Abstract: This paper investigates the application of autoencoder (AE) in supporting the training process of federated learning by reducing communication overhead and latency. We propose a scheduling ...

Abstract: Large-scale pre-training models have promoted the development of histopathology image analysis. However, existing self-supervised methods for histopathology images primarily focus on ...

I have been working with your excellent project,and I noticed that the pre-trained autoencoder model (autoencoder_vq_f4.pth) is designed for RGB images. However, I am currently working with gray-scale ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...

Autoencoders are a class of neural networks that aim to learn efficient representations of input data by encoding and then reconstructing it. They comprise two main parts: the encoder, which ...

The acquisition of sentence embeddings often necessitates a substantial volume of labeled data. However, in many cases and fields, labeled data is rarely accessible, and the procurement of such data ...

Aerobic exercise is hugely beneficial for health. But research increasingly shows you also need some resistance training. Here’s how to work it in. By Cindy Kuzma Anh Bui’s main focus as a physical ...