84 citations · 342 across the 31 of their papers we have counts for
4 papers · 1 filter
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
Adam Charles, Dong Yin, Christopher Rozell
Recurrent neural networks (RNNs) have drawn interest from machine learning researchers because of their effectiveness at preserving past inputs for time-varying data processing tas…
Short Term Memory Capacity in Networks via the Restricted Isometry Property
Adam S. Charles, Han Lun Yap, Christopher J. Rozell
Cortical networks are hypothesized to rely on transient network activity to support short term memory (STM). In this paper we study the capacity of randomly connected recurrent lin…
The Restricted Isometry Property for Random Block Diagonal Matrices
Armin Eftekhari, Han Lun Yap, Christopher J. Rozell +1
In Compressive Sensing, the Restricted Isometry Property (RIP) ensures that robust recovery of sparse vectors is possible from noisy, undersampled measurements via computationally…
Stable Manifold Embeddings with Structured Random Matrices
Han Lun Yap, Michael B. Wakin, Christopher J. Rozell
The fields of compressed sensing (CS) and matrix completion have shown that high-dimensional signals with sparse or low-rank structure can be effectively projected into a low-dimen…