Papers with Code 아카이브

regression

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하위 태스크

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평가표

6

보존된 평가 기록

15

코드

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

https://github.com/deepinsight/insightface

공식 코드

Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression

https://github.com/deepintostreams/mcgan

공식 코드

MCGAN: Enhancing GAN Training with Regression-Based Generator Loss

https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks

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Gaussian Gated Linear Networks

https://github.com/deepmind/neural-processes

공식 코드

Attentive Neural Processes

https://github.com/deepmind/neural_testbed

공식 코드

Evaluating High-Order Predictive Distributions in Deep Learning

https://github.com/deezer/MusicGenreTranslation

공식 코드

Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation

https://github.com/delicious-ai/ddpn

공식 코드

Flexible Heteroscedastic Count Regression with Deep Double Poisson Networks

https://github.com/dependable-cps/adversarial-MTSR

공식 코드

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

https://github.com/deshen24/panel-data-regressions

공식 코드

Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data

https://github.com/devintdha/diffusion-counterfactuals-for-image-regressors

공식 코드

Diffusion Counterfactuals for Image Regressors

https://github.com/devmotion/calibration_iclr2021

공식 코드

Calibration tests beyond classification

https://github.com/dgblack/hsibrain

공식 코드

A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors

https://github.com/dholzmueller/bmdal_reg

공식 코드

A Framework and Benchmark for Deep Batch Active Learning for Regression

https://github.com/dholzmueller/pytabkit

공식 코드

Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data

https://github.com/dholzmueller/universal_double_descent

공식 코드

On the Universality of the Double Descent Peak in Ridgeless Regression

https://github.com/diagnijmegen/tumor-percentage-mil-regression

공식 코드

"No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images

https://github.com/dial-rpi/regression-metric-loss

공식 코드

Regression Metric Loss: Learning a Semantic Representation Space for Medical Images

https://github.com/diegothomas/TetraTSDF

공식 코드

TetraTSDF: 3D human reconstruction from a single image with a tetrahedral outer shell

https://github.com/differential-machine-learning/notebooks

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Axes that matter: PCA with a difference

https://github.com/diliadis/deepmtp

공식 코드

Hyperparameter optimization in deep multi-target prediction

https://github.com/dimsum2k/drswk

공식 코드

Distribution Regression with Sliced Wasserstein Kernels

https://github.com/dionhaefner/rogue-wave-discovery

공식 코드

Machine-Guided Discovery of a Real-World Rogue Wave Model

https://github.com/dipendra009/IRNet

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

https://github.com/dispink/xpt

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MAX: Masked Autoencoder for X-ray Fluorescence in Geological Investigation

https://github.com/diux-xview/xview3-reference

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xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery

https://github.com/dixianzhu/far

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Gradient Aligned Regression via Pairwise Losses

https://github.com/dixianzhu/gar

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Gradient Aligned Regression via Pairwise Losses

https://github.com/dlalth557/ActMST

공식 코드

Activating Self-Attention for Multi-Scene Absolute Pose Regression

https://github.com/dlej/sketched-ridge

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Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning

https://github.com/dlr-mi/pim

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Can a single neuron learn predictive uncertainty?

https://github.com/dluvizon/pose-regression

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Human Pose Regression by Combining Indirect Part Detection and Contextual Information

https://github.com/dmbee/seglearn

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Seglearn: A Python Package for Learning Sequences and Time Series

https://github.com/dmollaaliod/bioasq8b-public

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Query Focused Multi-document Summarisation of Biomedical Texts

https://github.com/dobriban/Dist

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Distributed linear regression by averaging

https://github.com/dobriban/dist_ridge

공식 코드

WONDER: Weighted one-shot distributed ridge regression in high dimensions

https://github.com/dobriban/high-dim-risk-experiments

공식 코드

High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

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