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 ...

This repository contains a PyTorch implementation of the Lottery Ticket algorithm introduced by Frankle et al. in "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" [1] and ...

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 ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. A critical bottleneck for the training of large neural networks (NNs) is communication ...

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 ...

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 ...