153 datasets found

The dataset used in this paper is a collection of training data for over-parameterized deep neural networks.

The dataset consists of simultaneous recordings of 105 neurons from the motor cortex of a macaque.

The dataset consists of simultaneous recordings of 148 neurons from the primary visual cortex (area V1) of an anesthetized macaque.

ExNN dataset used for credit default prediction

The dataset used in the paper is a discrete-time stochastic dynamical system with two novel aspects: (a) using ranking supermartingales (RSMs) to certify almost-sure asymptotic...

A Convolutional Neural Network (CNN) can generate clinically relevant parametric maps from CT perfusion data in a clinical setting of patients with acute ischemic stroke.

This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching.

The dataset used in this paper is a collection of spike train data from a leakage integrate-and-fire (LIF) neuron model. The data is generated by applying different pulse inputs...

The dataset used in this paper is an artificial neural network with 300 neurons, where the firing activity and credit values are updated using the proposed neuroplasticity rule.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a black-box three-output neural network.

The dataset used in the paper is a simulation dataset for a robotic arm, where the robot learns to reproduce target sensorimotor sequences through free-energy minimization.

The dataset used in this paper is a neural network training dataset with radix-4 gradients.

The dataset used in the paper is a set of functions representable by ReLU neural networks with integer weights and arbitrary width.

Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns...

The proposed 1D Self-ONNs for patient-specific ECG classification and arrhythmia detection.

The dataset used in this paper is a multilayered sparse neural network, specifically a convolutional neural network.

Hybrid ELB-NN for accuracy and computational complexity tradeoffs. Our experimental results indicate that the accuracy varies with the precisions of weights and activations with...

The dataset used in this paper is a collection of training data and target functions for two-layer neural networks. The dataset is used to test the performance of the Linear...

The MNIST-parity experiment uses the MNIST dataset to test the performance of a ReLU network and various linear models on the parity of a single MNIST image and the parity of...

Long short-term memory