Latest Articles for Data Science

A study on the reliability of removal-based methods in machine learning.

DCP-NAS improves neural architecture search for resource-efficient 1-bit convolutional networks.

A new model reveals key factors in evasion attack success.

A new dataset to enhance personalized search in community question answering.

A new approach improves weight distribution estimation in Bayesian neural networks.

Examining the role of LLMs in predicting stock prices and enhancing market insights.

Exploring the Confidence Ranking framework for better ad predictions.

Learn how regularization shapes predictions in machine learning through Empirical Risk Minimization.

This article assesses the effectiveness of token-level text augmentation techniques in natural language processing.

Exploring how the Hessian matrix impacts neural network decision boundaries and generalization.

Examines the impact of foreground-background imbalance on object detection performance.

A new method enhances feature selection in complex systems.

A new method improves how AI understands images and text.

A study on TensorFlow and PyTorch package dependencies in the PyPI ecosystem.

Learn how tensor networks can simplify complex data in machine learning.

Assessing large language models' performance in answering biomedical questions through BioASQ.

A study introduces noise in training to compress neural networks while maintaining performance.

A new method enhances how models learn from data transformations.

Omega improves strategies in stochastic games by using past data effectively.

This article discusses methods to enhance fairness in machine learning by correcting label noise.

A new method tackles cold-start challenges in recommendation systems using language models.

This study clarifies identifying direct effects using summary causal graphs.

Discover a new method for combining different types of data effectively.

A look into the advancements and challenges in self-driving cars.

A new synthetic data generator aids human behavior analysis through realistic datasets.

A new system allows custom categories for text classification with no retraining needed.

A new approach enhances activity recognition accuracy using context and semantic loss.

Research enhances SVAEs for better data handling and interpretability.

This framework streamlines data processing for better recommendation systems.

A deep look at supervised contrastive loss and its impact on deep neural networks.

This study finds robust features help machines retain knowledge during continual learning.

Researchers use evolutionary algorithms for mathematical equation discovery from complex data.

A new method improves classification in graph neural networks facing complex connections.

A novel approach enhances segmentation of medical images using unannotated data.

Learn about a new algorithm improving decision-making safety and efficiency.

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

A new method aims to improve AutoML performance under strict limits.

Innovative methods improve ads targeting efficiency using sparse data and advanced algorithms.

Examining the critical impact of data on AV decision-making processes.

A new method for identifying maximum cliques in complex networks.