IJCNN 2019 Program May 20, 2019 1 Sunday, July 14th, 2019 Time Panorama I Sofitel Bellevue 1 Sofitel Bellevue 2 Sofitel Bellevue 3 8:00AM Tut1: Physics of the Tut2: Modern Tut3: Mind Gaussian Processes: Task-Independent and Scalable Inference Modality-Independent and Novel Developmental Applications Learning Engines: From Theory to Programming (*) 10:00AM Coffee Break 10:20AM W2 (Begins at 10am!) Tut4: Beyond Deep Tut5: Deep Learning Tut6: Theory and W2: Computational Learning: How to get for Graphs Methodology of Sport Science: Fast, Interpretable and Transfer Learning Human Motion Highly Accurate Modelling and Classifiers Analysis 12:20PM Lunch (on your own) 1:30PM W2 continued Tut7: Deep Learning: Tut8: Machine Tut9: Universal Turing Artificial Neural Learning methods in Machines and How Networks and Kernel Spiking Neural They Emerge from DN based Models Networks for Network classification problems 3:30PM Coffee Break 3:50PM W2 continued Tut10: Tensor Tut12: Non-Iterative Decompositions for Learning Methods for Big Data Analytics: Classification and Trends and Forecasting Applications 4:00PM Tut 10: Continued Tut 12: Continued 5:50PM End of Day Monday, July 15th, 2019 Time Ballroom I Ballroom II Ballroom Duna Duna Duna Panorama Panorama Panorama Panorama Panorama III Salon I Salon II Salon III I II III IV V 8:10AM D1 BIa: 1l: D1 BIIa: D1 BIIIa: D1 DIa: D1 DIIa: D1 DIIIa: D1 PIa: D1 PIIa: D1 PIIIa: D1 PIVa: Comp1: Deep 2e: Deep 8a: Appli- 1h: Spiking 1n: Other 2a: 1a: Feed- 1l: Deep Neural S01: Challenge neural learning cations of neural topics in Supervised forward neural Network Information UP: networks, deep networks artificial learning neural networks, Models Theory Multimodal Cellular networks neural networks Cellular and Deep Fall Computa- networks Computa- Learning Detection tional tional Networks Networks 9:30AM Coffee Break 10:00AM Plenary Session – Ple1: Isabelle Guyon, IRI France : Ballroom I+II+III 11:00AM Plenary Session – Ple2: Ichiro Tsuda, Chubu University : Ballroom I+II+III 12:00PM Lunch (on your own) 1:30PM D1 BIb: 1l: D1 BIIb: D1 BIIIb: D1 DIb: D1 DIIb: D1 DIIIb: D1 PIb: D1 PIIb: D1 PIIIb: D1 PIVb: Comp2: Deep 2e: Deep 8a: Appli- 1b: 2a: 2b: Unsu- 1b: 1c: Self- S31: 1a: Feed- L2RPN: neural learning cations of Recurrent Supervised pervised Recurrent organizing Intelligent forward Learning to networks, deep neural learning learning neural maps Vehicle neural run a Cellular networks networks and networks (including and Trans- networks, power Computa- clustering, neural gas, portation 2k, 2m network tional (including etc.) Systems Networks PCA, and and Other ICA) Applica- tions 3:30PM Coffee Break 4:00PM Plenary Session – Ple8: Erkki Oja, Aalto University, School of Science and Technology. : Ballroom I+II+III 5:00PM Break 5:30PM D1 BIc: 1l: D1 BIIc: D1 BIIIc: D1 DIc: D1 DIIc: D1 DIIIc: D1 PIc: D1 PIIc: D1 PIIIc: D1 PIVc: Pan1: Deep 2e: Deep 8a: Appli- 1h: Spiking 2a: 2f: Online 2e: Deep 8a: Appli- 1g: Fuzzy S24: Funding neural learning cations of neural Supervised learning learning cations of Neural Evolving Opportuni- networks, deep networks learning deep Networks Machine ties in Cellular networks networks Learning Neural Computa- and Deep Networks tional Learning and Networks Models for Biologically Computer Inspired AI Vision Research 7:30PM End of Day 2 Tuesday, July 16th, 