450 citations · 557 across the 27 of their papers we have counts for
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Mutual Information of Neural Network Initialisations: Mean Field Approximations
Jared Tanner, Giuseppe Ughi
The ability to train randomly initialised deep neural networks is known to depend strongly on the variance of the weight matrices and biases as well as the choice of nonlinear acti…
On the construction of sparse matrices from expander graphs
Bubacarr Bah, Jared Tanner
We revisit the asymptotic analysis of probabilistic construction of adjacency matrices of expander graphs proposed in [4]. With better bounds we derived a new reduced sample comple…
On construction and analysis of sparse random matrices and expander graphs with applications to compressed sensing
Bubacarr Bah, Jared Tanner
We revisit the probabilistic construction of sparse random matrices where each column has a fixed number of nonzeros whose row indices are drawn uniformly at random. These matrices…
Vanishingly Sparse Matrices and Expander Graphs, With Application to Compressed Sensing
Bubacarr Bah, Jared Tanner
We revisit the probabilistic construction of sparse random matrices where each column has a fixed number of nonzeros whose row indices are drawn uniformly at random with replacemen…
Compressed Sensing: How sharp is the Restricted Isometry Property
Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner
Compressed Sensing (CS) seeks to recover an unknown vector with entries by making far fewer than measurements; it posits that the number of compressed sensing measurements…
Phase Transitions for Greedy Sparse Approximation Algorithms
Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner +1
A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, so…