Latest Articles for Model Evaluation

Introducing the Balance score for improved model evaluation in competitive gaming.

A look at how Random Forests estimate prediction accuracy for better data classification.

Learn how Padding Aware Neurons impact image processing in machine learning models.

This article discusses ways to enhance AI model reliability in changing environments.

Research reveals weaknesses in how table models are tested and evaluated.

ModelGiF offers a method to quantify relationships between deep learning models.

Research highlights catastrophic forgetting in multimodal language models post fine-tuning.

Assessing the accuracy of neuron explanations in language models reveals significant flaws.

This article discusses how causal concepts enhance AI's ability to generalize to new data.

A look at how Prompt Tuning improves model performance through skill neurons.

This study examines the factors affecting learning curves in Kernel Ridge Regression.

A look into how deep learning performs on tabular datasets.

Using diffusion models to improve detection of adversarial examples in machine learning.

Examining how prompt templates impact the performance of large language models.

A study reveals small language models struggle with multiple choice questions.

Examining the effects of inter-dataset code duplication on model performance metrics.

A new method to assess model accuracy without true labels.

This study assesses the performance of language models on modified math problems.

Learn how cross-validation enhances the reliability of predictive models.

This study highlights the importance of measuring uncertainty in language model evaluations.

Improving model accuracy for rare categories in long-tailed datasets.

Evaluating LLMs for their ability to grasp various aspects of context.

Discover how agents can improve foundation models for better AI outcomes.

Examining Mamba's capabilities and its hybrid model with Transformers.

A new method combines decision trees and transformers for better decision-making.

This study explores methods to improve classifier performance on imbalanced datasets.

Longer instructions enhance language model performance and reduce complexity.

A look into how we assess the quality of forecasts.

This article examines the gap between generative and evaluative abilities of AI models.

A critical look at the effectiveness of rough volatility models in financial markets.

Examining the impact of Post-Selection on model evaluation in deep learning.

A look at K-fold cross-validation and its effectiveness in model selection.

This paper analyzes the advantages of multi-head attention over single-head attention in machine learning tasks.

A new framework helps analyze explanations from large language models effectively.

A new MLP-based model improves accuracy in time series forecasting using random projection layers.

A study on kernel regression addressing overfitting and kernel function behaviors.

A look into how VLMs combine image and text processing.

A look into the significance of the Local Learning Coefficient in machine learning models.

Investigating how tokenization methods affect arithmetic tasks in language models.

This study highlights the importance of uncertainty in assessing Vision-Language Models.