Papers Handwritten Digit Recognition
“Handwritten Digit Recognition” 태그가 달린 논문 115편 · 필터 해제
myMNIST: Benchmark of PETNN, KAN, and Classical Deep Learning Models for Burmese Handwritten Digit Recognition
We present the first systematic benchmark on a standardized iteration of the publicly available Burmese Handwritten Digit Dataset (BHDD), which we have designated as myMNIST Benchmarking. While BHDD serves as a foundatio…
Handwritten Digit RecognitionAn Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification
This paper presents a novel hardware system for high-speed, event-sparse sampling-based electronic skin (e-skin)that integrates sensing and neuromorphic computing. The system is built around a 16x16 piezoresistive tactil…
Handwritten Digit RecognitionReward-Modulated Local Learning in Spiking Encoders: Controlled Benchmarks with STDP and Hybrid Rate Readouts
This paper presents a controlled empirical study of biologically motivated local learning for handwritten digit recognition. We evaluate an STDP-inspired competitive proxy and a practical hybrid benchmark built on the sa…
Handwritten Digit RecognitionRAMAN: Resource-efficient ApproxiMate Posit Processing for Algorithm-Hardware Co-desigN
Edge-AI applications still face considerable challenges in enhancing computational efficiency in resource-constrained environments. This work presents RAMAN, a resource-efficient and approximate posit(8,2)-based Multiply…
Handwritten Digit RecognitionComputational EfficiencyApplication of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors
This paper presents a machine learning-based approach to correct inference errors caused by stuck-at faults in fully analog ReRAM-based neuromorphic circuits. Using a Design-Technology Co-Optimization (DTCO) simulation f…
Handwritten Digit RecognitionLow Power Approximate Multiplier Architecture for Deep Neural Networks
This paper proposes an low power approximate multiplier architecture for deep neural network (DNN) applications. A 4:2 compressor, introducing only a single combination error, is designed and integrated into an 8x8 unsig…
Handwritten Digit RecognitionImage DenoisingNovel Approaches to Artificial Intelligence Development Based on the Nearest Neighbor Method
Modern neural network technologies, including large language models, have achieved remarkable success in various applied artificial intelligence applications, however, they face a range of fundamental limitations. Among …
Handwritten Digit RecognitionSFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
Hardware accelerators are essential for achieving low-latency, energy-efficient inference in edge applications like image recognition. Spiking Neural Networks (SNNs) are particularly promising due to their event-driven a…
Handwritten Digit RecognitionDevanagari Digit Recognition using Quantum Machine Learning
Handwritten digit recognition in regional scripts, such as Devanagari, is crucial for multilingual document digitization, educational tools, and the preservation of cultural heritage. The script's complex structure and l…
Handwritten Digit RecognitionQuantum Machine LearningCompact and Efficient Neural Networks for Image Recognition Based on Learned 2D Separable Transform
The paper presents a learned two-dimensional separable transform (LST) that can be considered as a new type of computational layer for constructing neural network (NN) architecture for image recognition tasks. The LST ba…
Handwritten Digit RecognitionAn Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
Federated learning is a novel decentralized learning architecture. During the training process, the client and server must continuously upload and receive model parameters, which consumes a lot of network transmission re…
Federated LearningHandwritten Digit RecognitionHandwritten Digit Recognition: An Ensemble-Based Approach for Superior Performance
Handwritten digit recognition remains a fundamental challenge in computer vision, with applications ranging from postal code reading to document digitization. This paper presents an ensemble-based approach that combines …
Data AugmentationHandwritten Digit RecognitionHandling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees
Missing feature values are a significant hurdle for downstream machine-learning tasks such as classification. However, imputation methods for classification might be time-consuming for high-dimensional data, and offer fe…
Drug DiscoveryHandwritten Digit RecognitionImputationMissing ValuesMachine Unlearning using Forgetting Neural Networks
Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is desired sometimes for an ML model to forget part of the data it w…
Handwritten Digit RecognitionMachine UnlearningApproaching Metaheuristic Deep Learning Combos for Automated Data Mining
Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of …
Deep LearningDimensionality ReductionHandwritten Digit RecognitionFractional signature: a generalisation of the signature inspired by fractional calculus
In this paper, we propose a novel generalisation of the signature of a path, motivated by fractional calculus, which is able to describe the solutions of linear Caputo controlled FDEs. We also propose another generalisat…
Handwritten Digit RecognitionIntegrated Gradient Correlation: a Dataset-wise Attribution Method
Attribution methods are primarily designed to study the distribution of input component contributions to individual model predictions. However, some research applications require a summary of attribution patterns across …
Handwritten Digit RecognitionAdvancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning
As deep learning continues to evolve, we have observed huge breakthroughs in the fields of medical imaging, video and frame generation, optical character recognition (OCR), and other domains. In the field of data analysi…
Handwritten Digit RecognitionOptical Character RecognitionOptical Character Recognition (OCR)Transfer LearningGeneralized Relevance Learning Grassmann Quantization
Due to advancements in digital cameras, it is easy to gather multiple images (or videos) from an object under different conditions. Therefore, image-set classification has attracted more attention, and different solution…
Activity RecognitionFace RecognitionHandwritten Digit RecognitionObject Recognition+1Verification 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. The approach has been implemented in…
Handwritten Digit RecognitionObjectobject-detectionObject Detection