21 datasets found

A new approach to a value network architecture for the game Go, called a multi-labelled (ML) value network. The ML value network has the three advantages, offering different...

A comprehensive dataset featuring ten distinct hen breeds, capturing unique characteristics and traits of each breed.

The dataset used in the paper is the MSLR-WEB30K dataset and the Yahoo! LTR dataset, which are the largest public LTR datasets from commercial search engines.

A survey on machine learning and deep learning methods for intrusion detection systems

The dataset used in the paper is a publicly available dataset from Kaggle, used for demonstrating the effectiveness of the Lai loss function.

DaCeML is a Data-Centric Machine Learning framework that provides a simple, flexible, and customizable pipeline for optimizing training of arbitrary deep neural networks.

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and...

The dataset consists of 20 malware families. Three of these malware families, namely, Winwebsec, Zeroaccess, and Zbot, are from the Malicia dataset, while the remaining 17...

The paper proposes a Bagging Deep Learning Training Framework (BEND) based on efficient neural network diffusion.

This dataset contains images of cats and dogs, which is used for training deep neural networks.

This dataset is used to test the robustness of watermarking methods against adaptive attacks.

The dataset used in this paper is a collection of real-world, mutants, and synthetic training programs.

Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more...

The dataset used for the experiments of the paper "CORE-PERIPHERY PRINCIPLE GUIDED REDISIGN OF SELF-ATTENTION IN TRANSFORMERS"

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

The dataset is used to test the proposed generalized residue network (gResNet) framework for learning unknown governing equations from observational data.

The MNIST and CIFAR-10 datasets are used to test the theory suggesting the existence of many saddle points in high-dimensional functions.

This paper proposes BERT, a pre-trained deep bidirectional transformer for language understanding.

The datasets used in the paper are described as follows: WikiMIA, BookMIA, Temporal Wiki, Temporal arXiv, ArXiv-1 month, Multi-Webdata, LAION-MI, Gutenberg.

The dataset used in the paper is a population of trained NN models, from the model zoo dataset (Sch¨urholt et al., 2022c).