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Synthetic dataset for Leaky ResNets
The dataset used in the paper is a synthetic dataset generated by teacher networks for a given true rank k∗. -
Discontinuous Neural Network for Non-Negative Sparse Approximation
The dataset is used to model the mammalian olfactory system and solve non-negative sparse approximation problems. -
Large scale Lasso with windowed active set for convolutional spike sorting
The dataset used in the paper is a large-scale spike sorting dataset, containing recordings from a population of neurons with up to 4000 electrodes, 1000 neurons, and 108 time... -
Coexistence of fast and slow gamma oscillations
The dataset describes the coexistence of fast and slow gamma oscillations in a single inhibitory population of neurons. -
Neural Lattice Reduction
The dataset used in the paper is a randomly-generated dataset of lattices, where each lattice is represented by a basis of n vectors in R^n. -
Discovery of Dynamics via Deep Learning
The dataset used in the paper is a collection of time series data from a dynamical system, which is used to test the performance of the network-based LMMs for discovering... -
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. -
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. -
Synthetic dataset for multiplicative interactions
A synthetic dataset that exhibits multiplicative interactions between inputs. The function to be approximated is a multi-variate polynomial or multinomial. -
Model of Learning in C. elegans
A mathematical model of the fundamental roots of learning mechanisms within the brain of Caenorhabditis elegans. -
Feedforward Network of Spiking Neurons
The dataset used in the paper describes the evolution of the probability density function in a feedforward network of spiking neurons. -
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. -
State Space Representations of Deep Neural Networks
This paper deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of k-many skip connections into network... -
Minimal Spiking Neural Networks
A minimal motif consists of only two interconnected neurons – one excitatory neuron with a delayed self-connection (autapse) and one inhibitory neuron, yielding a bistable motif... -
Synthetic dataset for testing the proposed method
The dataset used in this paper is a synthetic dataset generated for testing the proposed method. It consists of three independent parts, each with 15 dimensions, and is used to... -
Neural Identification for Control
The proposed method for learning control for an unknown nonlinear dynamical system by formulating a system identification task.
