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Research Interests
I’m working on a variety of topics, loosely collected under the heading of finding or creating useful structure in neural representations
how can we better understand the layer-wise steps that neural networks take transforming inputs to ouputs?
what are the advantages of a “modular” neural network, and how do we build them?
how does the brain learn and model causal relationships?
And growing out of my earlier work on Bayesian inference in the brain, a separate line of my recent work asks
what sort of approximate inference algorithms exist “between” MCMC and variational inference?
(how) does the brain represent probability?
I’m working on a variety of topics, loosely collected under the heading of finding or creating useful structure in neural representations
how can we better understand the layer-wise steps that neural networks take transforming inputs to ouputs?
what are the advantages of a “modular” neural network, and how do we build them?
how does the brain learn and model causal relationships?
And growing out of my earlier work on Bayesian inference in the brain, a separate line of my recent work asks
what sort of approximate inference algorithms exist “between” MCMC and variational inference?
(how) does the brain represent probability?
研究兴趣
论文共 15 篇作者统计合作学者相似作者
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Journal of neurophysiologyno. 5 (2023): 1021-1044
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