FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2990498109> ?p ?o ?g. }

• W2990498109 abstract "Imitation learning is an efficient paradigm for teaching and controlling intelligent autonomous cars. Obtaining a set of suitable demonstrations to learn an end-to-end policy from raw pixels is a challenging task in imitation learning problems. Deep neural networks have recently shown outstanding results in learning from raw high dimensional data for solving a wide range of real-world applications. The success of deep neural networks depends on finding suitable hyperparameters for constructing network architecture. Besides, designing hand-crafted deep architectures is not an efficient way for achieving the best performance. To address this issue, this paper performs a neuro-evolution method based on genetic algorithm for finding the optimal deep neural networks architecture in terms of hyperparameters. The experimental results show the effectiveness of the proposed approach for training an autonomous vehicle." @default.
• W2990498109 created "2019-12-05" @default.
• W2990498109 creator A5008390039 @default.
• W2990498109 creator A5015293969 @default.
• W2990498109 creator A5059557438 @default.
• W2990498109 creator A5077932626 @default.
• W2990498109 creator A5088897776 @default.
• W2990498109 creator A5090193033 @default.
• W2990498109 date "2019-10-01" @default.
• W2990498109 modified "2023-09-27" @default.
• W2990498109 title "Optimal Autonomous Driving Through Deep Imitation Learning and Neuroevolution" @default.
• W2990498109 cites W1976744965 @default.
• W2990498109 cites W1985762823 @default.
• W2990498109 cites W1993398125 @default.
• W2990498109 cites W2018328737 @default.
• W2990498109 cites W2028559543 @default.
• W2990498109 cites W2031183907 @default.
• W2990498109 cites W2061438946 @default.
• W2990498109 cites W2065750810 @default.
• W2990498109 cites W2106845860 @default.
• W2990498109 cites W2197222329 @default.
• W2990498109 cites W2210472625 @default.
• W2990498109 cites W2250904038 @default.
• W2990498109 cites W2258585808 @default.
• W2990498109 cites W2440397727 @default.
• W2990498109 cites W2510924756 @default.
• W2990498109 cites W2512479688 @default.
• W2990498109 cites W2560557936 @default.
• W2990498109 cites W2593744649 @default.
• W2990498109 cites W2604319603 @default.
• W2990498109 cites W2606006859 @default.
• W2990498109 cites W2622826443 @default.
• W2990498109 cites W2624707650 @default.
• W2990498109 cites W2625957737 @default.
• W2990498109 cites W2674501427 @default.
• W2990498109 cites W2689604496 @default.
• W2990498109 cites W2768896610 @default.
• W2990498109 cites W2790858115 @default.
• W2990498109 cites W2791655422 @default.
• W2990498109 cites W2797347945 @default.
• W2990498109 cites W2807414627 @default.
• W2990498109 cites W2807809851 @default.
• W2990498109 cites W2807874563 @default.
• W2990498109 cites W2892028675 @default.
• W2990498109 cites W2892341857 @default.
• W2990498109 cites W2895639546 @default.
• W2990498109 cites W2897737355 @default.
• W2990498109 cites W2900605005 @default.
• W2990498109 cites W2901282250 @default.
• W2990498109 cites W2903891611 @default.
• W2990498109 cites W2908768031 @default.
• W2990498109 cites W2910390795 @default.
• W2990498109 cites W2955547492 @default.
• W2990498109 cites W2963327228 @default.
• W2990498109 cites W2963665412 @default.
• W2990498109 cites W2963669336 @default.
• W2990498109 cites W2963906250 @default.
• W2990498109 cites W2989608526 @default.
• W2990498109 cites W2990377129 @default.
• W2990498109 cites W4249439221 @default.
• W2990498109 cites W4249718223 @default.
• W2990498109 cites W4252049996 @default.
• W2990498109 cites W4253033325 @default.
• W2990498109 cites W4255158661 @default.
• W2990498109 cites W4289765287 @default.
• W2990498109 cites W806171748 @default.
• W2990498109 cites W883434633 @default.
• W2990498109 doi "https://doi.org/10.1109/smc.2019.8914582" @default.
• W2990498109 hasPublicationYear "2019" @default.
• W2990498109 type Work @default.
• W2990498109 sameAs 2990498109 @default.
• W2990498109 citedByCount "18" @default.
• W2990498109 countsByYear W29904981092020 @default.
• W2990498109 countsByYear W29904981092021 @default.
• W2990498109 countsByYear W29904981092022 @default.
• W2990498109 crossrefType "proceedings-article" @default.
• W2990498109 hasAuthorship W2990498109A5008390039 @default.
• W2990498109 hasAuthorship W2990498109A5015293969 @default.
• W2990498109 hasAuthorship W2990498109A5059557438 @default.
• W2990498109 hasAuthorship W2990498109A5077932626 @default.
• W2990498109 hasAuthorship W2990498109A5088897776 @default.
• W2990498109 hasAuthorship W2990498109A5090193033 @default.
• W2990498109 hasConcept C108583219 @default.
• W2990498109 hasConcept C118070581 @default.
• W2990498109 hasConcept C119857082 @default.
• W2990498109 hasConcept C123657996 @default.
• W2990498109 hasConcept C126388530 @default.
• W2990498109 hasConcept C127413603 @default.
• W2990498109 hasConcept C142362112 @default.
• W2990498109 hasConcept C146978453 @default.
• W2990498109 hasConcept C153349607 @default.
• W2990498109 hasConcept C154945302 @default.
• W2990498109 hasConcept C15744967 @default.
• W2990498109 hasConcept C177264268 @default.
• W2990498109 hasConcept C199360897 @default.
• W2990498109 hasConcept C201995342 @default.
• W2990498109 hasConcept C204323151 @default.
• W2990498109 hasConcept C2780451532 @default.
• W2990498109 hasConcept C2984842247 @default.
• W2990498109 hasConcept C41008148 @default.

• next