-
Learned Gridification for Efficient Point Cloud Processing
A point cloud processing pipeline that transforms the point cloud into a compact, regular grid and performs neural operations on the grid. -
Synthetic Dataset for Metaparametric Neural Networks
A synthetic dataset used to evaluate the performance of the proposed metaparametric neural network framework. -
Metaparametric Neural Networks for Survival Analysis
Survival analysis is a critical tool for the modelling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex... -
Multifunctional Agent
The dataset used in the paper is a set of embodied recurrent neural networks that perform object categorization and pole-balancing tasks. -
Leapfrogging for parallelism in deep neural networks
The dataset used in the paper is a neural network with L layers numbered 1,..., L, in which each of the hidden layers has N neurons. -
NITI: INTEGER TRAINING
The dataset used in this paper is MNIST, CIFAR10, and ImageNet. -
I2B2 2009 Medical Extraction Challenge
I2B2 2009 Medical Extraction Challenge -
I2B2 2009 Medical Information Extraction Challenge
Named Entity Recognition in Electronic Health Records using Transfer Learning Bootstrapped Neural Networks -
Hierarchical Exponential-family Energy-based (HEE) model on CIFAR10
The HEE model uses CIFAR10 to demonstrate its ability to generate high-quality images. -
Hierarchical Exponential-family Energy-based (HEE) model
The HEE model uses 2D synthetic datasets and FashionMNIST to validate its capabilities. -
Imaging Conductivity from Current Density Magnitude using Neural Networks
Conductivity imaging represents one of the most important tasks in medical imaging. In this work we develop a neural network based reconstruction technique for imaging the... -
Non-asymptotic approximations of neural networks by Gaussian processes
The dataset is not explicitly described in the paper, but it is mentioned that the authors study the extent to which wide neural networks may be approximated by Gaussian processes. -
Training Set
A dataset used to train and test the neural network classifiers. -
Validation Set
A dataset used to train and test the neural network classifiers. -
Training Over-Parameterized Deep Neural Networks
The dataset used in this paper is a collection of training data for over-parameterized deep neural networks. -
Neural Certificates for Safe Control Policies
This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching. -
NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-sourced Da...
Training complex neural network models using third-party cloud-based infrastructure among multiple data sources is a promising approach among existing machine learning... -
A Theoretical Perspective on Hyperdimensional Computing
Hyperdimensional computing is a set of neurally inspired methods for obtaining high-dimensional, low-precision, distributed representations of data.
