Papers with Code 아카이브
regression
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
- 15
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/eabarnes1010/controlled_abstention_networks
Controlled abstention neural networks for identifying skillful predictions for regression problems
https://github.com/eatsleepraverepeat/emusic_net
Transfer learning for music classification and regression tasks
https://github.com/ebadrian/metadl
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/eepperly/robust-randomized-preconditioning-for-kernel-ridge-regression
Robust, randomized preconditioning for kernel ridge regression
https://github.com/eikonal-equation/Bayesian-Surveillance-Evasion
Surveillance Evasion Through Bayesian Reinforcement Learning
https://github.com/emi-group/evox
EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming
https://github.com/emilianavt/OpenSeeFace
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
https://github.com/erickgalinkin/dropout_privacy
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/erictiandong/drn
Distributional Refinement Network: Distributional Forecasting via Deep Learning
https://github.com/erosenfeld/disagree_discrep
https://github.com/eugene/spngp
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
https://github.com/facebookresearch/Horizon
Distributional Reinforcement Learning with Quantile Regression
https://github.com/facebookresearch/ReAgent
Distributional Reinforcement Learning with Quantile Regression
https://github.com/facebookresearch/detectron2/tree/master/projects/Panoptic-DeepLab
https://github.com/facebookresearch/fastText/blob/master/docs/aligned-vectors.md
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
https://github.com/fairlearn/fairlearn
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
https://github.com/fanshixiong/tracker
https://github.com/faridSam/srn
Selective Refinement Network for High Performance Face Detection
https://github.com/farrell236/SVRnet
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
https://github.com/fastforwardlabs/learning-to-learn
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/fchamroukhi/MEteorits
https://github.com/fchamroukhi/SNMoE
https://github.com/felix-laumann/Bayesian_CNN_ContinualLearning
https://github.com/felixopolka/deep-gaussian-process
Doubly Stochastic Variational Inference for Deep Gaussian Processes
https://github.com/fengyang95/pyCFTrackers
https://github.com/fengye-lu/DRN-master
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
https://github.com/fiveai/on-episodes-fsl
https://github.com/fiveai/saod
Bounding Box Regression with Uncertainty for Accurate Object Detection
https://github.com/flaviagiammarino/brits-tensorflow
https://github.com/fmpr/DeepJMQR
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems
https://github.com/fmu2/PyTorch-MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/fomorians-oss/awr
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
https://github.com/foolyc/Meta-SGD
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/fortuinlab/la-nam
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/fpramunno/InDI-implementation
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.