Traditional deep learning models are trained at a centralized server using data samples collected from users. Such often include private information, which the users may not be willing to share. Federated (FL) is an emerging approach train such without requiring share their data. FL consists of iterative procedure, where in each iteration copy model locally. The then collects individual updates...

Neural maps and Learning Vector Quantizer are fundamental paradigms in neural vector quantization based on Hebbian learning. The beginning of this field dates back over twenty years with strong progress in theory and outstanding applications. Their success lies in its robustness and simplicity in application whereas the mathematics beyond is rather difficult. We provide an overview on recent ac...

Many image compression techniques require the quantization of multiple vector sources with significantly different distributions. With vector quantization (VQ), these sources are optimally quantized using separate codebooks, which may collectively require an enormous memory space. Since storage is limited in most applications, a convenient way to gracefully trade between performance and storage...