2019 Time Ballroom I Ballroom II Ballroom Duna Duna Duna Panorama Panorama Panorama Panorama Panorama III Salon I Salon II Salon III I II III IV V 8:10AM D2 BIa: 1l: D2 BIIa: D2 BIIIa: D2 DIa: D2 DIIa: D2 DIIIa: D2 PIa: D2 PIIa: D2 PIIIa: D2 PIVa: DocCon: Deep 2e: Deep 8a: Appli- 2c: Rein- 2d: Semi- S07: Neural 2d: Semi- 1l: Deep 2a: Doctoral neural learning cations of forcement supervised Advanced Network supervised neural Supervised Consor- networks, deep learning learning Machine Models learning networks, learning tium Cellular networks and Learning Cellular Computa- adaptive Methods Computa- tional dynamic for Big tional Networks program- Graph Networks ming Analytics 9:30AM Coffee Break 10:00AM Plenary Session – Ple4: Lee Giles, Pennsylvania State University : Ballroom I+II+III 11:00AM Plenary Session – Ple5: Wolf Singer, Ernst Strungmann Institute : Ballroom I+II+III 12:00PM Lunch (on your own) — Meet the Experts Lunch (in Panorama V 1:30PM D2 BIb: 1l: D2 BIIb: D2 BIIIb: D2 DIb: 2t: D2 DIIb: D2 DIIIb: D2 PIb: D2 PIIb: D2 PIIIb: D2 PIVb: Comp3: Deep 2e: Deep 8a: Appli- Topics in Neuroengi- 8k: Signal 8a: Appli- 2e: Deep S03: 2p: AutoML neural learning cations of machine neering process- cations of learning Computa- Feature Rematch networks deep learning ing, image deep tional/Artificial selection, and networks process- networks Intelli- extraction, artificial ing, and gence in and aggre- neural multi- Earth, gation networks media Space, and Envi- ronmental Sciences 3:30PM Coffee Break 4:00PM Plenary Session – Ple6: Vera Kurkova, Institute of Computer science, Czech academy of sciences : Ballroom I+II+III 5:00PM Break 5:30PM D2 BIc: D2 BIIc: D2 BIIIc: D2 DIc: 2t: D2 DIIc: D2 DIIIc: D2 PIc: D2 PIIc: D2 PIIIc: D2 PIVc: Pan2: NSF 1n: Other 2e: Deep 8a: Appli- Topics in Neuroengi- 8k: Signal Computa- Neural 8l: Neural Career topics in learning cations of machine neering process- tional Models of Temporal Models of Award artificial deep learning and Bio- ing, image Neuro- Perception, data Perception, Winners in neural networks inspired process- science Cognition analysis, Cognition Intelligent networks Systems ing, and and Action prediction, and Neuro- and multi- and fore- dynamics Adaptive media casting; Systems time series analysis 7:30PM End of Day Wednesday, July 17th, 2019 Time Ballroom I Ballroom II Ballroom Duna Duna Duna Panorama Panorama Panorama Panorama Panorama III Salon I Salon II Salon III I II III IV V 8:00AM D3 BIa: D3 BIIa: D3 BIIIa: D3 DIa: D3 DIIa: D3 DIIIa: D3 PIa: D3 PIIa: D3 PIIIa: D3 PIVa: Comp4: S11: S12: S15: S06: Deep 8l: 8: Other S10: Deep 2c: Rein- S18: S05: Deep AIML Learning Automatic Machine and Temporal Applica- learning for forcement Neuro- Neural Contest Represen- Machine Learning Generative data tions brain data, learning Inspired Audio 2019 tations for Learning and Deep Adversarial analysis, S14: Evo- and Computing Processing Structured and S13: Learning Learning prediction, lutionary adaptive with Nano- Data Extreme Methods and fore- NN dynamic electronic Learning applied to casting; program- Devices Machines Vision and time series ming (ELM) Robotics analysis (MLDLMVR) 10:00AM Coffee Break 10:30AM Plenary Session – Ple7: Nik Kasabov, KEDRI, Auckland University of Technology : Ballroom I+II+III 11:30AM Plenary Session – Ple3: Danil Prokhorov, Toyota R&D : Ballroom