James J. DiCarlo

发表

How far can you get with a modern face recognition test set using only simple features?

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Untangling invariant object

James J. DiCarlo, David Cox, 2007 .

Convolutional recurrent neural network models of dynamics in higher visual cortex

Surya Ganguli, Aran Nayebi, Jonas Kubilius, 2018 .

To find better neural network models of human vision, find better neural network models of primate vision

Nancy Kanwisher, Martin Schrimpf, James J. DiCarlo, 2019, bioRxiv.

Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures

Aran Nayebi, Jonas Kubilius, Martin Schrimpf, 2018 .

How Does the Brain Solve Visual Object Recognition?

James J. DiCarlo, Davide Zoccolan, Nicole C. Rust, 2012, Neuron.

Ultra-fast Object Recognition from Few Spikes

Gabriel Kreiman, Tomaso Poggio, James J. DiCarlo, 2005 .

Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results

Martin Schrimpf, Tomaso Poggio, Pouya Bashivan, 2018 .

Neural Population Control via Deep Image Synthesis

Pouya Bashivan, James J. DiCarlo, Kohitij Kar, 2018 .

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence

Jonas Kubilius, Martin Schrimpf, James J. DiCarlo, 2020, Neuron.

Supporting Online Material for Fast Readout of Object Identity from Macaque Inferior Temporal Cortex

Tomaso Poggio, Gabriel Kreiman, James J. DiCarlo, 2005 .

Why is Real-World Visual Object Recognition Hard?

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2008, PLoS Comput. Biol..

Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition

James J. DiCarlo, Kohitij Kar, 2021, Neuron.

Evidence that the ventral stream codes the errors used in hierarchical inference and learning

James J. DiCarlo, Charles F. Cadieu, Elias B. Issa, 2016, bioRxiv.

Selective behavioral deficits from focal inactivation of primate inferior temporal (IT) cortex: a new quantitative constraint for models of core object recognition

James J. DiCarlo, Rishi Rajalingham, Hyodong Lee, 2018 .

The Neural Representation Benchmark and its Evaluation on Brain and Machine pdf

Ha Hong, Nicolas Pinto, James J. DiCarlo, 2013, ICLR.

Teacher Guided Architecture Search

Pouya Bashivan, James J. DiCarlo, Mark Tensen, 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

Jonas Kubilius, Martin Schrimpf, Ha Hong, 2018, bioRxiv.

Comparison of Object Recognition Behavior in Human and Monkey

James J. DiCarlo, Rishi Rajalingham, Kailyn Schmidt, 2014, Journal of Neuroscience.

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

Aran Nayebi, Daniel L. K. Yamins, Jonas Kubilius, 2019, NeurIPS.

Neural Population Control via Deep ANN Image Synthesis

Pouya Bashivan, James J. DiCarlo, Kohitij Kar, 2018 .

A potential cortical precursor of visual word form recognition in untrained monkeys

James J. DiCarlo, Stanislas Dehaene, Rishi Rajalingham, 2019, bioRxiv.

Nicholas B. Turk-Browne, James J. DiCarlo, Dan Yamins, 2014, SIGGRAPH '14.

Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex

Ha Hong, James J. DiCarlo, Xiaoxuan Jia, 2020, bioRxiv.

Large-scale Characterization of a Universal and Compact Visual Perceptual Space

Ha Hong, James J. DiCarlo, Daniel Yamins, 2014 .

Pinwheel-like Iso-Orientation Domains in a Convolutional Neural Network Model

James J. DiCarlo, Daniel Yamins, Eshed Margalit, 2018 .

Linking image-by-image population dynamics in the macaque inferior temporal cortex to core object recognition behavior

James J. DiCarlo, Kohitij Kar, Kailyn Schmidt, 2018 .

Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits

James J. DiCarlo, Rishi Rajalingham, J. DiCarlo, 2018, bioRxiv.

CORnet: Modeling the Neural Mechanisms of Core Object Recognition

Aran Nayebi, Jonas Kubilius, Martin Schrimpf, 2018, bioRxiv.

Comparing state-of-the-art visual features on invariant object recognition tasks

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

Establishing Good Benchmarks and Baselines for Face Recognition

Nicolas Pinto, James J. DiCarlo, David Cox, 2008 .

Author response: Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals

James J. DiCarlo, Charles F. Cadieu, Elias B. Issa, 2018 .

Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior

James J. DiCarlo, Elias B. Issa, Kubilius Jonas, 2018, Nature Neuroscience.

Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

Pouya Bashivan, James J. DiCarlo, Rishi Rajalingham, 2018 .

How far can you get with a modern face recognition test set using only simple features?

Nicolas Pinto, James J. DiCarlo, David Cox, 2009, CVPR.

Modern population approaches for discovering neural representations and for discriminating among algorithms that might produce those representations.

Ha Hong, James J. DiCarlo, Daniel Yamins, 2014 .

A performance-optimized model of neural responses across the ventral visual stream

Ha Hong, James J. DiCarlo, Justin L. Gardner, 2016, bioRxiv.

Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex

James J. DiCarlo, Nuo Li, J. DiCarlo, 2008, Science.

Carlo Baldassi, Riccardo Zecchina, James J. DiCarlo, 2013, PLoS Comput. Biol..

Can Deep Neural Networks Rival Human Ability to Generalize in Core Object Recognition

Jonas Kubilius, James J. DiCarlo, Kohitij Kar, 2018 .

Balanced increases in selectivity and invariance produce constant sparseness across the ventral visual pathway

James J. DiCarlo, Nicole C. Rust, 2010 .

Topographic Deep Artificial Neural Networks (TDANNs) predict face selectivity topography in primate inferior temporal (IT) cortex

James J. DiCarlo, Hyodong Lee, J. DiCarlo, 2019, 1909.09847.

Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream

Ha Hong, James J. DiCarlo, Charles F. Cadieu, 2013, NIPS.

Task-Driven Convolutional Recurrent Models of the Visual System

Surya Ganguli, Aran Nayebi, Jonas Kubilius, 2018, NeurIPS.

Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception pdf

James J. DiCarlo, David D. Cox, Josh H. McDermott, 2021, NeurIPS.

Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition

James J. DiCarlo, Kohitij Kar, 2021, Neuron.

Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs pdf

James J. DiCarlo, Tiago Marques, Joel Dapello, 2021, ArXiv.

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence

Jonas Kubilius, Martin Schrimpf, James J. DiCarlo, 2020, Neuron.

Neural dynamics at successive stages of the ventral visual stream are 1 consistent with hierarchical error signals 2 Running title : Error signaling in the ventral stream 3

James J. DiCarlo, Charles F. Cadieu, Elias B. Issa, 2018 .

Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior

Martin Schrimpf, James J. DiCarlo, Tiago Marques, 2021, bioRxiv.

Reversible Inactivation of Different Millimeter-Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits

James J. DiCarlo, Rishi Rajalingham, J. DiCarlo, 2018, Neuron.

Balanced Increases in Selectivity and Tolerance Produce Constant Sparseness along the Ventral Visual Stream

James J. DiCarlo, Nicole C. Rust, N. Rust, 2012, The Journal of Neuroscience.

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation pdf

Chuang Gan, Joshua B. Tenenbaum, Jonas Kubilius, 2020, ArXiv.