Publication
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Filters: Author is Gabriel Kreiman [Clear All Filters]
What can human minimal videos tell us about dynamic recognition models?. International Conference on Learning Representations (ICLR 2020) (2020). at <https://baicsworkshop.github.io/pdf/BAICS_1.pdf> Authors' final version (516.09 KB)
XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020). gk7791.pdf (2.39 MB)
Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell 177, 1009 (2019). Author's last draft (20.26 MB)
Psychology of Learning and Motivation 70, (2019).
Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (2018). at <https://embc.embs.org/2018/>
Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications 9, (2018).
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition. arXiv | Cornell University arXiv:1803.01967, (2018).
Learning scene gist with convolutional neural networks to improve object recognition. 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (2018). doi:10.1109/CISS.2018.8362305 08362305.pdf (3.17 MB)
Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1–17, (2018).
A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf> 1805.10734.pdf (9.59 MB)
Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115 1719397115.full_.pdf (1.1 MB)
Spatiotemporal interpretation features in the recognition of dynamic images. (2018). CBMM-Memo-094.pdf (1.21 MB) CBMM-Memo-094-dynamic-figures.zip (1.8 MB) fig1.ppsx (147.67 KB) fig2.ppsx (419.72 KB) fig4.ppsx (673.41 KB) figS1.ppsx (587.88 KB) figS2.ppsx (281.56 KB)
What am I searching for?. (2018). CBMM-Memo-096.pdf (1.74 MB)
What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage 180, Part A, 147-159 (2018). Human neurophysiological responses during movies (2.78 MB)
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017). CBMM-Memo-064.pdf (3 MB)
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017). 1605.08104.pdf (2.9 MB)
A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience 274 - 285 (2017). doi:10.1080/23273798.2016.1218033
Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017). CBMM-Memo-065.pdf (687.76 KB)
What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage (2017). doi:https://doi.org/10.1016/j.neuroimage.2017.08.027
Bottom-up and Top-down Input Augment the Variability of Cortical Neurons. Neuron 91(3), 540-547 (2016).
Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352 Manuscript (1.83 MB)