SOTAVerified

Model Selection

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–925 of 2050 papers

TitleStatusHype
Unsupervised Image Representation Learning with Deep Latent ParticlesCode1
Understanding new tasks through the lens of training data via exponential tiltingCode0
Verifying Learning-Based Robotic Navigation Systems—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Fusion Subspace Clustering for Incomplete Data—0
Fast Instrument Learning with Faster RatesCode0
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Differentially Private Generalized Linear Models Revisited—0
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained ModelsCode0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Differentially Private Learning with Margin Guarantees—0
Improved Group Robustness via Classifier Retraining on Independent SplitsCode0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
Trinary Tools for Continuously Valued Binary Classifiers—0
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and PseudoposteriorsCode0
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
LaF: Labeling-Free Model Selection for Automated Deep Neural Network ReusingCode0
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level PerformancesCode0
Show:102550
← PrevPage 37 of 82Next →

No leaderboard results yet.