-
Overparametrised Shallow ReLU Networks
The dataset used in the paper is a high-dimensional dataset for supervised learning, with a focus on shallow neural networks and overparametrization. -
A Mathematical Motivation for Complex-valued Convolutional Networks
A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an... -
Simulated neural networks and larval zebrafish imaging
Simulated neural networks and larval zebrafish imaging data -
TensorQuant
TensorQuant toolbox is used to apply fixed point quantization to DNNs. The simulations are focused on popular CNN topologies, such as Inception V1, Inception V3, ResNet 50 and... -
Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in ...
The dataset used in this paper is a multidimensional cascade neural network with neuron pool optimization in each cascade. -
TransparentFPGAAccelerationwithTensorFlow
The dataset used in this paper is a collection of neural network acceleration with TensorFlow and FPGA. -
Binary Neural Network Dataset
The dataset used in this paper is a binary neural network model. -
Table II: Table containing the set of parameters used for the NMDA model
The dataset used in the paper is a set of parameters for a conductance-based integrate-and-fire neuron model with NMDA channels. -
Neural Network Repair with Reachability Analysis
The dataset used in the paper is a neural network repair framework for safety-critical systems. -
MIXED PRECISION TRAINING
The dataset used for training deep neural networks using half-precision floating point numbers. -
Framework for In-memory Computing based on Memristor and Memcapacitor for On-...
A comprehensive Python framework for evaluating large-scale deep neural networks (DNN) on memristive and memcapacitive crossbar systems, addressing various non-idealities. -
Restricted Boltzmann Machine Assignment Algorithm
The dataset is used to solve a perfect matching problem on a bipartite weighted graph. It contains 351 elements for the set A and 35 for the set B. -
Depth Separation with Intra-layer Links
The dataset used in the paper is a collection of functions that can be represented by a deep network, but cannot be represented by a shallow network. -
Population Based Augmentation
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations. -
Thalamo-cortical spiking model of incremental learning combining perception, ...
Sleep is essential for learning and cognition, but the mechanisms by which it stabilizes learning, supports creativity, and manages the energy consumption of networks engaged in... -
Anomalous diffusion dynamics of learning in deep neural networks
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used ResNet-14, ResNet-20, ResNet-56, and ResNet-110 networks, as well as... -
Hamiltonian Neural Networks
The dataset is used for learning Hamiltonian neural networks. -
Transformations between deep neural networks
The dataset used in the paper is a collection of neural networks trained on different tasks, including scalar functions, two-dimensional vector fields, and images of a rotating... -
PANGAEA search space
A dataset of 425,896 unique activation functions, created using the PANGAEA search space. -
Act-Bench-CNN, Act-Bench-ResNet, and Act-Bench-ViT
Three benchmark datasets: Act-Bench-CNN, Act-Bench-ResNet, and Act-Bench-ViT, created by training convolutional, residual, and vision transformer architectures from scratch with...
