Fetching the paper…
Reading the bibliography…
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles.
Colwell I, Phan B, Saleem S, Salay R, Czarnecki K (2018) An automated vehicle safety concept based on runtime restriction of the operational design domain. pp 1910–1917
1917
Earlier work this paper cites.
Stone M (1974) Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological) 36(2):111–133
1974
Earlier work this paper cites.
Dantzig GB (1987) Origins of the simplex method. Tech. rep., STANFORD UNIV CA SYSTEMS OPTIMIZATION LAB
1987
Earlier work this paper cites.
Ben-David S, Kushilevitz E, Mansour Y (1997) Online learning versus offline learning. Machine Learning 29(1):45–63
1997
Earlier work this paper cites.
ISO/IEC 9126:2001 (2001) Software engineering – product quality. Tech. rep., International Organization for Standardization, International Electrotechnical Commission
2001
Earlier work this paper cites.
Andrews PB (2002) Introduction to Mathematical Logic and Type Theory: To Truth Through Proof, 2nd edn. Kluwer Academic Publishers, Norwell, MA, USA
2002
Earlier work this paper cites.
Kelly T, Weaver R (2004) The goal structuring notation – a safety argument notation. In: Proc. of Dependable Systems and Networks 2004 Workshop on Assurance Cases
2004
Earlier work this paper cites.
Tsymbal A (2004) The problem of concept drift: Definitions and related work. Tech. rep
2004
Earlier work this paper cites.
Garcia FA, Sánchez A (2006) Formal verification of safety and liveness properties for logic controllers. a tool comparison. 2006 3rd International Conference on Electrical and Electronics Engineering pp 1–3
2006
Earlier work this paper cites.
De Moura L, Bjørner N (2008) Z3: An efficient smt solver. In: Proceedings of the Theory and Practice of Software, 14th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer-Verlag, Berlin, Heidelberg, TACAS’08/ETAPS’08, pp 337–340
2008
Earlier work this paper cites.
Lam WK (2008) Hardware Design Verification: Simulation and Formal Method-Based Approaches, 1st edn. Prentice Hall PTR, Upper Saddle River, NJ, USA
2008
Earlier work this paper cites.
Bickel S, Brückner M, Scheffer T (2009) Discriminative learning under covariate shift. Journal of Machine Learning Research 10:2137–2155
2009
Earlier work this paper cites.
Arlot S, Celisse A, et al (2010) A survey of cross-validation procedures for model selection. Statistics surveys 4:40–79
2010
Earlier work this paper cites.
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Fürnkranz J, Joachims T (eds) Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel, Omnipress, pp 807–814
2010
Earlier work this paper cites.
Pulina L, Tacchella A (2010) An abstraction-refinement approach to verification of artificial neural networks. In: CAV
2010
Earlier work this paper cites.
Ali GGMN, Chan E (2011) Co-operative data access in multiple road side units (rsus)-based vehicular ad hoc networks (vanets). In: Australasian Telecommunication Networks and Applications Conference, ATNAC 2011, Melbourne, Australia, November 9-11, 2011, IEEE, pp 1–6
2011
Earlier work this paper cites.
Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87
2012
Earlier work this paper cites.
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in Neural Information Processing Systems 25, Curran Associates, Inc., pp 1097–1105
2012
Earlier work this paper cites.
Pulina L, Tacchella A (2012) Challenging smt solvers to verify neural networks. AI Commun 25(2):117–135
2012
Earlier work this paper cites.
Donzé A (2013) On signal temporal logic. In: International Conference on Runtime Verification, Springer, pp 382–383
2013
Earlier work this paper cites.
Graves A, Jaitly N, Mohamed A (2013) Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic, December 8-12, 2013, IEEE, pp 273–278
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
Murphy KP (2013) Machine learning : a probabilistic perspective. MIT Press, Cambridge, Mass. [u.a.]
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
ISO/IEC 25000:2014 (2014) Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – Guide to SQuaRE. Standard, International Organization for Standardization, International Electrotechnical Commission
2014
Earlier work this paper cites.
James G, Witten D, Hastie T, Tibshirani R (2014) An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated
2014
Cited alongside, same era.
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22Nd ACM International Conference on Multimedia, ACM, New York, NY, USA, MM ’14, pp 675–678
2014
Cited alongside, same era.
Simonyan K, Vedaldi A, Zisserman A (2014) Deep inside convolutional networks: Visualising image classification models and saliency maps. In: Proceedings of the International Conference on Learning Representations (ICLR)
2014
Cited alongside, same era.
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in Neural Information Processing Systems 27, Curran Associates, Inc., pp 3104–3112
2014
Cheng CH, Nührenberg G, Ruess H (2017) Maximum resilience of artificial neural networks. In: ATVA
2017
Later among the works it cites.
Dreossi T, Ghosh S, Sangiovanni-Vincentelli AL, Seshia SA (2017) Systematic testing of convolutional neural networks for autonomous driving. In: ICML Workshop on Reliable Machine Learning in the Wild (RMLW)
2017
Later among the works it cites.
