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Found 7 papers, 5 papers with code
Spectral gap-based deterministic tensor completion
However, numerical experiments illustrate the dependence of the reconstruction error on the spectral gap for the practical max-quasinorm, ridge penalty, and Poisson loss minimization algorithms.
BrainViewer: interacting with spatial connectome data at the mesoscale
Scientists construct connectomes, comprehensive descriptions of neuronal connections across a brain, in order to better understand and model brain function.
Deterministic tensor completion with hypergraph expanders
We provide a novel analysis of low-rank tensor completion based on hypergraph expanders.
Additive function approximation in the brain
We identify three specific advantages of sparsity: additive function approximation is a powerful inductive bias that limits the curse of dimensionality, sparse networks are stable to outlier noise in the inputs, and sparse random features are scalable.
Time-varying Autoregression with Low Rank Tensors
In each time window, we assume the data follow a linear model parameterized by a system matrix, and we model this stack of potentially different system matrices as a low rank tensor.
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
We demonstrate the efficacy of a low rank version on visual cortex data and discuss the possibility of extending this to a whole-brain connectivity matrix at the voxel scale.
Human language reveals a universal positivity bias
Using human evaluation of 100, 000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (1) the words of natural human language possess a universal positivity bias; (2) the estimated emotional content of words is consistent between languages under translation; and (3) this positivity bias is strongly independent of frequency of word usage.
