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regression
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0
https://github.com/hamed-hosseini/ggcnn
Real-Time Grasp Detection Using Convolutional Neural Networks
https://github.com/hansungj/BayesianNeuralNetworkDiscretizedGibbsSampler
https://github.com/happyvictor008/High-order-GNN-LF-iter
Semantic Graph Convolutional Networks for 3D Human Pose Regression
https://github.com/hardianlawi/MTL-Homoscedastic-Uncertainty
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/hartb/faiss-gpu-feedstock
Link and code: Fast indexing with graphs and compact regression codes
https://github.com/harveyThomas4692/llmlex
Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks
https://github.com/harveydevereux/Mondrian
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
https://github.com/hayoung-kim/tf2-deep-ensemble-uncertainty
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/hbahadirsahin/bayes_by_backprop
https://github.com/heathentw/prnet-tf2
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
https://github.com/hegdepashupati/differential-dgp
https://github.com/hegdepashupati/gprn-svi
https://github.com/heinerkremer/conditional-moment-restrictions
Instrumental Variable Regression via Kernel Maximum Moment Loss
https://github.com/hfahrudin/reptile_implement_tf2
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/hibetterheyj/Note-Everyday
Learning regression and verification networks for long-term visual tracking
https://github.com/hibetterheyj/Paper-Everyday
Learning regression and verification networks for long-term visual tracking
https://github.com/hichamjanati/mtw
https://github.com/hll110/DIoU-darknet-master
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
https://github.com/hmi88/what
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://github.com/hoidn/ptychopinn
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/hollina/scul
Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis
https://github.com/howardmumu/Attack-Resistant-Federated-Learning
Attack-Resistant Federated Learning with Residual-based Reweighting
https://github.com/hoyeoplee/pytorch-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/hpcforge/gmflow
https://github.com/hrdwsong/BayesianCNN-Paddle
https://github.com/huggingface/transformers
D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement
https://github.com/hukaixuan19970627/yolov5_obb
On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited
https://github.com/hutgun11/muti_object_tracking_re3
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/huyng/incertae
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/hvak/bi-michi
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
https://github.com/hvak/mimrf-bfm
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
https://github.com/hwang787/Yelp-Sentiment
Predicting the Sentiment Polarity and Rating of Yelp Reviews
https://github.com/hyang1990/energy_constrained_compression
https://github.com/hyungrok-do/fair-glm-cvx
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.