Falcini F, Lami G (2017) Deep learning in automotive: Challenges and opportunities. In: Mas A, Mesquida A, O’Connor RV, Rout T, Dorling A (eds) Software Process Improvement and Capability Determination, Springer International Publishing, Cham, pp 279–288
2017
Later among the works it cites.
Huang X, Kwiatkowska MZ, Wang S, Wu M (2017) Safety verification of deep neural networks. In: CAV
2017
Later among the works it cites.
Lemmer K, Mazzega J (2017) Pegasus: Effectively ensuring automated driving. In: VDA Technical Congress
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision – ECCV 2014, Springer International Publishing, Cham, pp 818–833
2014
Cited alongside, same era.
Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, WWW ’15, pp 278–288
2015
Cited alongside, same era.
INCOSE (2015) Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, version 4.0 edn. John Wiley and Sons, Inc
2015
Cited alongside, same era.
Ng A (2015) Deep learning, nVIDIA GPU Technology Conference (GTC)
2015
Cited alongside, same era.
Sculley D, Holt G, Golovin D, Davydov E, Phillips T, Ebner D, Chaudhary V, Young M, Crespo JF, Dennison D (2015) Hidden technical debt in machine learning systems. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2, MIT Press, Cambridge, MA, USA, NIPS’15, pp 2503–2511
2015
Cited alongside, same era.
VDA QMC Working Group 13 / Automotive SIG (2015) Automotive spice process assessment / reference model version 3.0. Tech. rep., Automotive SPICE
2015
Cited alongside, same era.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2015) Object detectors emerge in deep scene cnns. In: International Conference on Learning Representations (ICLR)
2015
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Later among the works it cites.
Li LE, Dragan A, Niebles JC, Savarese S (2017) 2017 nips workshop on machine learning for intelligent transportation systems
2017
Later among the works it cites.
Montavon G, Lapuschkin S, Binder A, Samek W, Müller K (2017) Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recognition 65:211–222
2017
Later among the works it cites.
2017
Later among the works it cites.
of Transportation UD, Administration NHTS (2017) Automated Driving Systems: A Vision for Safety 2.0
2017
Later among the works it cites.
Wendorff W (2017) Quantitative sotif analysis for highly automated driving systems. In: Safetronic
2017
Later among the works it cites.
(2018) Report of traffic collision involving an autonomous vehicle (ol 316). URL https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/autonomousveh_ol316+
2018
Later among the works it cites.
Borraz R, Navarro PJ, Fernández C, Alcover PM (2018) Cloud incubator car: A reliable platform for autonomous driving. Applied Sciences 8(2)
2018
Later among the works it cites.
Cheng C, Huang C, Yasuoka H (2018a) Quantitative projection coverage for testing ml-enabled autonomous systems. In: Lahiri SK, Wang C (eds) Automated Technology for Verification and Analysis - 16th International Symposium, ATVA 2018, Los Angeles, CA, USA, October 7-10, 2018, Proceedings, Springer, Lecture Notes in Computer Science, vol 11138, pp 126–142
2018
Later among the works it cites.
Cheng C, Nührenberg G, Huang C, Ruess H, Yasuoka H (2018b) Towards dependability metrics for neural networks. In: 16th ACM/IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2018, Beijing, China, October 15-18, 2018, IEEE, pp 43–46
2018
Later among the works it cites.
Czarnecki K (2018) On-road safety of automated driving system (ads) - taxonomy and safety analysis methods
2018
Later among the works it cites.
Government U, of Transportation UD (2018) 2018 Federal Guide to Self-Driving Cars and Automated Driving: Preparing for the Future of Transportation - Automated Vehicles 3.0, Safety Issues and Role of the Government in Autonomous Regulation
2018
Later among the works it cites.
Ishikawa F, Matsuno Y (2018) Continuous argument engineering: Tackling uncertainty in machine learning based systems. In: Gallina B, Skavhaug A, Schoitsch E, Bitsch F (eds) Computer Safety, Reliability, and Security - SAFECOMP 2018 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Västerås, Sweden, September 18, 2018, Proceedings, Springer, Lecture Notes in Computer Science, vol 11094, pp 14–21
2018
Later among the works it cites.
ISO 26262-1:2018 (2018) Road vehicles – functional safety – part 1: Vocabulary. Tech. rep., International Organization for Standardization
2018
Later among the works it cites.
Poggenhans F, Pauls J, Janosovits J, Orf S, Naumann M, Kuhnt F, Mayr M (2018) Lanelet2: A high-definition map framework for the future of automated driving. In: ITSC, IEEE, pp 1672–1679
2018
Later among the works it cites.
2018
Later among the works it cites.
Dreossi T, Donzé A, A Seshia S (2019) Compositional falsification of cyber-physical systems with machine learning components. Journal of Automated Reasoning
2019
Closest in time.
Kuwajima H, Tanaka M, Okutomi M (2019) Improving transparency of deep neural inference process. Progress in Artificial Intelligence 8(2):273–285
2019
Closest in time.
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S (2019) Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery 18(6):463–477
2019
Closest in time.
Zendel O, Murschitz M, Humenberger M, Herzner W (2015) CV-HAZOP: introducing test data validation for computer vision. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015, IEEE Computer Society, pp 2066–2074
2074
Closest in time.