Papers with Code 아카이브
Handwritten Digit Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/GabrieleLagani/SpikingGrid
공식 코드Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation
https://github.com/PaddlePaddle/Quantum
공식 코드VSQL: Variational Shadow Quantum Learning for Classification
https://github.com/Sufianlab/BDNet
공식 코드https://github.com/fahim-sikder/Bangla-Digit-Generation-GAN
공식 코드https://github.com/fastmachinelearning/hls4ml
공식 코드Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
https://github.com/gyh5421/unified_deep_clustering
공식 코드https://github.com/hls-fpga-machine-learning/hls4ml
공식 코드Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
https://github.com/jwwthu/MNIST-MIX
공식 코드MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset
https://github.com/mak-sim/lst-2d
공식 코드Compact and Efficient Neural Networks for Image Recognition Based on Learned 2D Separable Transform
https://github.com/martaandronic/polylut
공식 코드PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference
https://github.com/ntu-aiot/ObfNet
공식 코드Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference
https://github.com/plelievre/int_grad_corr
공식 코드Integrated Gradient Correlation: a Dataset-wise Attribution Method
https://github.com/prasunroy/air-writing
공식 코드A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing
https://github.com/progirep/planet
공식 코드Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
https://github.com/ruidazeng/stratus
공식 코드Cloud-Based Deep Learning: End-To-End Full-Stack Handwritten Digit Recognition
https://github.com/sunkairan/MapReduce-Based-Deep-Learning
공식 코드Large-scale Artificial Neural Network: MapReduce-based Deep Learning
https://github.com/sunke123/FW-Net
공식 코드Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding
https://github.com/xcsf-dev/xcsf
공식 코드https://github.com/yrahul3910/adaptive-lr-dnn
공식 코드LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
https://github.com/BharadwajYellapragada/Effective-Handwritten-Digit-Recognition-using-Deep-Convolution-Neural-Network
https://github.com/Brain-Inspired-Computing/Final-Project
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
https://github.com/KyotoSunshine/CNN-for-handwritten-kanji
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
https://github.com/MindSpore-scientific/code-4/tree/main/Integrated-Gradient
Integrated Gradient Correlation: a Dataset-wise Attribution Method
https://github.com/adheep04/LeNet-5
https://github.com/ari9dam/ILPME
Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples
https://github.com/jsalbert/biotorch
https://github.com/khanu263/exploring-mnist-mix
MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset
https://github.com/mohammadimathstar/grlgq
https://github.com/sahamath/SYM-Net
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
https://github.com/sahamath/sym-netv1
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
https://github.com/tej-prash/GeneExpression
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
https://github.com/willwx/sign-symmetry
https://github.com/yrahul3910/symnet
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.