Model Selection
494 papers with code • 0 benchmarks • 1 datasets
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.
Benchmarks
These leaderboards are used to track progress in Model Selection
Libraries
Use these libraries to find Model Selection models and implementations
Image Caption Quality Dataset
Latest papers
Realistic Model Selection for Weakly Supervised Object Localization
shakeebmurtaza/wsol_model_selection
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Our experimental results with several WSOL methods on ILSVRC and CUB-200-2011 datasets show that our noisy boxes allow selecting models with performance close to those selected using ground truth boxes, and better than models selected using only image-class labels.
The CAST package for training and assessment of spatial prediction models in R
One key task in environmental science is to map environmental variables continuously in space or even in space and time.
Model Selection with Model Zoo via Graph Learning
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i. e., model zoos.
Idea-2-3D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs
The definition of an IDEA is the composition of multimodal inputs including text, image, and 3D models.
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
facebookresearch/glemos
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NeurIPS 2023
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
Learning the mechanisms of network growth
We propose a novel model-selection method for dynamic real-life networks.
Conformal online model aggregation
Conformal prediction equips machine learning models with a reasonable notion of uncertainty quantification without making strong distributional assumptions.
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
themaxiao/ditmos
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Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources.
Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation
Synthetic users are cost-effective proxies for real users in the evaluation of conversational recommender systems.
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables
Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem.
