• Refine your search:
  • 
    Keyword:
    
    • Computation and Language cs.CL (6)
    • FOS Computer and information sciences (6)
    • Machine Learning cs.LG (5)
    • Artificial Intelligence cs.AI (4)
    • Neural and Evolutionary Computing cs.NE (4)
    • Machine Learning stat.ML (2)
    • 68T07, 68T45, 68T50 (1)
    • Computer Vision and Pattern Recognition cs.CV (1)
    • I.2.7; I.2.10; I.5.1 (1)
  • 
    Creator / Publisher
  • 
    Year
  • 
    Medium
  • 
    Type
  • 
    BLLDB-Access

Search in the Catalogues and Directories

All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ... Bhardwaj, Peru; Kelleher, John; Costabello, Luca. - : arXiv, 2021 BASE Show details 2 Language-Driven Region Pointer Advancement for Controllable Image Captioning ... Lindh, Annika; Ross, Robert J.; Kelleher, John D.. - : arXiv, 2020 BASE Show details 3 Semantic Relatedness and Taxonomic Word Embeddings ... Kacmajor, Magdalena; Kelleher, John D.; Klubicka, Filip. - : arXiv, 2020 BASE Show details 4 Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing ... Dobnik, Simon; Kelleher, John D.. - : arXiv, 2018 Abstract: Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition. ... : 18 pages, 1 figure, Appears in CLASP Papers in Computational Linguistics Vol. 1: Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017) ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML; Neural and Evolutionary Computing cs.NE URL: https://dx.doi.org/10.48550/arxiv.1807.09844 https://arxiv.org/abs/1807.09844 BASE Hide details 5 What is not where: the challenge of integrating spatial representations into deep learning architectures ... Kelleher, John D.; Dobnik, Simon. - : arXiv, 2018 BASE Show details 6 Is it worth it? Budget-related evaluation metrics for model selection ... Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018 BASE Show details

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 0 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 0 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 6 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0