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/ivanpeng0414/nonparametric-estimation-via-variance-reduced-sketching

공식 코드

Multivariate Density Estimation via Variance-Reduced Sketching

https://github.com/izzak98/dist-pred

공식 코드

Generalized Distribution Prediction for Asset Returns

https://github.com/j4sabuda/braced-fourier-continuation-and-regression

공식 코드

Braced Fourier Continuation and Regression for Anomaly Detection

https://github.com/jaburke166/gaussian_process_edge_trace

공식 코드

Edge Tracing using Gaussian Process Regression

https://github.com/jackhu-bme/srrd

공식 코드

CT-based Subchondral Bone Microstructural Analysis in Knee Osteoarthritis via MR-Guided Distillation Learning

https://github.com/jackmiller2003/tiny-gen

공식 코드

Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity

https://github.com/jackonelli/ensemble_distr_distillation

공식 코드

A general framework for ensemble distribution distillation

https://github.com/jacksoneshbaugh/lambda-linearity-score

공식 코드

Fidelity Isn't Accuracy: When Linearly Decodable Functions Fail to Match the Ground Truth

https://github.com/jacobi93/alpha-iou

공식 코드

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

https://github.com/jagielski/manip-ml

공식 코드

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

https://github.com/jajajang/lowpopart

공식 코드

Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits

https://github.com/jajajang/sparse

공식 코드

PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits

https://github.com/jakobbossek/oneshot

공식 코드

One-Shot Decision-Making with and without Surrogates

https://github.com/james-simon/eigenlearning

공식 코드

The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks

https://github.com/jamesrobertlloyd/gp-structure-search

공식 코드

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

https://github.com/jamesrobertlloyd/gpss-research

공식 코드

Automatic Construction and Natural-Language Description of Nonparametric Regression Models

https://github.com/janzing/janzing.github.io

공식 코드

Causal Regularization

https://github.com/jarekduda/sgd-ogr-hessian-estimator

공식 코드

Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directions

https://github.com/jasonge27/picasso

공식 코드

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

https://github.com/jasonma2016/gofar

공식 코드

How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression

https://github.com/jaydu1/ood-ridge

공식 코드

Optimal Ridge Regularization for Out-of-Distribution Prediction

https://github.com/jbloom22/DASH

공식 코드

Secure multi-party linear regression at plaintext speed

https://github.com/jbrich95/pinnev

공식 코드

Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks

https://github.com/jdrugo/vb_linear

공식 코드

Variational Bayesian inference for linear and logistic regression

https://github.com/jeanollion/distnet

공식 코드

DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine

https://github.com/jeffwang87/rfr_af

공식 코드

Optimal Activation Functions for the Random Features Regression Model

https://github.com/jernelv/SpecAnalysis

공식 코드

Convolutional neural networks for classification and regression analysis of one-dimensional spectral data

https://github.com/jess-jpg-txt/dae_for_adv_attacks_in_mimo

공식 코드

Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders

https://github.com/jesse-islam/cbnn

공식 코드

Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions

https://github.com/jesusgl86/fdap01

공식 코드

Functional data learning using convolutional neural networks

https://github.com/jhb86253817/PIPNet

공식 코드

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

https://github.com/jhb86253817/stld

공식 코드

Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach

https://github.com/jiabei-yang/CIT

공식 코드

Causal Interaction Trees: Tree-Based Subgroup Identification for Observational Data

https://github.com/jiachangliu/okridge

공식 코드

OKRidge: Scalable Optimal k-Sparse Ridge Regression

https://github.com/jiahaoLjh/PlaneSweepPose

공식 코드

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo

https://github.com/jiangnanhugo/cvgp

공식 코드

Symbolic Regression via Control Variable Genetic Programming

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