Autonomic network management principles : from concepts to applications, 2010. ,
Biological neuron, 2013. ,
,
Ieee technology initiatives and related comsoc standards activities, IEEE Communications Magazine, vol.54, issue.7, pp.4-6, 2016. ,
, Gs nfv 003-v1.2.1-network functions virtualisation (nfv) ; terminology for main concepts in nfv, 2014.
Hands-on machine learning with scikit-learn and tensorflow : concepts, tools, and techniques to build intelligent systems, 2017. ,
In memoriam : Arthur samuel : Pioneer in machine learning, AI Magazine, vol.11, issue.3, p.10, 1990. ,
Machine learning. wcb, 1997. ,
The Data Science Handbook, 2017. ,
Computing machinery and intelligence, vol.59, pp.433-460, 1950. ,
In honor of marvin minsky's contributions on his 80th birthday, AI Magazine, vol.28, issue.4, p.207, 2007. ,
Some studies in machine learning using the game of checkers, IBM Journal of Research and Development, vol.44, pp.206-226, 2000. ,
An inductive inference machine, IRE Convention Record, Section on Information Theory, vol.2, pp.56-62, 1957. ,
A few useful things to know about machine learning, Communications of the ACM, vol.55, issue.10, pp.78-87, 2012. ,
DOI : 10.1145/2347736.2347755
URL : http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
Representation learning : A review and new perspectives, IEEE transactions on pattern analysis and machine intelligence, vol.35, pp.1798-1828, 2013. ,
DOI : 10.1109/tpami.2013.50
URL : http://www.cs.princeton.edu/courses/archive/spring13/cos598C/Representation Learning - A Review and New Perspectives.pdf
Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015. ,
Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
DOI : 10.1145/3065386
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf
Strategies for training large scale neural network language models, Automatic Speech Recognition and Understanding, p.2011 ,
DOI : 10.1109/asru.2011.6163930
URL : http://www.clsp.jhu.edu/%7Eadeoras/HomePage/Anoop_Deoras_files/ASRU-2011.pdf
, IEEE Workshop on, pp.196-201, 2011.
Principles of neurodynamics. perceptrons and the theory of brain mechanisms, 1961. ,
, Perceptrons, 1969.
The organization of behavior : A neuropsychological approach, 1949. ,
Learning internal representations by error propagation, 1985. ,
Beyond accuracy, f-score and roc : a family of discriminant measures for performance evaluation, Australian conference on artificial intelligence, vol.4304, pp.1015-1021, 2006. ,
Nonlinear dimensionality reduction by locally linear embedding, science, vol.290, issue.5500, pp.2323-2326, 2000. ,
Novel methods for subset selection with respect to problem knowledge, IEEE Intelligent Systems and their Applications, vol.13, pp.66-74, 1998. ,
Principal components analysis (pca), pp.30602-2501, 2008. ,
Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks, vol.3361, p.1995, 1995. ,
Understanding the difficulty of training deep feedforward neural networks, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp.249-256, 2010. ,
A fast learning algorithm for deep belief nets, Neural computation, vol.18, issue.7, pp.1527-1554, 2006. ,
The gpu computing era, IEEE micro, vol.30, issue.2, 2010. ,
Understanding deep neural networks with rectified linear units, 2016. ,
Improving neural networks by preventing coadaptation of feature detectors, 2012. ,
Dropout : A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014. ,
Long short-term memory, Neural computation, vol.9, issue.8, pp.1735-1780, 1997. ,
Show, attend and tell : Neural image caption generation with visual attention, International Conference on Machine Learning, pp.2048-2057, 2015. ,
A recurrent latent variable model for sequential data, Advances in neural information processing systems, pp.2980-2988, 2015. ,
