Latest Articles for Model Optimization

New methods improve efficiency in training smaller neural models.

A method for improving model performance through hyperparameter adjustment based on task order.

This study compares CMA-ES and GES for building better model ensembles.

A new method optimizes speech models for better performance with fewer resources.

This study focuses on improving model performance in ensembles through dissimilarity in training.

Learn about Dynamic Sparse Training and its benefits for neural network efficiency.

How pre-trained models impact performance on new data.

A new method enhances model speed and accuracy in machine learning.

Exploring quantization strategies to improve performance in large language models.

An overview of pruning and quantization methods applied to YOLOv5.

A method that enhances model performance while reducing resource needs.

New methods reduce model size while maintaining performance in computer vision tasks.

New insights on the potential of deep neural networks through optimistic estimates.

New methods to expand transformer models without losing prior training progress.

DiffTPT enhances model adaptability and accuracy through innovative data augmentation techniques.

Evol-Q enhances quantization accuracy in Vision Transformers through evolutionary search techniques.

Enhancing how TinyBERT learns from BERT for better language processing.

A novel approach reduces transformer model size with minimal impact on accuracy.

Techniques to enhance efficiency in vision models using pruning and matrix decomposition.

This article discusses bias correction for softmax layers in generative models.

Discover methods to make Vision Transformers more efficient for real-world applications.

Making vision transformers efficient for drones and mobile devices to enhance visual tasks.

Delta-LoRA streamlines fine-tuning for large language models with better performance and less resource use.

Research improves knowledge distillation methods for efficient semantic image segmentation.

A new approach improves language model performance through optimized weight rounding.

This article discusses improvements in pooling methods for transformers in supervised learning.

FedDIP optimizes communication in federated learning through dynamic pruning and regularization.

A new approach to improve Vision Transformers for mobile devices.

New method enhances Transformer models by reducing computation and memory usage.

Study explores FP8 formats for improved model efficiency and accuracy.

A new approach streamlines model design for devices with limited computing power.

Enhancing Zero-Shot NAS using bias correction for better model performance.

Combining models to boost accuracy and efficiency in deep learning.

GRANDE uses gradient descent to improve learning from tabular data.

DeeDiff improves diffusion models by skipping unnecessary steps, enhancing speed without sacrificing quality.

A new approach enhances feature learning in variational autoencoders.

A study on how parameter choices impact model performance in knowledge distillation.

Efficient low-rank training enhances CNN models for resource-limited environments.

Improving language model adaptability through selective example retrieval.

A new method improves feature selection efficiency in machine learning models.