Jeffrey S. Bowers

According to our database, Jeffrey S. Bowers authored at least 32 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

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

Guillermo Puebla, Jeffrey S. Bowers

CoRR, 2024

2023

On the importance of severely testing deep learning models of cognition.

Jeffrey S. Bowers, Gaurav Malhotra, Federico Adolfi, Marin Dujmovic, Milton Llera Montero, Valerio Biscione, Guillermo Puebla, John E. Hummel, Rachel Heaton

Cogn. Syst. Res., December, 2023

Successes and critical failures of neural networks in capturing human-like speech recognition.

Federico Adolfi, Jeffrey S. Bowers, David Poeppel

Neural Networks, May, 2023

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

Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers

Neural Networks, April, 2023

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

Guillermo Puebla, Jeffrey S. Bowers

CoRR, 2023

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

Dong Yin, Valerio Biscione, Jeffrey S. Bowers

CoRR, 2023

2022

Feature blindness: A challenge for understanding and modelling visual object recognition.

Gaurav Malhotra, Marin Dujmovic, Jeffrey S. Bowers

PLoS Comput. Biol., 2022

Biological convolutions improve DNN robustness to noise and generalisation.

Benjamin D. Evans, Gaurav Malhotra, Jeffrey S. Bowers

Neural Networks, 2022

Learning online visual invariances for novel objects via supervised and self-supervised training.

Valerio Biscione, Jeffrey S. Bowers

Neural Networks, 2022

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

Milton Llera Montero, Jeffrey S. Bowers, Rui Ponte Costa, Casimir J. H. Ludwig, Gaurav Malhotra

CoRR, 2022

Do DNNs trained on Natural Images acquire Gestalt Properties?

Valerio Biscione, Jeffrey S. Bowers

CoRR, 2022

Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation.

Milton Llera Montero, Jeffrey S. Bowers, Rui Ponte Costa, Casimir J. H. Ludwig, Gaurav Malhotra

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.

Valerio Biscione, Jeffrey S. Bowers

J. Mach. Learn. Res., 2021

Generalisation in Neural Networks Does not Require Feature Overlap.

Jeff Mitchell, Jeffrey S. Bowers

CoRR, 2021

The role of Disentanglement in Generalisation.

Milton Llera Montero, Casimir J. H. Ludwig, Rui Ponte Costa, Gaurav Malhotra, Jeffrey S. Bowers

Proceedings of the 9th International Conference on Learning Representations, 2021

Can Deep Convolutional Neural Networks Learn Same-Different Relations?

Guillermo Puebla, Jeffrey S. Bowers

Proceedings of the 43th Annual Meeting of the Cognitive Science Society, 2021

2020

Learning Translation Invariance in CNNs.

Valerio Biscione, Jeffrey S. Bowers

CoRR, 2020

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

Ella M. Gale, Nicholas Martin, Ryan Blything, Anh Nguyen, Jeffrey S. Bowers

CoRR, 2020

Harnessing the Symmetry of Convolutions for Systematic Generalisation.

Jeff Mitchell, Jeffrey S. Bowers

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Priorless Recurrent Networks Learn Curiously.

Jeff Mitchell, Jeffrey S. Bowers

Proceedings of the 28th International Conference on Computational Linguistics, 2020

Adding biological constraints to deep neural networks reduces their capacity to learn unstructured data.

Christian Tsvetkov, Gaurav Malhotra, Benjamin Evans, Jeffrey S. Bowers

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

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

Ivan Vankov, Jeffrey S. Bowers

CoRR, 2019

The contrasting roles of shape in human vision and convolutional neural networks.

Gaurav Malhotra, Jeffrey S. Bowers

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.

Ella Gale, Ryan Blything, Nicholas Martin, Jeffrey S. Bowers, Anh Nguyen

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Translation Tolerance in Vision.

Ryan Blything, Ivan Vankov, Casimir J. H. Ludwig, Jeffrey S. Bowers

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

2018

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

Ella M. Gale, Nicolas Martin, Jeffrey S. Bowers

CoRR, 2018

2017

Using single unit recordings in PDP and localist models to better understand how knowledge is coded in the cortex.

Jeffrey S. Bowers

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2013

When do PDP neural networks learn localist representations?

Ivan Vankov, Jeffrey S. Bowers

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

Do voices survive lexical consolidation?

Nicolas Dumay, Jeffrey S. Bowers

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

The Effect of Test Format on Visual Recognition Memory Performance.

Nora Andermane, Jeffrey S. Bowers

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?

Jeffrey S. Bowers

Connect. Sci., 2011

2009

Learning Representations of Wordforms With Recurrent Networks: Comment on.

Jeffrey S. Bowers, Colin J. Davis

Cogn. Sci., 2009