Search Results for author:
Found 7 papers, 2 papers with code
Structure Learning in Graphical Models from Indirect Observations
For the first time, we show that the correct graphical structure can be correctly recovered under the indefinite sensing system ($d < p$) using insufficient samples ($n < p$).
A General Compressive Sensing Construct using Density Evolution
This paper proposes a general framework to design a sparse sensing matrix $\ensuremath{\mathbf{A}}\in \mathbb{R}^{m\times n}$, in a linear measurement system $\ensuremath{\mathbf{y}} = \ensuremath{\mathbf{Ax}}^{\natural} + \ensuremath{\mathbf{w}}$, where $\ensuremath{\mathbf{y}} \in \mathbb{R}^m$, $\ensuremath{\mathbf{x}}^{\natural}\in \RR^n$, and $\ensuremath{\mathbf{w}}$ denote the measurements, the signal with certain structures, and the measurement noise, respectively.
A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT
IoT devices generating enormous data and state-of-the-art machine learning techniques together will revolutionize cyber-physical systems.
Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference
In this paper, we consider the parallel implementation of an already-trained deep model on multiple processing nodes (a. k. a.
Nested Dithered Quantization for Communication Reduction in Distributed Training
In distributed training, the communication cost due to the transmission of gradients or the parameters of the deep model is a major bottleneck in scaling up the number of processing nodes.
Fast Convex Pruning of Deep Neural Networks
We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network.
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
This program seeks a sparse set of weights at each layer that keeps the layer inputs and outputs consistent with the originally trained model.
