Search Results for author:
Found 10 papers, 5 papers with code
DLKoopman: A deep learning software package for Koopman theory
We present DLKoopman -- a software package for Koopman theory that uses deep learning to learn an encoding of a nonlinear dynamical system into a linear space, while simultaneously learning the linear dynamics.
LAGOON: An Analysis Tool for Open Source Communities
This paper presents LAGOON -- an open source platform for understanding the complex ecosystems of Open Source Software (OSS) communities.
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning
In particular, we show the superiority of a greedy strategy and justify our choice of Bayesian optimization as the primary search methodology over random / grid search.
Pre-Defined Sparse Neural Networks with Hardware Acceleration
Neural networks have proven to be extremely powerful tools for modern artificial intelligence applications, but computational and storage complexity remain limiting factors.
A Highly Parallel FPGA Implementation of Sparse Neural Network Training
We demonstrate an FPGA implementation of a parallel and reconfigurable architecture for sparse neural networks, capable of on-chip training and inference.
Interleaver Design for Deep Neural Networks
We propose a class of interleavers for a novel deep neural network (DNN) architecture that uses algorithmically pre-determined, structured sparsity to significantly lower memory and computational requirements, and speed up training.
Pricing Football Players using Neural Networks
We designed a multilayer perceptron neural network to predict the price of a football (soccer) player using data on more than 15, 000 players from the football simulation video game FIFA 2017.
Characterizing Sparse Connectivity Patterns in Neural Networks
We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training.
Accelerating Training of Deep Neural Networks via Sparse Edge Processing
We propose a reconfigurable hardware architecture for deep neural networks (DNNs) capable of online training and inference, which uses algorithmically pre-determined, structured sparsity to significantly lower memory and computational requirements.