Draw : A recurrent neural network for image generation, 2015. ,
Learning stochastic recurrent networks, 2014. ,
Superintelligence : Paths, dangers, strategies. OUP Oxford, 2014. ,
Deep belief networks, Scholarpedia, vol.4, issue.5, p.5947, 2009. ,
A survey on transfer learning, IEEE Transactions on knowledge and data engineering, vol.22, issue.10, pp.1345-1359, 2010. ,
Learning and transferring mid-level image representations using convolutional neural networks, Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pp.1717-1724, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00911179
A unified architecture for natural language processing : Deep neural networks with multitask learning, Proceedings of the 25th international conference on Machine learning, pp.160-167, 2008. ,
Do deep nets really need to be deep ?, Advances in neural information processing systems, pp.2654-2662, 2014. ,
Adaptive control processes : a guided tour, 1961. ,
Pro-active network management using data mining, Global Telecommunications Conference, 1998. GLOBECOM, vol.2, pp.1208-1211, 1998. ,
Svm learning of ip address structure for latency prediction, Proceedings of the 2006 SIGCOMM workshop on Mining network data, pp.299-304, 2006. ,
Cognitive radios with genetic algorithms : Intelligent control of software defined radios, Software defined radio forum technical conference, pp.3-8, 2004. ,
A machine learning approach to tcp throughput prediction, ACM SIGMETRICS Performance Evaluation Review, vol.35, pp.97-108, 2007. ,
Cognitive network management with reinforcement learning for wireless mesh networks, International Workshop on IP Operations and Management, pp.168-179, 2007. ,
Applying reinforcement learning towards automating resource allocation and application scalability in the cloud, Concurrency and Computation : Practice and Experience, vol.25, issue.12, pp.1656-1674, 2013. ,
Qos-aware adaptive routing in multi-layer hierarchical software defined networks : a reinforcement learning approach, IEEE International Conference on, pp.25-33, 2016. ,
Using reinforcement learning for autonomic resource allocation in clouds : towards a fully automated workflow, ICAS 2011, The Seventh International Conference on Autonomic and Autonomous Systems, pp.67-74, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-01122123
Url : A unified reinforcement learning approach for autonomic cloud management, Journal of Parallel and Distributed Computing, vol.72, issue.2, pp.95-105, 2012. ,
A preliminary performance comparison of five machine learning algorithms for practical ip traffic flow classification, ACM SIGCOMM Computer Communication Review, vol.36, issue.5, pp.5-16, 2006. ,
Automated traffic classification and application identification using machine learning, Local Computer Networks, 2005. 30th Anniversary. The IEEE Conference on, pp.250-257, 2005. ,
Self-learning ip traffic classification based on statistical flow characteristics, International Workshop on Passive and Active Network Measurement, pp.325-328, 2005. ,
, Designing self-driving networks workshop, pp.2018-2021
Why (and how) networks should run themselves, 2017. ,
Cobanets : A new paradigm for cognitive communications systems, Computing, Networking and Communications (ICNC), 2016 International Conference on, pp.1-7, 2016. ,
Knowledge-defined networking, ACM SIGCOMM Computer Communication Review, vol.47, issue.3, pp.2-10, 2017. ,
A knowledge plane for the internet, Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, pp.3-10, 2003. ,
Network management fundamentals, 2006. ,
Webprophet : Automating performance prediction for web services, NSDI, vol.10, pp.143-158, 2010. ,
