In 1989, a computer scientist tackled the messy challenge of reading handwritten zip codes for the US Post Office. This ...
The contemporary state of machine learning and artificial intelligence is marked by an increasing reliance on black-box methodologies, where the utilization of high-level packages and automated ...
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 ...
Automated recognition of handwritten text on bank cheques is crucial for streamlining financial transactions and reducing manual errors. However, traditional systems often encounter two significant ...
Artificial Intelligence (AI) has transformed how we interact with technology, but at its core, AI relies on a fundamental building block: tensors. Think of tensors as the unsung heroes that make data ...
This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired unsupervised local learning rule for the online implementation of Hebb’s plasticity mechanism on neuromorphic ...
Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077 Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong ...
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 ...
Dr. James McCaffrey of Microsoft Research demonstrates how to fetch and prepare MNIST data for image recognition machine learning problems. Many machine learning problems fall into one of three ...
In this project, I built a model to perform handwritten digit string recognition using synthetic data generated by concatenating digits from the MNIST dataset. Different overlapping rates and paddings ...
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