Results 31 to 40 of about 5,397 (57)
Deep Learning: Our Miraculous Year 1990-1991
, 2020 In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich.
Schmidhuber, Juergen
core
ROC Curves within the Framework of Neural Network Assembly Memory Model: Some Analytic Results [PDF]
, 2003 On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically.
Gopych, Petro
core
Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML. [PDF]
J Big Data, 2022 Siriborvornratanakul T.
europepmc +1 more source
CHR as grammar formalism. A first report
, 2001 Grammars written as Constraint Handling Rules (CHR) can be executed as efficient and robust bottom-up parsers that provide a straightforward, non-backtracking treatment of ambiguity.
Christiansen, Henning
core
Cross-lingual, Character-Level Neural Morphological Tagging
Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to
Cotterell, Ryan, Heigold, Georg
core +1 more source
Learning Term Weights for Ad-hoc Retrieval
, 2016 Most Information Retrieval models compute the relevance score of a document for a given query by summing term weights specific to a document or a query. Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used to specify how a term
Piwowarski, B.
core +1 more source
Exact Hard Monotonic Attention for Character-Level Transduction
Many common character-level, string-to string transduction tasks, e.g., grapheme-tophoneme conversion and morphological inflection, consist almost exclusively of monotonic transductions.
Cotterell, Ryan, Wu, Shijie
core
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