24 citations · 51 across the 6 of their papers we have counts for
6 papers
Modelling Compositionality and Structure Dependence in Natural Language
Karthikeya Ramesh Kaushik, Andrea E. Martin
Human beings possess the most sophisticated computational machinery in the known universe. We can understand language of rich descriptive power, and communicate in the same environ…
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?
Maryam Hashemzadeh, Greta Kaufeld, Martha White +2
The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. Howev…
A Theory of Relation Learning and Cross-domain Generalization
Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin +1
People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in…
The relational processing limits of classic and contemporary neural network models of language processing
Guillermo Puebla, Andrea E. Martin, Leonidas A. A. Doumas
The ability of neural networks to capture relational knowledge is a matter of long-standing controversy. Recently, some researchers in the PDP side of the debate have argued that (…
Predicate learning in neural systems: Discovering latent generative structures
Andrea E. Martin, Leonidas A. A. Doumas
Humans learn complex latent structures from their environments (e.g., natural language, mathematics, music, social hierarchies). In cognitive science and cognitive neuroscience, mo…
Human-like generalization in a machine through predicate learning
Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin
Humans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning and artificial intelligence have begun to approximate and even sur…