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Found 10 papers, 0 papers with code
Neural Network Training with Asymmetric Crosspoint Elements
Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training.
EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation
However, the existing methods for sparse Kronecker-sum inverse covariance estimation are limited in that they do not scale to more than a few hundred features and samples and that the unidentifiable parameters pose challenges in estimation.
SEMULATOR: Emulating the Dynamics of Crossbar Array-based Analog Neural System with Regression Neural Networks
As deep neural networks require tremendous amount of computation and memory, analog computing with emerging memory devices is a promising alternative to digital computing for edge devices.
EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices
In this paper, we address the problem of jointly estimating dependencies across samples and dependencies across multiple features, where each set of dependencies is modeled as an inverse covariance matrix.
Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays
By using this zero-shifting method, we show that network performance dramatically improves for imbalanced synapse devices.
Analog CMOS-based Resistive Processing Unit for Deep Neural Network Training
Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations running on CPU/GPU.
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
While highly scalable optimization methods exist for sparse Gaussian graphical model estimation, state-of-the-art methods for conditional Gaussian graphical models are not efficient enough and more importantly, fail due to memory constraints for very large problems.
On Sparse Gaussian Chain Graph Models
In this paper, we address the problem of learning the structure of Gaussian chain graph models in a high-dimensional space.
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables
Most previous methods were based on a two-stage approach that prunes the search space in the first stage and then searches for a network structure that satisfies the DAG constraint in the second stage.
Heterogeneous multitask learning with joint sparsity constraints
In this paper we consider the problem learning multiple related tasks where tasks consist of both continuous and discrete outputs from a common set of input variables that lie in a high-dimensional space.
