190 datasets found

The dataset used in the paper is related to neural networks and artificial intelligence.

Energy-based out-of-distribution detection.

Enhancing the reliability of out-of-distribution image detection in neural networks.

A baseline for detecting misclassified and out-of-distribution examples in neural networks.

MERCURY accelerates DNN training by exploiting input similarity.

The dataset consists of 24 state-of-the-art Google edge neural network models, including CNNs, LSTMs, Transducers, and RCNNs.

The dataset used in this paper is LDMNet, a low-dimensional manifold regularized neural network.

The dataset used in this paper is a network of renewal neurons, a well-established class of spiking cells. The network's dynamics can be accurately represented by a partial...

The dataset is used to analyze the occurrence of bidirectional connections in local cortical networks.

Zenkai is an open-source framework designed to give researchers more control and flexibility over building and training deep learning machines.

The dataset is a class of noisy recurrent neural networks with w (unbounded) weights for classification of sequences of length T, where independent noise distributed according...

The dataset used in the paper is a Wilson-Cowan network with homeostatic regulation, which is a model of neural activity.

We consider the fundamental problem of learning a single neuron x (cid:55)→ σ(w(cid:62)x) in a realizable setting, using standard gradient methods with random initialization,...

A large-scale malware dataset containing 100 families and more than 50 thousand samples.

Malware open-set recognition: the samples of only known families are presented during training, while the recognition system is expected to recognize samples from both known and...

The dataset used in this paper is a liquid state machine (LSM) with a liquid composed of LIF neurons. The liquid is a set of recurrent spiking neurons that are randomly...

The dataset used in the paper is a finite-size branching network model with various connectivity structures, ranging from spatially arranged networks to random or all-to-all...

The dataset used in the paper is a synthetic dataset generated by teacher networks for a given true rank k∗.

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

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...