Jeffrey S. Bowers

Orcid: 0000-0001-9558-5010

According to our database1, Jeffrey S. Bowers authored at least 34 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Adapting to time: why nature evolved a diverse set of neurons.
CoRR, 2024

MindSet: Vision. A toolbox for testing DNNs on key psychological experiments.
CoRR, 2024

Visual Reasoning in Object-Centric Deep Neural Networks: A Comparative Cognition Approach.
CoRR, 2024

2023
On the importance of severely testing deep learning models of cognition.
Cogn. Syst. Res., December, 2023

Successes and critical failures of neural networks in capturing human-like speech recognition.
Neural Networks, May, 2023

The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data.
Neural Networks, April, 2023

The role of object-centric representations, guided attention, and external memory on generalizing visual relations.
CoRR, 2023

Convolutional Neural Networks Trained to Identify Words Provide a Good Account of Visual Form Priming Effects.
CoRR, 2023

2022
Feature blindness: A challenge for understanding and modelling visual object recognition.
PLoS Comput. Biol., 2022

Biological convolutions improve DNN robustness to noise and generalisation.
Neural Networks, 2022

Learning online visual invariances for novel objects via supervised and self-supervised training.
Neural Networks, 2022

Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation.
CoRR, 2022

Do DNNs trained on Natural Images acquire Gestalt Properties?
CoRR, 2022

Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be.
J. Mach. Learn. Res., 2021

Generalisation in Neural Networks Does not Require Feature Overlap.
CoRR, 2021

The role of Disentanglement in Generalisation.
Proceedings of the 9th International Conference on Learning Representations, 2021

Can Deep Convolutional Neural Networks Learn Same-Different Relations?
Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021

2020
Learning Translation Invariance in CNNs.
CoRR, 2020

Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?
CoRR, 2020

Harnessing the Symmetry of Convolutions for Systematic Generalisation.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Priorless Recurrent Networks Learn Curiously.
Proceedings of the 28th International Conference on Computational Linguistics, 2020

Adding biological constraints to deep neural networks reduces their capacity to learn unstructured data.
Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019
Out-of-the box neural networks can support combinatorial generalization.
CoRR, 2019

The contrasting roles of shape in human vision and convolutional neural networks.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Selectivity metrics provide misleading estimates of the selectivity of single units in neural networks.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Translation Tolerance in Vision.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

2018
When and where do feed-forward neural networks learn localist representations?
CoRR, 2018

2017
Using single unit recordings in PDP and localist models to better understand how knowledge is coded in the cortex.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2013
When do PDP neural networks learn localist representations?
Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

Do voices survive lexical consolidation?
Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

The Effect of Test Format on Visual Recognition Memory Performance.
Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

2011
What is a grandmother cell? And how would you know if you found one?
Connect. Sci., 2011

2009
Learning Representations of Wordforms With Recurrent Networks: Comment on.
Cogn. Sci., 2009


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