Handwritten Digit Recognition
23 papers with code • 1 benchmarks • 5 datasets
Latest papers with no code
Generalized Relevance Learning Grassmann Quantization
The proposed model returns a set of prototype subspaces and a relevance vector.
Verification for Object Detection -- IBP IoU
We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric.
Digital-analog hybrid matrix multiplication processor for optical neural networks
Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words.
Polariton lattices as binarized neuromorphic networks
We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through non-resonant optical pumping.
NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks
In this article, we introduce NeuroWrite, a unique method for predicting the categorization of handwritten digits using deep neural networks.
Neuromorphic Hebbian learning with magnetic tunnel junction synapses
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency.
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks
The results after the comprehensive analysis of attack strategies clearly convey that untargeted and distance-based poisoning attacks have greater impacts in evading the classifier outcomes compared to targeted attacks in SFL
Quantum Neural Network for Quantum Neural Computing
Neural networks have achieved impressive breakthroughs in both industry and academia.
Image Moment Invariants to Rotational Motion Blur
Further, we achieve their invariance to similarity transform.
Composite Optimization Algorithms for Sigmoid Networks
In this paper, we use composite optimization algorithms to solve sigmoid networks.