190 datasets found

The dataset used in this paper is a collection of quantum neural network models, including VQA, CV, swap test and phase estimation, RUS, quantum generalization, QBM, QCVNN,...

The Hamiltonian neural network architecture is used to solve DE systems. The Hamiltonian NN is an evolution of previously used unsupervised NNs for finding solutions to DEs that...

The dataset is used for resting state functional Magnetic Resonance Imaging (fMRI) trajectory to predict future brain images based on the given sequence.

The dataset used in this paper is a 40x40 neuron sheet with a grid-like firing pattern, which is used to simulate the behavior of grid cells in the medial entorhinal cortex.

The dataset is used to study the computational complexity of learning pushforwards of Gaussians under one-hidden-layer ReLU networks.

The dataset used in the paper to analyze intrinsic and embedding dimensionality of neural activity.

The dataset describes the upcrossing-rate dynamics for a minimal neuron model receiving spatially distributed synaptic drive.

This work addresses the problem of planting and defending cryptographic-based backdoors in artificial intelligence (AI) models. The motivation comes from our lack of...

The dataset contains low-dimensional neural trajectories of populations of neurons in the RNNs for online handwriting synthesis.

The dataset used in the paper describes the dynamics of synaptic plasticity and learning in neural networks.

The dataset used in the paper describes the dynamics of metabolic resource transport across the network of glial cells, which can stabilize learning dynamics in neuronal networks.

The dataset is used for training and testing of spiking neural networks for height control of a hexacopter. The dataset consists of a set of inputs and corresponding outputs,...

The dataset used in this paper is a collection of spike trains from different neural motifs.

The dataset used in this paper is a collection of spike trains from leaky-integrate-and-fire (LIF) neurons.

The dataset used in this paper is a collection of spike trains from salamander retina.

The proposed quantum neural net is used for machine learning problems, specifically for pattern recognition on iris and breast cancer datasets.

PROBEN1 is a set of neural network problems and benchmarking rules.

The dataset used in this paper is a collection of neural abstractions for dynamical models. The dataset consists of neural networks with different activation functions...

The dataset used in this paper is a silicon-photonic neural network (SPNN) with two hidden layers and 1374 tunable-thermal-phase shifters.

The dataset is used for analyzing individual neurons in pre-trained models.