October 12, 2022 : Pretrained models for CelebA generation using blur and animorphs, and AFHQ generation using blur, added to our drive. We use the create_data.py file to split data into individual ...

In the field of neuromorphic computing, time-series prediction poses a significant challenge to recurrent neural network architectures, often requiring task-specific customization that limits the ...

The paper describes the dataset for a deeper evaluation of the machine learning models for handwritten character recognition. For that purpose, we build a dataset that, combined with existing NIST ...

Kafka-ML is a framework to manage the pipeline of Tensorflow/Keras and PyTorch (Ignite) machine learning (ML) models on Kubernetes. The pipeline allows the design, training, and inference of ML models ...

In machine learning, data is king—but often, we lack enough of it. This is where Data Augmentation becomes invaluable. It’s a method that generates new training samples by applying transformations to ...

Artificial intelligence (AI) models, frequently built using deep neural networks (DNNs), have become integral to many aspects of modern life. However, the vast amount of data they process is not ...

Abstract: Including Artificial Neural Networks in embedded systems at the edge allows applications to exploit Artificial Intelligence capabilities directly within devices operating at the network ...

GPT (Generative Pre-trained Transformer) models, developed by OpenAI, are pre-trained language models specifically designed for text generation. These models can generate highly coherent, contextually ...

TensorFlow has emerged as one of the most popular frameworks for building machine learning models. Whether you are a beginner or an experienced data scientist, understanding how to build AI models ...

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow, allowing ...

Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As ...