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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cheb-ai/python-chebai
공식 코드https://github.com/chen-xin-94/Deep-learning-based-position-detection-for-hydraulic-cylinders-using-scattering-parameters
공식 코드Deep Learning-Based Position Detection for Hydraulic Cylinders Using Scattering Parameters
https://github.com/chengstark/interp.-eeg-public
공식 코드https://github.com/chengstark/siamaf
공식 코드https://github.com/chengxihan/hanet-cd
공식 코드https://github.com/chenxinyan-sg/wifi-csi-sensing-benchmark
공식 코드SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
https://github.com/chenyilan/Y-Net
공식 코드https://github.com/chenzhoy/fractional-dynamics-foster-deep-learning-of-copdstage-prediction
공식 코드Fractional dynamics foster deep learning of COPD stage prediction
https://github.com/cheungdaven/DeepRec
공식 코드DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
https://github.com/choasma/HSIC-Bottleneck
공식 코드https://github.com/chrisisking/rule-by-example
공식 코드Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection
https://github.com/christa60/dynens
공식 코드Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
https://github.com/chuhuaxian/HF-RGBD
공식 코드Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution
https://github.com/chuong/cattle_identification_action_recognition
공식 코드https://github.com/cimagroup/simap-layer
공식 코드https://github.com/cirquit/presto
공식 코드Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
https://github.com/cleardusk/MeGlass
공식 코드https://github.com/clementinboittiaux/homography-loss-function
공식 코드https://github.com/clinicalml/TabLLM
공식 코드TabLLM: Few-shot Classification of Tabular Data with Large Language Models
https://github.com/clu5/aaai22
공식 코드Fair Conformal Predictors for Applications in Medical Imaging
https://github.com/clu5/lumbar-conformal
공식 코드https://github.com/cmb-chula/recasnet
공식 코드ReCasNet: Improving consistency within the two-stage mitosis detection framework
https://github.com/cmu-catalyst/collage
공식 코드Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
https://github.com/cmzuo11/dcca
공식 코드https://github.com/cnjianliu/awesome-object-pose-estimation
공식 코드Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
https://github.com/codelzz/vulnerabilities4dlsystem
공식 코드On Security Weaknesses and Vulnerabilities in Deep Learning Systems
https://github.com/codingclaire/structural-code-understanding
공식 코드A Survey of Deep Learning Models for Structural Code Understanding
https://github.com/cogsys-tuebingen/labpal
공식 코드https://github.com/coin-dataset/annotations
공식 코드https://github.com/colepatten/DeepLearningSourceID
공식 코드https://github.com/comedialkb/learning_with_passive_optical_nonlinear_mapping
공식 코드Nonlinear optical encoding enabled by recurrent linear scattering
https://github.com/congliuUvA/Multivector-Neurons
공식 코드Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks
https://github.com/conor-horgan/DeepeR
공식 코드High-throughput molecular imaging via deep learning enabled Raman spectroscopy
https://github.com/conor-horgan/spectrai
공식 코드https://github.com/context-aware-failure-identification/clog
공식 코드Failure Identification from Unstable Log Data using Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.