28 datasets found

The dataset used for gradient adversarial training of neural networks.

Six-bit quantization can effectively reduce the size of large language models and preserve the model quality consistently across varied applications.

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.

Memory Association Networks (MANs) that memorize and remember any data. This neural network has two memories. One consists of a queue-structured short-term memory to solve the...

A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper.

The dataset used in the paper is a toy model, an MLP with a single linear hidden unit (1MLP) f (x) = w2w1x, which allows us to compare BP and PC exactly.

Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection...

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

Machine learning and deep learning methods for cybersecurity

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

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.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

The C-GAN dataset is used for training and validation of the Conditional Generative Adversarial Network model.