68 datasets found

LEURN: a neural network architecture that learns univariate decision rules

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 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 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.

Noisy Differentiable Architecture Search

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 situated dataset is a dataset of objects annotated with properties and affordances in real-world images.

The abstract dataset is a refreshed version of the McRate dataset, pruned and densely annotated to eliminate false negatives present in previous work.

We study the learning and generalization of overparameterized neural networks, going beyond two layers.

BLUFF is an interactive system for visualizing, characterizing, and deciphering adversarial attacks on DNNs.

The dataset is used to model the mammalian olfactory system and solve non-negative sparse approximation problems.

Visual language grounding is widely studied in modern neural networks, which typically adopts an encoder-decoder framework consisting of a convolutional neural network (CNN) for...

The dataset describes the coexistence of fast and slow gamma oscillations in a single inhibitory population of neurons.

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.

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...

The dataset used in the paper is a set of polynomial and non-polynomial dynamical models, including the Darboux model, the exponential model, the obstacle avoidance problem, the...

The proposed method for learning control for an unknown nonlinear dynamical system by formulating a system identification task.