Papers with Code 아카이브

parameter estimation

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

https://github.com/ibm/rgan-demo-pytorch

공식 코드

Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modeling

https://github.com/igomezv/neuralike

공식 코드

Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up

https://github.com/iip-group/blind_and_nonparametric_estimators

공식 코드

Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals

https://github.com/inspire-group/ModelPoisoning

공식 코드

Analyzing Federated Learning through an Adversarial Lens

https://github.com/intellhave/ASKER

공식 코드

A Graduated Filter Method for Large Scale Robust Estimation

https://github.com/intelligentEarth/surrogateBayeslands

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Surrogate-assisted Bayesian inversion for landscape and basin evolution models

https://github.com/isvy08/otp

공식 코드

Parameter Estimation in DAGs from Incomplete Data via Optimal Transport

https://github.com/jazzcharles/cream

공식 코드

CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping

https://github.com/jedforrest/larva-pupa-adult

공식 코드

Symbolic-numeric algorithm for parameter estimation in discrete-time models with $\exp$

https://github.com/jeovafarias/improved-roughness-estimation-sar

공식 코드

Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery

https://github.com/jeovafarias/sar-roughness-estimation-neural-nets

공식 코드

Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images

https://github.com/jesusgl86/fdap01

공식 코드

Functional data learning using convolutional neural networks

https://github.com/jmwalchessen/neural_likelihood

공식 코드

Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods

https://github.com/jnsbck/hhdifftemp

공식 코드

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations

https://github.com/johannrudi/fhn_dnn

공식 코드

Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE

https://github.com/johneverettkoo/pabm-grdpg

공식 코드

Popularity Adjusted Block Models are Generalized Random Dot Product Graphs

https://github.com/jonasw247/a-learnable-prior-improves-inverse-tumor-growth-modeling

공식 코드

A Learnable Prior Improves Inverse Tumor Growth Modeling

https://github.com/jonghyunharrylee/pyPCGA

공식 코드

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

https://github.com/jplte/deep-t1-ball-stick

공식 코드

Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning

https://github.com/jun-jieh/dyadanalysis

공식 코드

Analysis of social interactions in group-housed animals using dyadic linear models

https://github.com/jvking/bp-lda

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End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

https://github.com/jwhgdeu/tsbp

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TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation

https://github.com/kaandocal/wasserstein_inference

공식 코드

Parameter estimation for biochemical reaction networks using Wasserstein distances

https://github.com/kaushalkumarsimmons/parameter_estimation2

공식 코드

Data-driven modeling and parameter estimation of Nonlinear systems

https://github.com/kaustubhsridhar/PoE-robustness

공식 코드

Improving Neural Network Robustness via Persistency of Excitation

https://github.com/kerkelae/scnn-investigation

공식 코드

Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data

https://github.com/krikamol/kcm-test

공식 코드

Kernel Conditional Moment Test via Maximum Moment Restriction

https://github.com/kuanweih/strong_lensing_vit_resnet

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Strong Gravitational Lensing Parameter Estimation with Vision Transformer

https://github.com/labull/engineeringpatternrecognition

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Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning

https://github.com/lacava/ode-strogatz

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Contemporary Symbolic Regression Methods and their Relative Performance

https://github.com/lacerbi/ibs

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Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling

https://github.com/lcneuro/ornstein_uhlenbeck

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Parameter estimation from an Ornstein-Uhlenbeck process with measurement noise

https://github.com/lcs2-iiitd/monteclora

공식 코드

Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation

https://github.com/levishery/accelerateoed

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Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design

https://github.com/ligoudaner377/homo_translucent

공식 코드

Inverse Rendering of Translucent Objects using Physical and Neural Renderers

https://github.com/lindermanlab/salt

공식 코드

Switching Autoregressive Low-rank Tensor Models

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