FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2973122905> ?p ?o ?g. }

• W2973122905 abstract "In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in our experiments are designed to be small-sized networks both to prevent them from memorizing the training sets and to visualize and interpret their behaviour at test time. Our results demonstrate that the Long Short-Term Memory (LSTM) networks can learn to recognize the well-balanced parenthesis language (Dyck-1) and the shuffles of multiple Dyck-1 languages, each defined over different parenthesis-pairs, by emulating simple real-time k-counter machines. To the best of our knowledge, this work is the first study to introduce the shuffle languages to analyze the computational power of neural networks. We also show that a single-layer LSTM with only one hidden unit is practically sufficient for recognizing the Dyck-1 language. However, none of our recurrent networks was able to yield a good performance on the Dyck-2 language learning task, which requires a model to have a stack-like mechanism for recognition." @default.
• W2973122905 created "2019-09-19" @default.
• W2973122905 creator A5009980324 @default.
• W2973122905 creator A5026789962 @default.
• W2973122905 creator A5051184573 @default.
• W2973122905 creator A5053102850 @default.
• W2973122905 date "2019-01-01" @default.
• W2973122905 modified "2023-09-27" @default.
• W2973122905 title "LSTM Networks Can Perform Dynamic Counting" @default.
• W2973122905 cites W1562818191 @default.
• W2973122905 cites W1602017060 @default.
• W2973122905 cites W1732222442 @default.
• W2973122905 cites W179875071 @default.
• W2973122905 cites W1890888469 @default.
• W2973122905 cites W1969010420 @default.
• W2973122905 cites W2012140001 @default.
• W2973122905 cites W2024987067 @default.
• W2973122905 cites W2025665242 @default.
• W2973122905 cites W2056647002 @default.
• W2973122905 cites W2064675550 @default.
• W2973122905 cites W2067619114 @default.
• W2973122905 cites W2069143585 @default.
• W2973122905 cites W2079601390 @default.
• W2973122905 cites W2110485445 @default.
• W2973122905 cites W2115082275 @default.
• W2973122905 cites W2116723809 @default.
• W2973122905 cites W2157331557 @default.
• W2973122905 cites W2560826841 @default.
• W2973122905 cites W2561386235 @default.
• W2973122905 cites W2914557243 @default.
• W2973122905 cites W2950527759 @default.
• W2973122905 cites W2963059228 @default.
• W2973122905 cites W2963400886 @default.
• W2973122905 cites W2963423043 @default.
• W2973122905 cites W2963503967 @default.
• W2973122905 cites W2963723151 @default.
• W2973122905 cites W2963753324 @default.
• W2973122905 cites W2964121744 @default.
• W2973122905 doi "https://doi.org/10.18653/v1/w19-3905" @default.
• W2973122905 hasPublicationYear "2019" @default.
• W2973122905 type Work @default.
• W2973122905 sameAs 2973122905 @default.
• W2973122905 citedByCount "46" @default.
• W2973122905 countsByYear W29731229052019 @default.
• W2973122905 countsByYear W29731229052020 @default.
• W2973122905 countsByYear W29731229052021 @default.
• W2973122905 countsByYear W29731229052022 @default.
• W2973122905 countsByYear W29731229052023 @default.
• W2973122905 crossrefType "proceedings-article" @default.
• W2973122905 hasAuthorship W2973122905A5009980324 @default.
• W2973122905 hasAuthorship W2973122905A5026789962 @default.
• W2973122905 hasAuthorship W2973122905A5051184573 @default.
• W2973122905 hasAuthorship W2973122905A5053102850 @default.
• W2973122905 hasBestOaLocation W29731229051 @default.
• W2973122905 hasConcept C104317684 @default.
• W2973122905 hasConcept C111472728 @default.
• W2973122905 hasConcept C119857082 @default.
• W2973122905 hasConcept C137293760 @default.
• W2973122905 hasConcept C138885662 @default.
• W2973122905 hasConcept C145420912 @default.
• W2973122905 hasConcept C147168706 @default.
• W2973122905 hasConcept C154945302 @default.
• W2973122905 hasConcept C162324750 @default.
• W2973122905 hasConcept C178790620 @default.
• W2973122905 hasConcept C185592680 @default.
• W2973122905 hasConcept C187736073 @default.
• W2973122905 hasConcept C199360897 @default.
• W2973122905 hasConcept C204321447 @default.
• W2973122905 hasConcept C2776542307 @default.
• W2973122905 hasConcept C2779227376 @default.
• W2973122905 hasConcept C2780451532 @default.
• W2973122905 hasConcept C2780586882 @default.
• W2973122905 hasConcept C30038468 @default.
• W2973122905 hasConcept C33923547 @default.
• W2973122905 hasConcept C41008148 @default.
• W2973122905 hasConcept C41895202 @default.
• W2973122905 hasConcept C50644808 @default.
• W2973122905 hasConcept C55493867 @default.
• W2973122905 hasConcept C66746571 @default.
• W2973122905 hasConcept C80444323 @default.
• W2973122905 hasConcept C9395851 @default.
• W2973122905 hasConceptScore W2973122905C104317684 @default.
• W2973122905 hasConceptScore W2973122905C111472728 @default.
• W2973122905 hasConceptScore W2973122905C119857082 @default.
• W2973122905 hasConceptScore W2973122905C137293760 @default.
• W2973122905 hasConceptScore W2973122905C138885662 @default.
• W2973122905 hasConceptScore W2973122905C145420912 @default.
• W2973122905 hasConceptScore W2973122905C147168706 @default.
• W2973122905 hasConceptScore W2973122905C154945302 @default.
• W2973122905 hasConceptScore W2973122905C162324750 @default.
• W2973122905 hasConceptScore W2973122905C178790620 @default.
• W2973122905 hasConceptScore W2973122905C185592680 @default.
• W2973122905 hasConceptScore W2973122905C187736073 @default.
• W2973122905 hasConceptScore W2973122905C199360897 @default.
• W2973122905 hasConceptScore W2973122905C204321447 @default.
• W2973122905 hasConceptScore W2973122905C2776542307 @default.
• W2973122905 hasConceptScore W2973122905C2779227376 @default.
• W2973122905 hasConceptScore W2973122905C2780451532 @default.
• W2973122905 hasConceptScore W2973122905C2780586882 @default.
• W2973122905 hasConceptScore W2973122905C30038468 @default.

• next