24 citations · 65 across the 6 of their papers we have counts for
8 papers
Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment
Jaap Jumelet, Willem Zuidema, Dieuwke Hupkes
Extensive research has recently shown that recurrent neural language models are able to process a wide range of grammatical phenomena. How these models are able to perform these re…
Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains
Samira Abnar, Lisa Beinborn, Rochelle Choenni +1
In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models. ReStA is a variant of the popular representati…
Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization
Mathijs Mul, Willem Zuidema
Can neural nets learn logic? We approach this classic question with current methods, and demonstrate that recurrent neural networks can learn to recognize first order logical entai…
Formal models of Structure Building in Music, Language and Animal Songs
Willem Zuidema, Dieuwke Hupkes, Geraint Wiggins +2
Human language, music and a variety of animal vocalisations constitute ways of sonic communication that exhibit remarkable structural complexity. While the complexities of language…
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity
Samira Abnar, Rasyan Ahmed, Max Mijnheer +1
We evaluate 8 different word embedding models on their usefulness for predicting the neural activation patterns associated with concrete nouns. The models we consider include an ex…
Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs
Phong Le, Willem Zuidema
Recursive neural networks (RNN) and their recently proposed extension recursive long short term memory networks (RLSTM) are models that compute representations for sentences, by re…