I+II+III 12:30PM Lunch (on your own) — IEEE TNNLS Lunch (in Panorama V 2:00PM D3 BIb: D3 BIIb: D3 BIIIb: D3 DIb: D3 DIIb: D3 DIIIb: D3 PIb: D3 PIIb: D3 PIIIb: D3 PIVb: Pan3: S09: S22: Deep 8n: Data S08: 8: Other 8a: Appli- Machine 2i: Support Neural Deep Metrology Artificial In- Reinforce- mining and Dynamics, Applica- cations of Learning vector Models of Learning: of AI: telligence ment knowledge Applica- tions deep and Deep machines Perception, Hype or blessing of and Learning discovery tions, and networks Learning and kernel Cognition Hallelujah? dimension- Security for Au- Hardware methods, and Action ality, (AISE) tonomous Implemen- 2: ML tolerance Driving tation of and fits Reservoir Computing 4:00PM Coffee Break 4:30PM Plenary Session – Ple9: Adam Miklosi, Eotvos Lorand University, Budapest : Ballroom I+II+III 5:30PM Break 7:30PM Banquet and Award Ceremony (Room TBA) 11:00PM End of Day 3 Thursday, July 18th, 2019 Time Ballroom I Duna Duna Duna Panorama Panorama Panorama Panorama Panorama Sofitel Sofitel + II +II Salon I Salon II Salon III I II III IV V Bellevue 1 Bellevue 3 8:00AM POS1: D4 DIa: D4 DIIa: D4 DIIIa: D4 PIa: D4 PIIa: D4 PIIIa: D4 PIVa: D4 PVa: 8: Poster S25: S29: S30: Deep Applica- Extreme S17: Other Ap- Session 1 Artificial In- Biologically Exploring Learning tions and Learning Biologically plications telligence Inspired Uncertain- and Appli- Data Machines Inspired in Health Learning ties in Big cations Mining (ELM) and Computa- and for Data by Machine tional Medicine: Cognitive Neural Learning Vision and from Robotics Fuzzy S19: Theory to Systems Ensemble Applica- Learning tions and Appli- cations 9:40AM Coffee Break 10:00AM POS2: D4 DIb: D4 DIIb: D4 IIIb: D4 PIb: D4 PIIb: D4 PIIIb: D4 PIVb: D4 PVb: Poster S25: S29: 2b: Unsu- S07: Deep Neural S16: Ex- S32: Deep Session 2 Artificial In- Biologically pervised Advanced Learning Network plainable Reinforce- telligence Inspired learning Machine and Models Machine ment in Health Learning and Learning Algorithms Learning Learning and for clustering, Methods for Games Medicine: Cognitive (including for Big from Robotics PCA, and Graph Theory to and S02: ICA) Analytics Applica- Low Power tions and Hardware S27: Deep for Spiking Neural Neural image and Networks text processing 11:40AM Break 11:50AM POS3: D4 DIc: D4 DIIc: D4 DIIIc: D4 PIc: D4 PIIc: D4 PIIIc: D4 PIVc: D4 PVc: Poster S34: Mind, 8c: Bioin- 8e: Data Deep Machine Applica- S33: S32: Deep Session 3 Brain, and formatics analysis Learning Learning tions Transfer- Reinforce- Cognitive and Other and and Neural able neural ment Algorithms Applica- pattern Network models for Learning and Other tions recognition Models language for Games Cross- and Other under- Disciplinary Applica- standing; Topics tions Applica- tions 1:30PM Lunch (on your own) 2:30PM W1: W3: Advances Causality in Learning and from/with Dynamics Multiple in Brain Learners Networks (ALML) Learn more 6:30PM End of Day Friday, July 19th, 2019 Time Sofitel Bellevue 1 Sofitel Bellevue 2 Sofitel Bellevue 3 9:00AM W1 a: Advances in W4: Ethical AI W3 a: Causality and Learning from/with Challenges Dynamics in Brain Multiple Learners Networks (ALML) 1:00PM End of Day 4 IJCNN 2019 Program Sunday, July 14, 2019 Tutorial Tut1: Physics of the Mind Sunday, July 14, 8:00AM-10:00AM, Room: Sofitel Bellevue 1, Instructor: Leonid I.