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/chrisc36/debias

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

https://github.com/christianyang37/chiwun

How to Protect Copyright Data in Optimization of Large Language Models?

https://github.com/chronoshell/copyydscsv

HybridPose: 6D Object Pose Estimation under Hybrid Representations

https://github.com/ck37/coral-ordinal

Rank consistent ordinal regression for neural networks with application to age estimation

https://github.com/ckinateder/pytsetlinmachineparallel

A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation

https://github.com/clancyLab/NCM2018

Bayesian optimization for materials design

https://github.com/clinicalml/deepDiagnosis

Temporal Convolutional Neural Networks for Diagnosis from Lab Tests

https://github.com/clrrrr/promp_plus

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

https://github.com/code-implementation1/Code5/tree/main/MAML

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

https://github.com/code-implementation1/Code5/tree/main/MIMO-UNet

Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression

https://github.com/codecolony/re3-tensorflow

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

https://github.com/cognitivemodeling/finn

Weight Uncertainty in Neural Networks

https://github.com/cohortshapley/cohortintgrad

Deletion and Insertion Tests in Regression Models

https://github.com/continental/kiwissen-bayesian-trajectory-prediction

CoverNet: Multimodal Behavior Prediction using Trajectory Sets

https://github.com/cornellius-gp/gpytorch

Doubly Stochastic Variational Inference for Deep Gaussian Processes

https://github.com/cpark321/bayesian-neural-networks

Weight Uncertainty in Neural Networks

https://github.com/cpark321/uncertainty-deep-learning

Weight Uncertainty in Neural Networks

https://github.com/cran/fairmodels

Fairness Measures for Regression via Probabilistic Classification

https://github.com/cran/meteorits

Non-Normal Mixtures of Experts

https://github.com/cran/missRanger

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

https://github.com/cran/mlrMBO

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

https://github.com/crashmoon/MAML-Pytorch-Multi-GPUs

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

https://github.com/crivello-jc/sigma-phase-prediction

Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $σ-$phase as an example

https://github.com/crowd-ai-lab/generating-figure-captions-as-a-text-summarization-task

Generalization Error of Generalized Linear Models in High Dimensions

https://github.com/csiesheep/deepist

DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation

https://github.com/csyanbin/MAML-Pytorch-Multi-GPUs

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

https://github.com/cycloneboy/pdf_table

LORE: Logical Location Regression Network for Table Structure Recognition

https://github.com/cyn228/Yelp-Sentiment

Predicting the Sentiment Polarity and Rating of Yelp Reviews

https://github.com/czgdp1807/bayesiandl

Weight Uncertainty in Neural Networks

https://github.com/dC4rlos/TFM

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/dahtah/DPP.jl

Determinantal Point Processes for Coresets

https://github.com/dahtah/determinantal.jl

Determinantal Point Processes for Coresets

https://github.com/damedollaforthree/nrml

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

https://github.com/danfeiX/RPN

Regression Planning Networks

https://github.com/dangwalp/qg

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

https://github.com/danielgordon10/re3-tensorflow

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.