Model Selection
497 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 with no code
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection
This position paper proposes a systematic approach towards developing a framework to help select the most effective embedding models for natural language processing (NLP) tasks, addressing the challenge posed by the proliferation of both proprietary and open-source encoder models.
Individual Text Corpora Predict Openness, Interests, Knowledge and Level of Education
For training and validation, we relied on 179 participants and held out a test sample of 35 participants.
Bayesian Nonparametrics: An Alternative to Deep Learning
Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets.
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields
Machine learning force fields (MLFFs) have emerged as a promising approach to bridge the accuracy of quantum mechanical methods and the efficiency of classical force fields.
Carbon Intensity-Aware Adaptive Inference of DNNs
DNN inference, known for its significant energy consumption and the resulting high carbon footprint, can be made more sustainable by adapting model size and accuracy to the varying carbon intensity throughout the day.
Bridge the Modality and Capacity Gaps in Vision-Language Model Selection
It then uses this matrix to transfer useful statistics of VLMs from open-source datasets to the target dataset for bridging those two gaps and enhancing the VLM's capacity estimation for VLM selection.
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
Our model selection criteria allow significantly faster and high quality model selection of Gaussian process models.
Pre-Trained Model Recommendation for Downstream Fine-tuning
As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task.
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
In this paper, we propose a time-increasing bandit algorithm TI-UCB, which effectively predicts the increase of model performances due to finetuning and efficiently balances exploration and exploitation in model selection.
A data-centric approach to class-specific bias in image data augmentation
Data augmentation (DA) enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
