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Kreiman Lab News

PredNet: A deep neural network architecture for predictive coding

Bill Lotter explains the PredNet neural network architecture

See publication:
Lotter W, Kreiman G, Cox D. (2020) A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Intelligence, 2:210-219 PDF

See also:

Tang H, Schrimpf M, Lotter W, Moerman C, Paredes A, Ortega Caro J, Hardesty W, Cox D, Kreiman G. (2018) Recurrent computations for visual pattern completion. PNAS, 115:8835-8840. PDF

Lotter W, Kreiman, G, Cox, D. (2017) Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. International Conference on Learning Representations (ICLR), Toulon, France. PDF

Lotter, W, Kreiman, G, Cox, D. (2016.) Unsupervised representation learning using predictive generative works. International Conference on Learning Representations (ICLR), Puerto Rico. PDF

GitHub Page

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Kreiman Lab News

Contextual reasoning in man and machines

Flash presentation by Prof. Gabriel Kreiman in the SciFoo conference 2021

See also:

Bomatter P, Zhang M, Karev D, Madan S, Tseng C, Kreiman G (2021). When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. International Conference on Computer Vision (ICCV) arXiv 2104.02215. PDF

Zhang M, Tseng C, Kreiman G. (2020) Putting visual object recognition in context. CVPR. PDF

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Kreiman Lab News

Beauty is in the eye of the machine

Edmond de Belamy

Perspective article by Mengmi Zhang. Nature Human Behavior (2021).

Ansel Adams said, “There are no rules for good photographs, there are only good photographs.” Is it possible to predict our fickle and subjective appraisal of ‘aesthetically pleasing’ visual art? Iigaya et al. used an artificial intelligence approach to show how human aesthetic preference can be partially explained as an integration of hierarchical constituent image features.

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Kreiman Lab News

At the interface of brain and machine

Beijing, Apr 20, 2021

Prof. Gabriel Kreiman’s lecture titled:

How brain computations can inspire new paths in AI

See also:

Bomatter P, Zhang M, Karev D, Madan S, Tseng C, Kreiman G (2021). When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. International Conference on Computer Vision (ICCV) PDF

Casper S, Boix X, D’Amario V, Guo L, Schrimpf M, Vinken K, Kreiman G. (2021). Frivolous Units: Wider Networks are not really that Wide. AAAI Conference on Artificial Intelligence PDF

Kreiman G and Serre T (2020). Beyond the feedforward sweep: feedback computations in the visual cortex. Ann N Y Acad Sci. 1464:222-241. PDF


Jacquot V, Ying J, Kreiman G. (2020) Can Deep Learning Recognize Subtle Human Activities? Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 14244-14253. arXiv 2003.13852

Zhang M, Tseng C, Kreiman G. (2020) Putting visual object recognition in context. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 12982-12991. arXiv:1911.07349

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Kreiman Lab News

Brains, Minds and Machines Summer Course 2021

Applications are now open for the 2021 edition of the Brain, Minds and Machines Summer Course in Woods Hole MA.

08/05/2021 – 08/26/2021

Link to application: https://www.mbl.edu/education/courses/brains-minds-and-machines/