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/hz-ants/HybridPose

HybridPose: 6D Object Pose Estimation under Hybrid Representations

https://github.com/hz-ants/Pix2Pose

Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation

https://github.com/hzn666/rlbid_ea

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

https://github.com/iQuHACK/2021_Surfers

QUBO Formulations for Training Machine Learning Models

https://github.com/iancovert/sage

Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression

https://github.com/ibayer/fastFM

fastFM: A Library for Factorization Machines

https://github.com/icc2115/sage

Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression

https://github.com/iclavera/meta-mb

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

https://github.com/icoz69/DeepEMD

Meta-learning with differentiable closed-form solvers

https://github.com/ido90/roml-maml

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

https://github.com/iiyama-lab/PS-VAEs

Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift

https://github.com/imanlab/deep_movement_primitives

Geometric Loss Functions for Camera Pose Regression with Deep Learning

https://github.com/inesylla/energy-disaggregation-DL

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

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

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

https://github.com/ip200/venn-abers

Venn-Abers predictors

https://github.com/ischeinfeld/natality

Generalized Random Forests

https://github.com/itouchz/Neural-Predictor-Tensorflow

Neural Predictor for Neural Architecture Search

https://github.com/itsrainingdata/sparsebn

Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression

https://github.com/jackaduma/NLP4CyberSecurity

Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification

https://github.com/jakerhodes/RF-GAP-Python

Geometry- and Accuracy-Preserving Random Forest Proximities

https://github.com/jakerhodes/RF-GAP-R

Geometry- and Accuracy-Preserving Random Forest Proximities

https://github.com/jameshorine/fastFeatures

Bolasso: model consistent Lasso estimation through the bootstrap

https://github.com/jasonyzhang/raydiffusion

Cameras as Rays: Pose Estimation via Ray Diffusion

https://github.com/jehangiramjad/tslib

Model Agnostic Time Series Analysis via Matrix Estimation

https://github.com/jelleman8/TractSeg

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

https://github.com/jessiedbq/Auto_Sci-master

Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs

https://github.com/jhcircle/less-is-more

LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

https://github.com/jiajunhua/facebookresearch-MUSE

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

https://github.com/jiang-du/BlazePose-tensorflow

BlazePose: On-device Real-time Body Pose tracking

https://github.com/jiean001/models_m/tree/main/DecoMR

3D Human Mesh Regression with Dense Correspondence

https://github.com/jiho-yesnlp/qytl

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

https://github.com/jimmy0087/faceai-master

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

https://github.com/jingw2/demand_forecast

A Multi-Horizon Quantile Recurrent Forecaster

https://github.com/jiwei0921/Saliency-Evaluation-Toolbox

Disentangled High Quality Salient Object Detection

https://github.com/jmarecek/OnlineLDS

On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters

https://github.com/joaoreis-feup/hyper_process_model

Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis

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