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Found 9 papers, 4 papers with code
Fast Multipole Attention: A Divide-and-Conquer Attention Mechanism for Long Sequences
The overall complexity of Fast Multipole Attention is $\mathcal{O}(n)$ or $\mathcal{O}(n \log n)$, depending on whether the queries are down-sampled or not.
Diffusion Random Feature Model
Diffusion probabilistic models have been successfully used to generate data from noise.
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics
The most popular tools for modelling and predicting infectious disease epidemics are compartmental models.
SRMD: Sparse Random Mode Decomposition
Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis.
HARFE: Hard-Ridge Random Feature Expansion
We prove that the HARFE method is guaranteed to converge with a given error bound depending on the noise and the parameters of the sparse ridge regression model.
Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data
In this paper, we propose an adaptive group Lasso deep neural network for high-dimensional function approximation where input data are generated from a dynamical system and the target function depends on few active variables or few linear combinations of variables.
Generalization Bounds for Sparse Random Feature Expansions
In particular, we provide generalization bounds for functions in a certain class (that is dense in a reproducing kernel Hilbert space) depending on the number of samples and the distribution of features.
Recovery guarantees for polynomial approximation from dependent data with outliers
In this work, we study the problem of learning nonlinear functions from corrupted and dependent data.
