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Found 7 papers, 1 papers with code
Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects
A wide variety of orthographic coding schemes and models of visual word identification have been developed to account for masked priming data that provide a measure of orthographic similarity between letter strings.
The role of Disentanglement in Generalisation
It is claimed that such representations should be able to capture the compositional structure of the world which can then be combined to produce novel representations.
A case for robust translation tolerance in humans and CNNs. A commentary on Han et al
Han et al. (2020) reported a behavioral experiment that assessed the extent to which the human visual system can identify novel images at unseen retinal locations (what the authors call "intrinsic translation invariance") and developed a novel convolutional neural network model (an Eccentricity Dependent Network or ENN) to capture key aspects of the behavioral results.
Priorless Recurrent Networks Learn Curiously
Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs.
Learning Translation Invariance in CNNs
In this work we show how, even though CNNs are not 'architecturally invariant' to translation, they can indeed 'learn' to be invariant to translation.
What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation
Convolutional neural networks (CNNs) were inspired by human vision and, in some settings, achieve a performance comparable to human object recognition.
Training neural networks to encode symbols enables combinatorial generalization
Combinatorial generalization - the ability to understand and produce novel combinations of already familiar elements - is considered to be a core capacity of the human mind and a major challenge to neural network models.
