The strategic advantage of QML continues to expand its presence in industries that deal with complex, high-dimensional data.

Most dynamical models introduced in the past were linear ... with a multisection neural network architecture and training approach." The researchers trained their RNN-based model using a four-step ...

TITLE: Dynamic Classification Using the Adaptive Competitive Algorithm for Breast Cancer Detection AUTHORS: Maryam Deldadehasl, Mohsen Jafari, Mohammad R. Sayeh KEYWORDS: Breast Cancer, Real-Time ...

This paper presents a new technique for designing a jointly optimized residual vector quantizer (RVQ). In conventional stage-by-stage design procedure, each stage codebook is optimized for that ...

A groundbreaking study by Qualcomm AI Research introduces a method known as GPTVQ, which leverages vector quantization (VQ) to enhance the size-accuracy trade-off in neural network quantization ...

These range in complexity from linear regression for numeric prediction to convolutional neural networks for image processing, transformer-based models for generative AI, and reinforcement ...

Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog ...