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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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6 results for “multi-spike”

Equivalence of approximation by networks of single- and multi-spike neuronscs.NE

Single-spike neurons can approximate as well as multi-spike neurons.

problem Limitation of single-spike neurons in spiking neural networks.

method Comparison of single-spike and multi-spike neural networks.

result Single-spike and multi-spike neural networks are equivalent in approximation capabilities.

spiking neural networks single-spike multi-spike approximation neurons

2026-03-13abs ↗pdf ↗

Stochastic gradient descent in high dimensions for multi-spiked tensor PCAstat.ML

SGD recovers multiple signal vectors in noisy tensor PCA.

problem Estimating multiple signal vectors from noisy tensor observations.

method Online stochastic gradient descent (SGD) in high dimensions with detailed analysis of correlations.

result Sequential elimination of correlations allows recovery of all spikes from

N^{p-2}

samples.

tensor PCA stochastic gradient descent sequential elimination signal-to-noise ratios multi-spiked tensor model

2024-10-23abs ↗pdf ↗

Langevin dynamics for high-dimensional optimization: the case of multi-spiked tensor PCAstat.ML

Study of Langevin dynamics for tensor PCA recovery in high dimensions.

problem Recovering hidden signal vectors (spikes) from noisy Gaussian tensor observations.

method Langevin dynamics approach for nonconvex optimization.

result Sample complexity matches the single-spike case but degrades for all spikes.

Langevin dynamics tensor PCA high-dimensional optimization signal recovery maximum likelihood estimation

2024-08-12abs ↗pdf ↗

Statistical Limits in Random Tensors with Multiple Correlated Spikesmath.ST

New estimator for tensor weights with improved bias.

problem Estimating tensor weights from noisy data.

method Random matrix theory and KKT conditions.

result Asymptotically unbiased estimator for tensor rank.

Tensor estimation Random matrices KKT conditions Phase transitions Maximum likelihood

2025-03-05abs ↗pdf ↗

Sampled forms of functional PCA in reproducing kernel Hilbert spacesmath.ST

We consider the sampling problem for functional PCA (fPCA), where the simplest example is the case of taking time samples of the underlying functional components. More generally, we model the sampling operation as a continuous linear map from $\mathcal{H}$ to $\mathbb{R}^m$ , where the functional components to lie in so…

2011-09-15abs ↗pdf ↗

Permutation recovery of spikes in noisy high-dimensional tensor estimationmath.PR

Study recovers spike order in noisy tensor estimation without SNR assumptions.

problem Estimating multiple signal vectors from noisy tensor observations.

method Gradient flow optimization of a nonconvex function.

result Determines sample complexity for efficient permutation recovery.

tensor estimation gradient flow spike recovery noisy data sample complexity

2024-12-19abs ↗pdf ↗