36 datasets found

A dataset for estimating measurements received over a network, using a deep neural network based approximation architecture.

This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using stochastic gradient...

The Bird10 dataset is a dataset of bird species, used for training and testing deep neural networks.

ILMPQ: An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA

Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function...

Deep neural networks (DNN) are increasingly being accelerated on application-specific hardware such as the Google TPU designed especially for deep learning. Timing speculation...

Speech recognition systems driven by Deep Neural Networks (DNNs) have revolutionized human-computer interaction through voice interfaces, which significantly facilitate our...

News session-based recommendations using deep neural networks.

The Eesen dataset is a speech recognition dataset used in the Eesen framework.

The ESPNet dataset is a speech recognition dataset used in the ESPNet framework.

The proposed approach is compared with state-of-the-art methods on the reprocessed Color Checker dataset and the NUS 8-camera dataset.

A new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances.

The pNML scheme outperforms the ERM in the accuracy and log-loss sense, it is more robust for noisy trainset and has better performance for adversarial samples.

The dataset used in this paper is a pre-trained DNN trained on the Cifar10 and Cifar100 datasets.

The dataset used for training deep neural networks using half-precision floating point numbers.

The dataset used in this paper for Automatic Modulation Classification (AMC) using deep learning.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used various neural networks, including AlexNet, VGG16, InceptionV4, ResNet50,...

The dataset used in the paper is a 2D object detection dataset, where the authors investigate the issues of achieving sufficient rigor in the arguments for the safety of machine...

The dataset used in this paper is a collection of 25 state-of-the-art deep neural networks (DNNs) with different architectures and sizes.

The TIMIT corpus is a widely used benchmark for speech recognition tasks. It contains 3,696 training utterances from 462 speakers, excluding the SA sentences. The core test set...