The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

2012/10/19 by Li Deng · 451 citations
Computer Science · #Handwritten Text Recognition Techniques #Advanced Neural Network Applications #Advanced Image and Video Retrieval Techniques

paper · doi:10.1109/msp.2012.2211477

Abstract

In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.

Citations

• Gradient-based learning applied to document recognition

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• Vertical federated learning: a structured literature review
• CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning
• Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
• Explainable Unsupervised Anomaly Detection with Random Forest
• Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning
• Robust Compressive Phase Retrieval via Deep Generative Priors
• Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning
• Learning to Score
• Internal noise in hardware deep and recurrent neural networks helps with learning
• Networks with pixels embedding: a method to improve noise resistance in images classification
• Multistage Model for Robust Face Alignment Using Deep Neural Networks
• Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling
• Readable Twins of Unreadable Models
• Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining
• Support is All You Need for Certified VAE Training
• The Pontryagin Maximum Principle for Training Convolutional Neural Networks
• QAMA: Scalable Quantum Annealing Multi-Head Attention Operator for Deep Learning
• Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
• LEMUR Neural Network Dataset: Towards Seamless AutoML
• Quantum Image Loading: Hierarchical Learning and Block-Amplitude Encoding
• PQS (Prune, Quantize, and Sort): Low-Bitwidth Accumulation of Dot Products in Neural Network Computations
• On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning
• Proxy-Anchor and EVT-Driven Continual Learning Method for Generalized Category Discovery
• Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
• Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
• A Dataset For Computational Reproducibility
• Independence Is Not an Issue in Neurosymbolic AI
• pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
• Traversal Learning: A Lossless And Efficient Distributed Learning Framework
• Adaptive Locally Linear Embedding

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