Answering what-if deployment and configuration questions with wise, ACM SIGCOMM Computer Communication Review, vol.38, pp.99-110, 2008. ,
An application of machine learning to network intrusion detection, Computer Security Applications Conference, 1999.(ACSAC'99) Proceedings. 15th Annual, pp.371-377, 1999. ,
A deep learning approach for network intrusion detection system, Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp.21-26, 2016. ,
An ai-based network management system, Seventh Annual International Phoenix Conference on, pp.458-461, 1988. ,
Intelligent resource management for local area networks : Approach and evolution, 1988. ,
Expert systems in network management-the second revolution, IEEE Journal on Selected Areas in Communications, vol.6, issue.5, pp.784-787, 1988. ,
Toward the intelligent integrated network management, Global Telecommunications Conference, 1990, and Exhibition.'Communications : Connecting the Future', GLOBECOM'90, pp.1498-1502, 1990. ,
Specification and verification of network managers for large internets, vol.19, 1989. ,
DOI : 10.1145/75247.75251
URL : https://minds.wisconsin.edu/bitstream/handle/1793/59094/TR832.pdf?sequence=1
Applications of artificial intelligence for meeting network management challenges in the 1990s, Global Telecommunications Conference and Exhibition'Communications Technology for the 1990s and Beyond'(GLOBECOM), pp.501-506, 1989. ,
Translation of application-level terms to resource-level attributes across the cloud stack layers, IEEE Symposium on Computers and Communications (ISCC), pp.153-160, 2011. ,
A survey of anticipatory mobile networking : Contextbased classification, prediction methodologies, and optimization techniques, IEEE Communications Surveys & Tutorials, vol.19, issue.3, pp.1790-1821, 2017. ,
DOI : 10.1109/comst.2017.2694140
URL : http://arxiv.org/pdf/1606.00191
An empirical study of throughput prediction in mobile data networks, Global Communications Conference (GLOBECOM), pp.1-6, 2015. ,
A survey of techniques for internet traffic classification using machine learning, IEEE Communications Surveys & Tutorials, vol.10, issue.4, pp.56-76, 2008. ,
Survey and challenges of qoe management issues in wireless networks, Journal of Computer Networks and Communications, vol.2013, 2013. ,
Application-Aware Network Design Using Software-Defined Networking for Application Performance Optimization for Big Data and Video Streaming, 2017. ,
DOI : 10.1109/tnsm.2017.2728519
Sla-nfv : an sla-aware high performance framework for network function virtualization, Proceedings of the 2016 ACM SIGCOMM Conference, pp.581-582, 2016. ,
Performance analysis of unsupervised machine learning techniques for network traffic classification, Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on, pp.401-404, 2015. ,
DOI : 10.1109/acct.2015.54
Flow clustering using machine learning techniques, Passive and Active Network Measurement, pp.205-214, 2004. ,
Internet traffic classification using bayesian analysis techniques, ACM SIGMETRICS Performance Evaluation Review, vol.33, pp.50-60, 2005. ,
DOI : 10.1145/1064212.1064220
URL : http://www.cl.cam.ac.uk/~awm22/publications/moore2005internet.pdf
Unsupervised traffic flow classification using a neural autoencoder, 2017 IEEE 42nd Conference on Local Computer Networks (LCN), pp.523-526, 2017. ,
DOI : 10.1109/lcn.2017.57
vtc : Machine learning based traffic classification as a virtual network function, Proceedings of the 2016 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization, pp.53-56, 2016. ,
Packet routing in dynamically changing networks : A reinforcement learning approach, Advances in neural information processing systems, pp.671-678, 1994. ,
Reinforcement learning for adaptive routing, Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on, vol.2, pp.1825-1830, 2002. ,
