Latest Articles for Multi-task Learning

A new approach improves disease prediction using electronic health records.

A new model improves voice identification security and resists voice spoofing.

A flexible tool designed for varied data labeling situations.

New framework improves predictions in multi-task learning despite data limitations.

Innovative methods aim to improve brain imaging for mental health research.

Introducing HeMeNet for efficient protein multi-task learning using 3D structures.

New technology enhances the accuracy of lung disease diagnosis through sound analysis.

This study connects word analysis in reports to ESG performance.

A new method aims to improve fairness in machine learning without sacrificing performance.

A new method enhances speech model performance and efficiency in noisy environments.

New methods improve predictions in drug discovery and materials science.

UniAV combines action localization, sound detection, and audio-visual event localization for better video understanding.

Exploring the benefits and challenges of shared variable embeddings in machine learning.

drGAT uses machine learning to predict how cells respond to drugs.

Explore the role of memory and context in improving machine learning.

SpeechVerse bridges audio understanding and language processing for improved human-computer interaction.

New model enhances prediction accuracy for hydrophobic patches in proteins.

VulLLM improves automatic detection of software vulnerabilities through advanced learning techniques.

This study enhances AES by focusing on grammar in essay evaluation.

Introducing model evolution for improved language model performance without extra training data.

MODA enhances decision-making processes in urban areas using advanced learning techniques.

A novel model enhances language models' function calling abilities for complex tasks.

VCHAR improves recognition of complex human activities using innovative visual methods.

HydraGNN uses machine learning to accelerate materials discovery and modeling.

HyperLoader improves multi-task model training using innovative techniques and hypernetworks.

A new method improves road extraction accuracy using graph neural networks.

Context-aware neural translation models enhance accuracy through multi-task learning.

This study explores how representation learning helps robots adapt in changing environments.

A new method uses AI to enhance cancer grading accuracy.

A method to improve deep learning deployment on limited-resource devices.

A new method improves efficiency in multi-task machine learning.

A new method improves multi-task learning by aggregating targets and features.

A new method improves how cars understand their surroundings.

CrowdMAC improves predictions in crowd density forecasting despite incomplete data.

A new method helps robots learn and adapt to tasks effectively.

MT-Diffusion efficiently generates multiple data types simultaneously, improving AI applications.

Analyzing how training datasets affect AI fairness in skin lesion diagnosis.

A study comparing machine learning models for evaluating vulnerabilities in C/C++ code.

A new approach to enhance efficiency in multi-task learning using low-rank matrices.

Multi-Task Grouping improves learning efficiency and performance across various AI applications.