.edu.sg/2019/wp-content/uploads/2019/02/IUI19WS-ExSS2019-1.pdf
2019
Later among the works it cites.
doi:10.3389/frobt.2019.00099
A. Al-Yacoub, Y. Zhao, N. Lohse, M. Goh, P. Kinnell, P. Ferreira, E.-M. Hubbard, Symbolic-based recognition of contact states for learning assembly skills , Frontiers in Robotics and AI 6 (2019) 99 · 2019
Later among the works it cites.
T. Lesort, M. Seurin, X. Li, N. Díaz-Rodríguez, D. Filliat, Deep unsupervised state representation learning with robotic priors: a robustness analysis , in: 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–8. URL https://hal.archives-ouvertes.fr/hal-02381375/document
2019
Later among the works it cites.
H. Caselles-Dupré, M. Garcia Ortiz, D. Filliat, Symmetry-based disentangled representation learning requires interaction with environments , in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32, Curran Associates, Inc., 2019, pp. 4606–4615. URL http://papers.nips.cc/paper/8709-symmetry-based-disentangled-representation-learning-requires-interaction-with-environments.pdf
2019
Later among the works it cites.
doi:10.1098/rstb.2019.0307
M. Baroni, Linguistic generalization and compositionality in modern artificial neural networks , Philosophical Transactions of the Royal Society B: Biological Sciences 375 (1791) (2019) 20190307 · 2019
Later among the works it cites.
R. Chaabouni, E. Kharitonov, E. Dupoux, M. Baroni, Anti-efficient encoding in emergent communication , in: Advances in Neural Information Processing Systems, 2019, pp. 6290–6300. URL https://papers.nips.cc/paper/8859-anti-efficient-encoding-in-emergent-communication.pdf
2019
Later among the works it cites.
J. Kim, S. Moon, A. Rohrbach, T. Darrell, J. Canny, Advisable learning for self-driving vehicles by internalizing observation-to-action rules , in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. URL https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Advisable_Learning_for_Self-Driving_Vehicles_by_Internalizing_Observation-to-Action_Rules_CVPR_2020_paper.html
2020
Closest in time.