DOI : 10.1109/ijcnn.2002.1007796
URL : http://arxiv.org/pdf/cs/0703138
An artificial intelligence approach to network fault management, Sri international, vol.86, 1996. ,
Fault detection and diagnosis in aerospace systems using analytical redundancy, Computing & Control Engineering Journal, vol.2, issue.3, pp.127-136, 1991. ,
DOI : 10.1049/cce:19910031
Fault diagnosis in nonlinear dynamic systems via neural networks, IEE Conference Publication, pp.1346-1346, 1994. ,
DOI : 10.1049/cp:19940332
Fault classification with the aid of artificial neural networks, IFAC Proceedings Volumes, vol.24, pp.541-545, 1991. ,
Neural networks in process fault diagnosis, IEEE Transactions on systems, man, and cybernetics, vol.21, pp.815-825, 1991. ,
DOI : 10.1109/21.108299
A probabilistic approach to fault diagnosis in linear lightwave networks, IEEE Journal on selected areas in communications, vol.11, issue.9, pp.1438-1448, 1993. ,
DOI : 10.1109/49.257935
A study on network fault knowledge acquisition based on support vector machine, Proceedings of 2005 International Conference on, vol.6, pp.3893-3898, 2005. ,
Self-modeling based diagnosis of software-defined networks, Network Softwarization (NetSoft), 2015 1st IEEE Conference on, pp.1-6, 2015. ,
Practical real-time intrusion detection using machine learning approaches, Computer Communications, vol.34, issue.18, pp.2227-2235, 2011. ,
DOI : 10.1016/j.comcom.2011.07.001
Cognitive networks : towards self-aware networks, 2007. ,
Cognet : A network management architecture featuring cognitive capabilities, Networks and Communications (EuCNC), 2016 European Conference on, pp.325-329, 2016. ,
DOI : 10.1109/eucnc.2016.7561056
Knowledge-defined networking, SIGCOMM Comput. Commun. Rev, vol.47, pp.2-10, 2017. ,
DOI : 10.1145/3138808.3138810
URL : http://arxiv.org/pdf/1606.06222
An architectural approach to autonomic computing, Proceedings. International Conference on, pp.2-9, 2004. ,
DOI : 10.1109/icac.2004.1301340
URL : https://www.netlab.tkk.fi/opetus/s384030/k06/papers/AnArchitectureApproachToAutonomicComputing.pdf
Intelligent agents : Theory and practice, The knowledge engineering review, vol.10, issue.2, pp.115-152, 1995. ,
DOI : 10.1017/s0269888900008122
URL : http://www.cse.unsw.edu.au/~wobcke/COMP4416/readings/Wooldridge.Jennings.95.pdf
A survey of autonomic computing-degrees, models, and applications, ACM Computing Surveys (CSUR), vol.40, issue.3, p.7, 2008. ,
DOI : 10.1145/1380584.1380585
URL : http://pubs.doc.ic.ac.uk/autonomic-computing/autonomic-computing.pdf
, Juniper-self-driving networks, 2018.
, Acm sigcomm 2018 afternoon workshop on self-driving networks (selfdn 2018)-acm sigcomm 2018, 2018.
Cognition-based networks : A new perspective on network optimization using learning and distributed intelligence, IEEE Access, vol.3, pp.1512-1530, 2015. ,
DOI : 10.1109/access.2015.2471178
URL : https://doi.org/10.1109/access.2015.2471178
, Amazon mechanical turk, 2018.
860 : Framework of a service level agreement, 2002. ,
Extensible markup language (xml), World Wide Web Journal, vol.2, issue.4, pp.27-66, 1997. ,
Owl : Web ontology language, Encyclopedia of database systems, 2008. ,
The application/json media type for javascript object notation (json), 2006. ,
DOI : 10.17487/rfc4627
URL : https://www.rfc-editor.org/rfc/pdfrfc/rfc4627.txt.pdf
Yaml ain't markup language (yaml) version 1.1," yaml. org, p.23, 2005. ,
Network functions virtualisation ,
Network function virtualization : State-of-the-art and research challenges, IEEE Communications Surveys & Tutorials, vol.18, issue.1, pp.236-262, 2016. ,
Opnfv : An open platform to accelerate nfv, White Paper. A Linux Foundation Collaborative Project, 2012. ,
, Opnfv-ovp, 2018.
