Latest Articles for Contrastive Learning

Combining contrastive learning with control theory for graph analysis.

This study enhances emotion recognition through multi-modal self-supervised methods.

A method to adapt machine learning models without needing source data.

RS3L enhances model training using re-simulation techniques in high-energy physics.

New methods combine ECG data and clinical reports for better heart condition diagnosis.

Exploring the balance of real and generated data for better machine learning performance.

HiVG enhances the linking of images and text for better visual grounding.

This study evaluates methods to enhance large language models using user preference data.

A new method combines local and global data for better event predictions.

A new approach to enhance code clone detection across software projects.

New approaches improve understanding and transferability in neural networks.

This approach improves satellite image analysis by reducing labeling needs.

This paper discusses combining federated and contrastive learning to enhance data privacy and model performance.

A new model enhances image retrieval for breast histopathology diagnosis.

A new approach to enhance contrastive learning through adaptive data augmentation techniques.

Combining large language models and message passing networks improves molecular property predictions.

A new AI model improves Alzheimer's diagnosis accuracy by analyzing brain patterns.

HILL improves hierarchical text classification by preserving data integrity and structure.

A new approach improves classification of cognitive workload using fNIRS data.

LangCell combines cell data and natural language for better cell analysis.

A new method improves how systems handle errors in spoken language understanding.

A model that effectively fills in missing values in time series data.

SegCLR improves eye image segmentation using both labeled and unlabeled data.

This explores how probabilistic approaches improve contrastive learning in machine learning.

A new system that improves robot learning efficiency using video and language feedback.

LInK combines machine learning with traditional methods to improve mechanism design significantly.

A new method improves fault detection in machines through better feature learning.

A new method improves data augmentation for laparoscopic surgery images.

A method using MCMC for effective negative sample generation in contrastive learning.

New methods strengthen watermarks against modern image editing threats.

Introducing new methods to improve forgetting processes in contrastive learning models.

Knowledge distillation enhances segmentation accuracy in medical imaging with limited data.

A new method to align machine learning with human thinking using generative similarity.

PairCFR improves training models using counterfactual data for better performance.

New approach improves learning from interleaved image-text data.

A new method improves image retrieval efficiency using text samples.

Introducing a fast and efficient system for retrieving CAD parts using graph neural networks.

A new method for teaching machines through videos despite visual differences.

A study on Italy's regional languages using advanced speech analysis techniques.

Exploring new methods in graph learning without labeled data.