Scaling of learning time for high dimensional inputs
Representation learning from complex data typically involves models with a large number of parameters, which in turn require large amounts of data samples. In neural network models, model complexity grows with the number of inputs to each neuron, with a trade-off between model expressivity and learning time. A precise characterization of this trade-off would help explain the connectivity and learning times observed in artificial and biological networks. We present a theoretical analysis of how learning time depends on input dimensionality for a Hebbian learning model performing independent component analysis. Based on the geometry of high-dimensional spaces, we show that the learning dyna...
| Authors | Carlos Stein Brito |
|---|---|
| Primary category | cs.LG |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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