, Pdna-devnet, pp.2018-2021
, The data plane development kit
Software-defined networking : The new norm for networks, ONF White Paper, vol.2, pp.2-6, 2012. ,
Fabric : a retrospective on evolving sdn, Proceedings of the first workshop on Hot topics in software defined networks, pp.85-90, 2012. ,
Are we ready for sdn ? implementation challenges for software-defined networks, IEEE Communications Magazine, vol.51, issue.7, pp.36-43, 2013. ,
B4 : Experience with a globally-deployed software defined wan, ACM SIGCOMM Computer Communication Review, vol.43, pp.3-14, 2013. ,
Using sdn for cloud services provisioning : the xifi use-case, Future Networks and Services (SDN4FNS), 2013 IEEE SDN for, pp.1-7, 2013. ,
A scalable multidatacenter layer-2 network architecture, Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research, 2015. ,
Opendaylight : Towards a model-driven sdn controller architecture, World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp.1-6, 2014. ,
Onos : towards an open, distributed sdn os, Proceedings of the third workshop on Hot topics in software defined networking, pp.1-6, 2014. ,
Sla monitoring and management framework for telecommunication services, Fourth International Conference on, pp.170-175, 2008. ,
Call level service differentiation for efficient sla management, Global Telecommunications Conference, 2005. GLOBECOM'05. IEEE, vol.2, p.6, 2005. ,
Fresco : a web services based framework for configuring extensible sla management systems, Web Services, 2005. ICWS 2005. Proceedings. 2005 IEEE International Conference on, pp.237-245, 2005. ,
Sla managementchallenges in the context of web-service-based infrastructures, Proceedings. IEEE International Conference on, pp.606-613, 2004. ,
Sla representation, management and enforcement, e-Technology, e-Commerce and e-Service, 2005. EEE'05. Proceedings, pp.158-163, 2005. ,
A wsla-based monitoring system for grid service-gsmon, Proceedings. 2004 IEEE International Conference on, pp.596-599, 2004. ,
Web services agreement specification (ws-agreement), Open grid forum, vol.128, p.216, 2007. ,
Web services policy framework (ws-policy), Sonic Software, VeriSign, 2004. ,
Service level agreement in cloud computing, 2009. ,
Towards autonomic detection of sla violations in cloud infrastructures, Future Generation Computer Systems, vol.28, issue.7, pp.1017-1029, 2012. ,
DOI : 10.1016/j.future.2011.08.018
Predictive auto-scaling techniques for clouds subjected to requests with service level agreements, IEEE World Congress on Services (SERVICES), pp.311-318, 2015. ,
DOI : 10.1109/services.2015.54
Predicting service metrics for clusterbased services using real-time analytics, 11th International Conference on Network and Service Management (CNSM), pp.135-143, 2015. ,
DOI : 10.1109/cnsm.2015.7367349
URL : http://kth.diva-portal.org/smash/get/diva2:849585/FULLTEXT01
Conceptual sla framework for cloud computing, Digital Ecosystems and Technologies (DEST), pp.606-610, 2010. ,
DOI : 10.1109/dest.2010.5610586
Csla : a language for improving cloud sla management, International Conference on Cloud Computing and Services Science, pp.586-591, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00675077
Cloud4soa : A semantic-interoperability paas solution for multicloud platform management and portability, European Conference on Service-Oriented and Cloud Computing, pp.64-78, 2013. ,
DOI : 10.1007/978-3-642-40651-5_6
Decision model for cloud computing under sla constraints, Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2010. ,
DOI : 10.1109/mascots.2010.34
URL : https://hal.archives-ouvertes.fr/hal-00788868
, IEEE International Symposium on, pp.257-266, 2010.
SLALOM european project, 2016. ,
Support vector regression for service level agreement violation prediction, Computer, Control, Informatics and Its Applications (IC3INA), 2013 International Conference on, pp.307-311, 2013. ,
ioverbook : intelligent resourceoverbooking to support soft real-time applications in the cloud, IEEE 7th international conference on Cloud computing (CLOUD), pp.538-545, 2014. ,
, Cloud providers adoption assessment d4.2, 2016.
