The DiCarlo Lab at MIT

Lee, Michael J.

2024
Ferguson, Michael E.
Jerry Ngo
Lee, Michael J.
James J. DiCarlo
Martin Schrimpf
How Well is Visual Search Asymmetry predicted by a Binary-Choice, Rapid, Accuracy-based Visual-search, Oddball-detection (BRAVO) task?.

Abstract: How Well is Visual Search Asymmetry predicted by a Binary-Choice, Rapid, Accuracy-based Visual-search, Oddball-detection (BRAVO) task?.
2023
DiCarlo, James J.
Lee, Michael J.
Kietzmann, Tim Christian
How well do rudimentary plasticity rules predict adult visual object learning?

In:
PLOS Computational Biology

Available At:
https://dx.plos.org/10.1371/journal.pcbi.1011713

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Abstract: How well do rudimentary plasticity rules predict adult visual object learning?
2023
Lee, Michael J.
DiCarlo, James J.
Kietzmann, Tim Christian
How well do rudimentary plasticity rules predict adult visual object learning?

In:
PLOS Computational Biology

Available At:
https://dx.plos.org/10.1371/journal.pcbi.1011713

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Abstract: How well do rudimentary plasticity rules predict adult visual object learning?
2023
Gaziv, Guy
Lee, Michael J.
DiCarlo, James J.
Strong and Precise Modulation of Human Percepts via Robustified ANNs

In:
Neural Information Processing Systems

Available At:
https://openreview.net/pdf?id=5GmTI4LNqX

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Abstract: Strong and Precise Modulation of Human Percepts via Robustified ANNs
2023
Gaziv, Guy
Lee, Michael J.
DiCarlo, James J.
Robustified ANNs Reveal Wormholes Between Human Category Percepts

In:
arXiv

Available At:
https://arxiv.org/pdf/2308.06887.pdf

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Abstract: Robustified ANNs Reveal Wormholes Between Human Category Percepts
2023
Lee, Michael J.
DiCarlo, James J.
An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models

In:
bioRxiv

Available At:
https://www.biorxiv.org/content/biorxiv/early/2023/01/02/2022.12.31.522402.full.pdf

Abstract: An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models
2022
Guo, Chong
Lee, Michael J.
Leclerc, Guillaume
Dapello, Joel
Rao, Yug
Madry, Aleksander
DiCarlo, James J.
Adversarially trained neural representations may already be as robust as corresponding biological neural representations

In:
arXiv

Available At:
https://arxiv.org/abs/2206.11228v1

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Abstract: Adversarially trained neural representations may already be as robust as corresponding biological neural representations
2019
Lee, Michael J.
DiCarlo, James J.
Comparing novel object learning in humans, models, and monkeys

In:
Journal of Vision

Available At:
https://jov.arvojournals.org/article.aspx?articleid=2750359

Abstract: Comparing novel object learning in humans, models, and monkeys
2020
Schrimpf, Martin
Kubilius, Jonas
Lee, Michael J.
Murty, NAR
Ajemian, Robert
DiCarlo, James J.
Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence

In:
Neuron

Available At:
https://linkinghub.elsevier.com/retrieve/pii/S089662732030605X

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Abstract: Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence