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/danielkelshaw/RadialBNN

Weight Uncertainty in Neural Networks

https://github.com/danielkelshaw/WeightUncertainty

Weight Uncertainty in Neural Networks

https://github.com/danielsyahputra/HydraNet

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/danielwilhelm/r-cs-ranks

Inference for Rank-Rank Regressions

https://github.com/daveredrum/ScanRefer

ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

https://github.com/david-siqi-liu/yelp-sentiment-analysis

Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and Models

https://github.com/davidar/bib

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

https://github.com/davidecarminati/cufagp

Fast Approximate Multi-output Gaussian Processes

https://github.com/davidenapolitano/bones

Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression

https://github.com/davidruegamer/deepregression_tutorial

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

https://github.com/dbrzyski/SpINNEr

A Sparsity Inducing Nuclear-Norm Estimator (SpINNEr) for Matrix-Variate Regression in Brain Connectivity Analysis

https://github.com/deargen/MT-ENet

Improving evidential deep learning via multi-task learning

https://github.com/deebuls/deep_evidential_regression_loss_pytorch

Deep Evidential Regression

https://github.com/deep-symbolic-mathematics/Multimodal-Math-Pretraining

End-to-end symbolic regression with transformers

https://github.com/deepmind/conditional-neural-process

Attentive Neural Processes

https://github.com/deepmind/rgb_stacking

Critic Regularized Regression

https://github.com/deezer/MultilingualMusicGenreEmbedding

Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation

https://github.com/deftruth/torchlm

Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild

https://github.com/dengdan/seglink

PixelLink: Detecting Scene Text via Instance Segmentation

https://github.com/dennysemko/VectorizedBayesByBackprop

Weight Uncertainty in Neural Networks

https://github.com/dhavala/bqr

Estimation and Applications of Quantiles in Deep Binary Classification

https://github.com/dhruvdcoder/sparse-structured-attention

The Ordered Weighted $\ell_1$ Norm: Atomic Formulation, Projections, and Algorithms

https://github.com/dhuynh95/cryptotree

Neural Random Forests

https://github.com/diptamath/covid_fake_news

Fighting an Infodemic: COVID-19 Fake News Dataset

https://github.com/dkalpakchi/ReproducingSCAPytorch

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

https://github.com/dmolitor/bolasso

Bolasso: model consistent Lasso estimation through the bootstrap

https://github.com/dnbaker/frp

Generalization Properties of Learning with Random Features

https://github.com/dntai/dntai_fg20_affwild2_challenges

Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model

https://github.com/dontLoveBugs/DORN_pytorch

Deep Ordinal Regression Network for Monocular Depth Estimation

https://github.com/dougbrion/pytorch-deep-ensembles

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/dpclip/dpclip

Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation

https://github.com/dragen1860/MAML-Pytorch

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

https://github.com/dragen1860/MAML-Pytorch-RL

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

https://github.com/dragen1860/Reptile-Pytorch

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

https://github.com/druidowm/occamnet_socialsci

OccamNet: A Fast Neural Model for Symbolic Regression at Scale

https://github.com/dslisleedh/GenerativeAutoencoders-tensorflow2/blob/main/caae.py

Age Progression/Regression by Conditional Adversarial Autoencoder

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