Search Results for author:
Found 6 papers, 2 papers with code
A coherence parameter characterizing generative compressed sensing with Fourier measurements
In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is the range of a generative neural network (GNN).
PLUGIn: A simple algorithm for inverting generative models with recovery guarantees
We prove that, when weights are Gaussian and layer widths $n_i \gtrsim 5^i n_0$ (up to log factors), the algorithm converges geometrically to a neighbourhood of $x$ with high probability.
PLUGIn-CS: A simple algorithm for compressive sensing with generative prior
After a sufficient number of iterations, the estimation errors for both $x$ and $\mathcal{G}(x)$ are at most in the order of $\sqrt{4^dn_0/m} \|\epsilon\|$.
Polar Deconvolution of Mixed Signals
The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components.
Global Guarantees for Blind Demodulation with Generative Priors
That is, the objective function has a descent direction at every point outside of a small neighborhood around four hyperbolic curves.
A Convex Program for Mixed Linear Regression with a Recovery Guarantee for Well-Separated Data
We introduce a convex approach for mixed linear regression over $d$ features.