Cloud providers adoption assessment d4.2, 2016. ,
, T-NOVA project website | t-NOVA, FP7, ICT, network functions VIrtualisation
An integrating framework for efficient nfv monitoring, NetSoft Conference and Workshops (NetSoft), pp.1-5, 2016. ,
A qos assured network service chaining algorithm in network function virtualization architecture, Cluster, Cloud and Grid Computing (CCGrid), pp.1221-1224, 2015. ,
Topology-aware prediction of virtual network function resource requirements, IEEE Transactions on Network and Service Management, 2017. ,
Understanding disruptive monitoring capabilities of programmable networks, Network Softwarization (NetSoft), 2017 IEEE Conference on, pp.1-6, 2017. ,
DOI : 10.1109/netsoft.2017.8004248
URL : https://hal.archives-ouvertes.fr/hal-01636117
Automated generation of vnf deployment rules using infrastructure affinity characterization, IEEE NetSoft Conference and Workshops (NetSoft), pp.226-233, 2016. ,
DOI : 10.1109/netsoft.2016.7502417
Network function virtualization : Challenges and opportunities for innovations, IEEE Communications Magazine, vol.53, issue.2, pp.90-97, 2015. ,
DOI : 10.1109/mcom.2015.7045396
Detection and prevention of dos attacks in software-defined cloud networks, Dependable and Secure Computing, 2017 IEEE Conference on, pp.217-223, 2017. ,
Mitigating dns query-based ddos attacks with machine learning on software-defined networking ,
DOI : 10.1109/milcom.2017.8170802
vnmf : Distributed fault detection using clustering approach for network function virtualization, p.2015 ,
DOI : 10.1109/inm.2015.7140349
, IFIP/IEEE International Symposium on, pp.640-645, 2015.
Forecasting with artificial neural networks : : The state of the art, International journal of forecasting, vol.14, issue.1, pp.35-62, 1998. ,
Technical target setting in qfd for web service systems using an artificial neural network, IEEE Transactions on Services Computing, vol.3, issue.4, pp.338-352, 2010. ,
, Prometheus-monitoring system and time series database, pp.2018-2021
Monitoring of sla compliances for hosted streaming services, Integrated Network Management, 2009. IM'09. IFIP/IEEE International Symposium on, pp.251-258, 2009. ,
, Welcome to clearwater, 2016.
Towards black-box anomaly detection in virtual network functions, 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop, pp.254-257, 2016. ,
DOI : 10.1109/dsn-w.2016.17
URL : https://hal.archives-ouvertes.fr/hal-01419016
High available deployment of cloud-based virtualized network functions, International Conference on High Performance Computing & Simulation (HPCS), pp.468-475, 2016. ,
, Welcome to sipp, 2016.
, Stress-ng, 2016.
, Welcome to clearwater-project clearwater 1.0 documentation, pp.2018-2021
, Monasca-monitoring at scale
Kafka : A distributed messaging system for log processing, Proceedings of the NetDB, pp.1-7, 2011. ,
Multivariate analysis, 2002. ,
URL : https://hal.archives-ouvertes.fr/hal-00296049
Reducing the dimensionality of data with neural networks, science, vol.313, issue.5786, pp.504-507, 2006. ,
Visualizing data using t-sne, Journal of machine learning research, vol.9, pp.2579-2605, 2008. ,
An introduction to variable and feature selection, Journal of machine learning research, vol.3, pp.1157-1182, 2003. ,
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, chine intelligence, vol.27, pp.1226-1238, 2005. ,
Tensorflow : A system for large-scale machine learning, OSDI, vol.16, pp.265-283, 2016. ,
Improving management of distributed services using correlations and predictions in sla-driven cloud computing systems, IEEE Network Operations and Management Symposium (NOMS), pp.1-8, 2014. ,
Sla enforcement in programmable networks, 9th International Conference on Autonomous Infrastructure, 2015. ,
Framewise phoneme classification with bidirectional lstm and other neural network architectures, Neural Networks, vol.18, issue.5, pp.602-610, 2005. ,
Dropout improves recurrent neural networks for handwriting recognition, 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp.285-290, 2014. ,
Forecasting and anticipating slo breaches in programmable networks, Innovations in Clouds, Internet and Networks (ICIN), pp.127-134, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01531499
An empirical evaluation of deep architectures on problems with many factors of variation, Proceedings of the 24th international conference on Machine learning, pp.473-480, 2007. ,
Random search for hyper-parameter optimization, Journal of Machine Learning Research, vol.13, pp.281-305, 2012. ,
Algorithms for hyper-parameter optimization, Advances in Neural Information Processing Systems, pp.2546-2554, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00642998
, Tensorflow-an open source software library for machine intelligence, 2016.
Introduction to the special issue on meta-learning, Machine learning, vol.54, issue.3, pp.187-193, 2004. ,
Neural architecture search with reinforcement learning, 2016. ,