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Found 4 papers, 3 papers with code
On 1/n neural representation and robustness
In this work, we investigate the latter by juxtaposing experimental results regarding the covariance spectrum of neural representations in the mouse V1 (Stringer et al) with artificial neural networks.
Characterizing the invariances of learning algorithms using category theory
Many learning algorithms have invariances: when their training data is transformed in certain ways, the function they learn transforms in a predictable manner.
Fast and accurate spike sorting of high-channel count probes with KiloSort
Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms.
High-dimensional cluster analysis with the Masked EM Algorithm
Cluster analysis faces two problems in high dimensions: first, the `curse of dimensionality' that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data.
