Papers with Code 아카이브

Model Selection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

코드

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

https://github.com/gentopia-ai/gentopia

Specializing Smaller Language Models towards Multi-Step Reasoning

https://github.com/gotutiyan/gec-metrics

BERTScore: Evaluating Text Generation with BERT

https://github.com/heinerkremer/conditional-moment-restrictions

Deep Generalized Method of Moments for Instrumental Variable Analysis

https://github.com/heysemkaya/amofa

Adaptive Mixtures of Factor Analyzers

https://github.com/hughsalimbeni/bayesian_benchmarks

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

https://github.com/huybery/MvDSCN

Multi-view Deep Subspace Clustering Networks

https://github.com/inouye-lab/feddg_benchmark

In Search of Lost Domain Generalization

https://github.com/ir2718/semantic-similarity-scoring

Population Based Training of Neural Networks

https://github.com/iwasakishuto/DeepScreening

Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities

https://github.com/ja-thomas/autoxgboostMC

Automatic Gradient Boosting

https://github.com/jameshorine/fastFeatures

Bolasso: model consistent Lasso estimation through the bootstrap

https://github.com/jasonzhang929/BVFT_empirical_experiments

Batch Value-function Approximation with Only Realizability

https://github.com/jere1882/RRL

Automatic Catalog of RRLyrae from $\sim$ 14 million VVV Light Curves: How far can we go with traditional machine-learning?

https://github.com/jere1882/RRL_classification

Automatic Catalog of RRLyrae from $\sim$ 14 million VVV Light Curves: How far can we go with traditional machine-learning?

https://github.com/jindongwang/transferlearning

Easy Transfer Learning By Exploiting Intra-domain Structures

https://github.com/jkbren/ps-gpucbpe

Optimal design of experiments to identify latent behavioral types

https://github.com/jonas-becker/text-generation

BERTScore: Evaluating Text Generation with BERT

https://github.com/juangamella/stars

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

https://github.com/judydnguyen/pardon-feddg

In Search of Lost Domain Generalization

https://github.com/kklipski/ALHE-projekt

Population Based Training of Neural Networks

https://github.com/klemens-floege/svn_ensembles

Laplace Redux -- Effortless Bayesian Deep Learning

https://github.com/kowshikthopalli/DREAME

In Search of Lost Domain Generalization

https://github.com/kparaschakis/bbc_algorithm

Post-Selection Confidence Bounds for Prediction Performance

https://github.com/ksheng-/sparse-nn

Learning Sparse Neural Networks through $L_0$ Regularization

https://github.com/kshitijd20/RSA-CVPR19-release

Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning

https://github.com/lightning-uq-box/lightning-uq-box

Laplace Redux -- Effortless Bayesian Deep Learning

https://github.com/liqimai/gcn

Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

https://github.com/lixixibj/tsImage-forecasting

Forecasting with time series imaging

https://github.com/lovit/KoBERTScore

BERTScore: Evaluating Text Generation with BERT

https://github.com/lovit/ko-BERTScore

BERTScore: Evaluating Text Generation with BERT

https://github.com/luh-dbs/mate

ARDA: Automatic Relational Data Augmentation for Machine Learning

https://github.com/machine-intelligence-laboratory/OptimalNumberOfTopics

How Many Topics? Stability Analysis for Topic Models

https://github.com/maryam089/sdvit

In Search of Lost Domain Generalization

https://github.com/matsuolab/t3a

In Search of Lost Domain Generalization

https://github.com/maxwestphal/SEPM.PUB

A multiple testing framework for diagnostic accuracy studies with co-primary endpoints

https://github.com/miccai2022-155/autoseg

One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation

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