Papers with Code 아카이브

regression

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하위 태스크

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6

보존된 평가 기록

15

코드

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

https://github.com/lmn-extracts/dcn_plus

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

https://github.com/longhaiSK/HTLR

Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection

https://github.com/lt-nlp-lab-unibo/ethical-survey-speech

Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge

https://github.com/lucadellalib/bayestorch

Weight Uncertainty in Neural Networks

https://github.com/lucadellalib/bdl-rul-svgd

Weight Uncertainty in Neural Networks

https://github.com/lucadellalib/sac-beta

Pathwise Derivatives Beyond the Reparameterization Trick

https://github.com/lucasxlu/ComboLoss

SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

https://github.com/lucianacendon/simile

Smooth Imitation Learning for Online Sequence Prediction

https://github.com/lucidrains/ema-pytorch

Switch EMA: A Free Lunch for Better Flatness and Sharpness

https://github.com/lucidrains/lie-transformer-pytorch

LieTransformer: Equivariant self-attention for Lie Groups

https://github.com/luciferbobo/dae-aqa

Auto-Encoding Score Distribution Regression for Action Quality Assessment

https://github.com/luke-97/BayesByBackprop

Weight Uncertainty in Neural Networks

https://github.com/lwhyc/oneshot_weaklyseg

Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images

https://github.com/lyqcom/irn

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery

https://github.com/mac-theobio/Lab_meeting

Polynomial Regression As an Alternative to Neural Nets

https://github.com/mansimane/WormML

Numerical Coordinate Regression with Convolutional Neural Networks

https://github.com/manujosephv/pytorch_tabular

GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

https://github.com/marcobellagente93/Bayesian_Regression

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

https://github.com/marcovirgolin/SimpleGP

Learning a Formula of Interpretability to Learn Interpretable Formulas

https://github.com/marcovirgolin/UncertaintyEstimationInDeepNets

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

https://github.com/mari-linhares/tensorflow-maml

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

https://github.com/mariacer/regression_uncertainty_mm

Uncertainty estimation under model misspecification in neural network regression

https://github.com/marioboley/fastridge

Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization

https://github.com/marload/DistRL-TensorFlow2

Distributional Reinforcement Learning with Quantile Regression

https://github.com/marload/dist-rl-tf2

Distributional Reinforcement Learning with Quantile Regression

https://github.com/marpogaus/tensorflow-probability-bernstein-polynomial-bijector

Deep transformation models: Tackling complex regression problems with neural network based transformation models

https://github.com/martius-lab/beta-nll

Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization

https://github.com/matanatz/SAL

SALD: Sign Agnostic Learning with Derivatives

https://github.com/mathieuchal/LFT

Learning Functional Transduction

https://github.com/mattans/AgeProgression

Age Progression/Regression by Conditional Adversarial Autoencoder

https://github.com/maudzung/Complex-YOLOv4-Pytorch

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

https://github.com/maudzung/TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch

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

https://github.com/maudzung/TTNet-Realtime-for-Table-Tennis-Pytorch

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

https://github.com/maudzung/YOLO3D-YOLOv4-PyTorch

YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

https://github.com/mavillan/yolanda

DART: Dropouts meet Multiple Additive Regression Trees

https://github.com/mayank-git-hub/Text-Recognition

PixelLink: Detecting Scene Text via Instance Segmentation

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