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Model Selection

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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/kaavyarekanar/towards-a-performance-analysis-on-pre-trained-vqa-models-for-autonomous-driving

공식 코드

Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving

https://github.com/kahramankostas/GeMID

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GeMID: Generalizable Models for IoT Device Identification

https://github.com/kaiyin97/disaster_ir

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DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management

https://github.com/kapilw25/Evaluation_generated_images

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Multimodal Benchmarking and Recommendation of Text-to-Image Generation Models

https://github.com/kari-genomics-lab/barcodebert

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BarcodeBERT: Transformers for Biodiversity Analysis

https://github.com/kdrl/WIPS

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Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities

https://github.com/kebaek/agreement-on-the-line

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Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift

https://github.com/kirthevasank/nasbot

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Neural Architecture Search with Bayesian Optimisation and Optimal Transport

https://github.com/klkeys/IHT.jl

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Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

https://github.com/knagrecha/hydra

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Hydra: A System for Large Multi-Model Deep Learning

https://github.com/knagrecha/saturn

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Saturn: An Optimized Data System for Large Model Deep Learning Workloads

https://github.com/krankile/ensemble_forecasting

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Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation

https://github.com/kstoneriv3/confounding-robust-inference

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A Convex Framework for Confounding Robust Inference

https://github.com/kw2934/krr

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Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift

https://github.com/lacerbi/infbench

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Variational Bayesian Monte Carlo

https://github.com/lacerbi/vbmc

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Variational Bayesian Monte Carlo

https://github.com/lancopku/cascadebert

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CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade

https://github.com/lanl/pyDNMFk

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Distributed Out-of-Memory NMF on CPU/GPU Architectures

https://github.com/lanl/t-elf

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

https://github.com/lasgroup/alexp

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Anytime Model Selection in Linear Bandits

https://github.com/lbaa2022/llmtaskplanning

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LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents

https://github.com/learning-curve-research/LCDB-1.1

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LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought

https://github.com/lezhangleonard/E-QUARTIC

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E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning

https://github.com/lidiamandre/NBE_classifier_depmodels

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Neural Bayes inference for complex bivariate extremal dependence models

https://github.com/linanzhang/nonconvexsparsecyclicrecovery

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Structured model selection via $\ell_1-\ell_2$ optimization

https://github.com/lingxiaoshawn/glam

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Graph Anomaly Detection with Unsupervised GNNs

https://github.com/lins-lab/ttab

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On Pitfalls of Test-Time Adaptation

https://github.com/lipryou/smlsom

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SMLSOM: The shrinking maximum likelihood self-organizing map

https://github.com/liuyue002/woundhealing

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Parameter identifiability and model selection for partial differential equation models of cell invasion

https://github.com/livingmatterlab/cann

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A new family of Constitutive Artificial Neural Networks towards automated model discovery

https://github.com/lixiaotong97/ped

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Exploring Model Transferability through the Lens of Potential Energy

https://github.com/lixin4ever/BERT-E2E-ABSA

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Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

https://github.com/lixixibj/forecasting-with-time-series-imaging

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Forecasting with time series imaging

https://github.com/liyuhui-12/drmfordg

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Direct-Effect Risk Minimization for Domain Generalization

https://github.com/localcascadeensemble/lce

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LCE: An Augmented Combination of Bagging and Boosting in Python

https://github.com/locluclak/si-seqfs-da

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Statistical Inference for Sequential Feature Selection after Domain Adaptation

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