189 citations · 206 across the 6 of their papers we have counts for
6 papers
KrADagrad: Kronecker Approximation-Domination Gradient Preconditioned Stochastic Optimization
Jonathan Mei, Alexander Moreno, Luke Walters
Second order stochastic optimizers allow parameter update step size and direction to adapt to loss curvature, but have traditionally required too much memory and compute for deep l…
SKI to go Faster: Accelerating Toeplitz Neural Networks via Asymmetric Kernels
Alexander Moreno, Jonathan Mei, Luke Walters
Toeplitz Neural Networks (TNNs) (Qin et. al. 2023) are a recent sequence model with impressive results. They require O(n log n) computational complexity and O(n) relative positiona…
Single Index Latent Variable Models for Network Topology Inference
Jonathan Mei, José M. F. Moura
A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to unm…
EigenNetworks
Jonathan Mei, José M. F. Moura
Many applications donot have the benefit of the laws of physics to derive succinct descriptive models for observed data. In alternative, interdependencies among time series $\{…
SILVar: Single Index Latent Variable Models
Jonathan Mei, José M. F. Moura
A semi-parametric, non-linear regression model in the presence of latent variables is introduced. These latent variables can correspond to unmodeled phenomena or unmeasured agents…
Signal Processing on Graphs: Causal Modeling of Unstructured Data
Jonathan Mei, José M. F. Moura
Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit th…