Papers with Code 아카이브

regression

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

6

보존된 평가 기록

15

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/sremes/a2d2

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/sremes/nonstationary-spectral-kernels

Non-Stationary Spectral Kernels

https://github.com/ssatsuki/label-selection-layer

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

https://github.com/stat-lu/PhDpos

High-dimensional classification by sparse logistic regression

https://github.com/stat-ml/GeoMLE

Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension

https://github.com/stat-ml/dpp-dropout-uncertainty

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/statmixedml/lightgbmlss

Distributional Gradient Boosting Machines

https://github.com/statsu1990/deep_ensembles

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/statsu1990/regression_dropout

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/stavros0/bitcoin-price-prediction

Bayesian regression and Bitcoin

https://github.com/stefanowoerner/medimeta-pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/stephanegaiffas/AMF

AMF: Aggregated Mondrian Forests for Online Learning

https://github.com/steven7woo/fair_regression_reduction

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

https://github.com/stonybrooknlp/musique

SQuAD: 100,000+ Questions for Machine Comprehension of Text

https://github.com/stran123/solving-mlp

Solving Machine Learning Problems

https://github.com/strawberryfg/c2f-3dhm-human-caffe

Integral Human Pose Regression

https://github.com/suranakritika/Deep-pose

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/sutoiku/autostat

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

https://github.com/svedel/greattunes

A Tutorial on Bayesian Optimization

https://github.com/symoon94/YOLO-keras

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/taewankim1/uncertainty_deeplearning

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

https://github.com/tamasino52/Any-GCN

Semantic Graph Convolutional Networks for 3D Human Pose Regression

https://github.com/tangxyw/RecAlgorithm

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

https://github.com/tanyanair/segmentation_uncertainty

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

https://github.com/teaguetomesh/coresets

Practical Coreset Constructions for Machine Learning

https://github.com/teddykoker/evidential-learning-pytorch

Deep Evidential Regression

https://github.com/tejasai97/BitCoinPricePrediction

Bayesian regression and Bitcoin

https://github.com/tencentarc/umt

TALL: Temporal Activity Localization via Language Query

https://github.com/tennisonliu/bayesian-neural-network

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/tensorflow/models

Weight Uncertainty in Neural Networks

https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits

Weight Uncertainty in Neural Networks

https://github.com/themisbo/Rule-based-Evol-Bayesian-regr

Rule-based Bayesian regression

https://github.com/themisbo/auto-Rule-based-Bayesian-regr

Rule-based Bayesian regression

https://github.com/themisbo/rule-based-evol-bayesian-learn

Rule-based Bayesian regression

https://github.com/theneuralbeing/maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/thjashin/solvegp

Sparse Orthogonal Variational Inference for Gaussian Processes

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