https://github.com/davidanugraha/proxylm
공식 코드ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
Papers with Code 아카이브
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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/davidhofmeyr/nnec
공식 코드https://github.com/davidinouye/adp
공식 코드https://github.com/daya6489/DriveML
공식 코드https://github.com/dbolya/parc
공식 코드Scalable Diverse Model Selection for Accessible Transfer Learning
https://github.com/deaglanbartlett/katz
공식 코드https://github.com/deeppsp/cinc2022
공식 코드Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram
https://github.com/dhimasryan/Zero_Shot_Model_Selection
공식 코드https://github.com/dionhaefner/rogue-wave-discovery
공식 코드https://github.com/divyat09/cate-estimator-selection
공식 코드Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
https://github.com/djstrouse/information-bottleneck
공식 코드https://github.com/dki-lab/few-shot-bioie
공식 코드Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
https://github.com/dlyuangod/mlp-kan
공식 코드https://github.com/doerlbh/UnsupervisedAttentionMechanism
공식 코드Unsupervised Attention Mechanism across Neural Network Layers
https://github.com/doerlbh/topologicalrsa
공식 코드https://github.com/doheonhan/anyloss
공식 코드AnyLoss: Transforming Classification Metrics into Loss Functions
https://github.com/dpbayes/generative-ensemble-bias-variance-decomposition
공식 코드A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets
https://github.com/dpiras/cosmopower-jax
공식 코드https://github.com/dswatson/cpi
공식 코드Testing Conditional Independence in Supervised Learning Algorithms
https://github.com/dswatson/cpi_paper
공식 코드Testing Conditional Independence in Supervised Learning Algorithms
https://github.com/dteney/collages-dataset
공식 코드https://github.com/dwanzhang-ai/seppo
공식 코드SePPO: Semi-Policy Preference Optimization for Diffusion Alignment
https://github.com/dynamicslab/SINDy-PI
공식 코드SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics
https://github.com/ebtisaamcs/rkd
공식 코드Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks
https://github.com/edchengg/model_selection
공식 코드https://github.com/egordm/mgtcom
공식 코드https://github.com/ejhigson/bsr
공식 코드Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning
https://github.com/el-hult/lal
공식 코드https://github.com/elisejiuqizhang/RLMSAD
공식 코드Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection
https://github.com/eliselyhan/arw
공식 코드Model Assessment and Selection under Temporal Distribution Shift
https://github.com/elseml/deephierarchicalmodelcomparison
공식 코드A Deep Learning Method for Comparing Bayesian Hierarchical Models
https://github.com/epimath/lasso-ode
공식 코드https://github.com/ethanjperez/true_few_shot
공식 코드https://github.com/eugenium/MMD
공식 코드A Test of Relative Similarity For Model Selection in Generative Models
https://github.com/evfro/kdd2018
공식 코드https://github.com/explainableml/probvlm
공식 코드ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.