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
Automated Model Selection for Tabular Data
Structured data in the form of tabular datasets contain features that are distinct and discrete, with varying individual and relative importances to the target.
GeoGalactica: A Scientific Large Language Model in Geoscience
To our best knowledge, it is the largest language model for the geoscience domain.
Probabilistic Modeling for Sequences of Sets in Continuous-Time
In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model.
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation Tasks
Beyond investigating these research questions above, we discuss the foundational concept of annotation complexity, present a new aggregation model as a bridge between traditional models and our own, and contribute a new semi-supervised learning method for complex label aggregation that outperforms prior work.
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting
Our method clearly outperforms other model selection approaches - on average, it only requires 20% of computation costs for recommending models with 90% of the best-possible quality.
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
mcg-nju/bivdiff
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Now text-to-image foundation models are widely applied to various downstream image synthesis tasks, such as controllable image generation and image editing, while downstream video synthesis tasks are less explored for several reasons.
Towards Measuring Representational Similarity of Large Language Models
Understanding the similarity of the numerous released large language models (LLMs) has many uses, e. g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well.
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking
This paper presents LEMR (Label-Efficient Model Ranking) and introduces the MoraBench Benchmark.
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors
There is a positive correlation between PSR scores and ASR performance, suggesting that phonetic information extracted by monolingual SSL models can be used for downstream tasks in cross-lingual settings.
Machine-Guided Discovery of a Real-World Rogue Wave Model
dionhaefner/rogue-wave-discovery
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Yet, it is still not clear how we can use the superior pattern matching abilities of machine learning models for scientific discovery.
