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- W2172535169 abstract "This paper presents our solution to the European Conference of Machine Learning Neural Connectomics Discovery Challenge. The challenge goal was to improve the performance of existing methods for recovering the neural network structure given the time series of neural activities. We propose to approximate a function able to combine several connectivity indicators between neuron pairs where each indicator is the result of running a feature engineering pipeline optimized for a particular noise level and firing synchronization rate among neurons. We proved the suitability of our solution by improving the state of the art prediction performance more than 6% and by obtaining the third best score on the test dataset out of 144 teams." @default.
- W2172535169 created "2016-06-24" @default.
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- W2172535169 date "2017-01-01" @default.
- W2172535169 modified "2023-09-27" @default.
- W2172535169 title "Supervised Neural Network Structure Recovery" @default.
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- W2172535169 doi "https://doi.org/10.1007/978-3-319-53070-3_3" @default.
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