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Learning From Brains How to Regularize Machines


Miles Brundage: "Learning From [Mouse] Brains How to Regularize Machines," Li et al.: Wild stuff - they showed images to mice, recorded the mice's neural activity, made a model of that, then penalized not-mouse-brain-y representations when training new classifiers.

3 replies, 228 likes

Andreas Tolias Lab: Neuroscience has inspired #DeepLearning, but lacks methods to directly translate neural data into better #AI algorithms. Lead by Zhe Li in our @NeurIPSConf paper we used neural data to engineer more robust AI algorithms with better generalization

2 replies, 94 likes

Shahab Bakhtiari: So, if we regularize an ANN to have similar representations as those of mouse visual cortex, the ANN becomes more robust to adversarial attacks. Here is the most interesting part of the paper for me:

0 replies, 22 likes

arxiv: Learning From Brains How to Regularize Machines.

0 replies, 13 likes

Daisuke Okanohara: Scan neural responses of actual mouses against several images and compute the similarity matrix among images, then use this matrix to regularize NN representation to improve NN's robustness against input noises. Inductive bias from actual brains.

0 replies, 12 likes

ALife Papers: Learning From Brains How to Regularize Machines "We then used the neural representation similarity to regularize CNNs trained on image classification by penalizing intermediate representations that deviated from neural ones"

0 replies, 11 likes

Andreas Tolias Lab: We aim to create AI algorithms that are robust and generalize better using neuroscience data. Learn about our work presented #NeurIPS today (10:45 AM -- 12:45 PM @ East Exhibition Hall B + C #65) and Send your CV to

0 replies, 8 likes

TĂĽbingenNeuroCampus: Cool new paper with contributions from @bethgelab @sinzlab @wielandbr "Learning From #Brains How to Regularize Machines"

0 replies, 6 likes

Carlos E. Perez 🧢: Well this is certainly wild and a sign of things to come. @Miles_Brundage Research showing using of brain imaging as a regularization parameter for convolutional networks! .

1 replies, 5 likes

Erik Jonker: Some very small but interesting beginnings with regard to improving machine learning with data from natural brains

0 replies, 1 likes

Peng Liu: Great work. How about an inverse way? Let’s see “Learning From Machine How to Optimize [Biological] Brains” #AI #DeepLearning #MachineLearning #brain

0 replies, 1 likes


Found on Nov 14 2019 at

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