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Found 5 papers, 3 papers with code
Frequency effects in Linear Discriminative Learning
So far, the mappings can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modelling the theoretical endstate of learning (EL) where all words are learned optimally.
How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning
This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision.
How direct is the link between words and images?
To what extent does this setup rely on visual information from images?
Language with Vision: a Study on Grounded Word and Sentence Embeddings
Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora.
Modeling morphology with Linear Discriminative Learning: considerations and design choices
This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning.
