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Excited about our work on biologically inspired #NeuralNetwork with recurrent generative feedback. We show feedback is key to robustness, which current feedforward #NeuralNetwork lack. @YujiaHuangC @JamesGornet @dai_sh29 @ZhidingYu @TanNguyen689 @doristsao @Caltech @NVIDIAAIhttps://twitter.com/YujiaHuangC/status/1284016518594834434 …
Inspired by Bayesian Brain and predictive coding model in #neuroscience we integrate bottom up and top down signals via approximate Bayesian inference for self consistency. https://arxiv.org/abs/2007.09200 pic.twitter.com/Zez7Hc8oye
We use deconvolutional generative model for feedback: satisfies Bayes rule with feedforward #NeuralNetwork Allows us to implement Bayesian brain theory. Iterative MAP inference results in recurrent feedback, enforces self-consistency. Top-down feedback modulates ReLu/other unitspic.twitter.com/KQX31Xf6vO
More iterations are needed for “harder images” i.e. stronger attacks. Our model is robust while being agnostic to attacks. Adversarial training further enhances robustness. Generative feedback also restores corrupted images: get both clean images and labels. #AI #DeepLearningpic.twitter.com/lp3NaR5H7F
I skimmed through the paper and it looks amazing! Definitely the first paper to read after NeurIPS reviewing 
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