Surveys

Latent Space Class Dispersion: Effective Test Data Quality Assessment for DNNs

High-quality test datasets are crucial for assessing the reliability of Deep Neural Networks (DNNs). Mutation testing evaluates test dataset quality based on their ability to uncover injected faults in DNNs as measured by mutation score (MS). At the same time, its high computational cost motivates researchers to seek alternative test adequacy criteria. We propose Latent Space Class Dispersion (L...

A Survey on Mathematical Reasoning and Optimization with Large Language Models

Mathematical reasoning and optimization are fundamental to artificial intelligence and computational problem-solving. Recent advancements in Large Language Models (LLMs) have significantly improved AI-driven mathematical reasoning, theorem proving, and optimization techniques. This survey explores the evolution of mathematical problem-solving in AI, from early statistical learning approaches to ...

Bayesian generative models can flag performance loss, bias, and out-of-distribution image content

Generative models are popular for medical imaging tasks such as anomaly detection, feature extraction, data visualization, or image generation. Sinc...

A Comparative Analysis of Image Descriptors for Histopathological Classification of Gastric Cancer

Gastric cancer ranks as the fifth most common and fourth most lethal cancer globally, with a dismal 5-year survival rate of approximately 20%. Despi...

Does a Rising Tide Lift All Boats? Bias Mitigation for AI-based CMR Segmentation

Artificial intelligence (AI) is increasingly being used for medical imaging tasks. However, there can be biases in the resulting models, particularl...

A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitatio...

Casual Inference via Style Bias Deconfounding for Domain Generalization

Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in diverse realworld applications. To address t...

Machine Learning-Based Genomic Linguistic Analysis (Gene Sequence Feature Learning): A Case Study on Predicting Heavy Metal Response Genes in Rice

This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa)....

REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models

The rapid evolution of Large Vision-Language Models (LVLMs) has highlighted the necessity for comprehensive evaluation frameworks that assess these ...

A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli

In daily life, we encounter diverse external stimuli, such as images, sounds, and videos. As research in multimodal stimuli and neuroscience advance...

Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey

Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as health...

ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models

Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Tr...

SpecReX: Explainable AI for Raman Spectroscopy

Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential....

Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction

Automatic anatomical landmark localization in medical imaging requires not just accurate predictions but reliable uncertainty quantification for eff...

Survey of Adversarial Robustness in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence by facilitating integrated understandi...

Identifying and Mitigating Position Bias of Multi-image Vision-Language Models

The evolution of Large Vision-Language Models (LVLMs) has progressed from single to multi-image reasoning. Despite this advancement, our findings in...

Ethical Implications of AI in Data Collection: Balancing Innovation with Privacy

This article examines the ethical and legal implications of artificial intelligence (AI) driven data collection, focusing on developments from 2023 ...

A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry

The bias of low-cost Inertial Measurement Units (IMU) is a critical factor affecting the performance of Visual-Inertial Odometry (VIO). In particula...

A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis

Accurate illness diagnosis is vital for effective treatment and patient safety. Machine learning models are widely used for cancer diagnosis based o...

FAILS: A Framework for Automated Collection and Analysis of LLM Service Incidents

Large Language Model (LLM) services such as ChatGPT, DALLE, and Cursor have quickly become essential for society, businesses, and individuals, empow...