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Found 24 papers, 5 papers with code
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications.
Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty
The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability.
Construction of Functional Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
The convergence of materials science and artificial intelligence has unlocked new opportunities for gathering, analyzing, and generating novel materials sourced from extensive scientific literature.
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification
Although some approaches have attempted to address this problem through single-stage clustering as an intermediate training step coupled with a pre-trained language model, which generates pseudo-labels to improve classification, these methods are often error-prone due to the limitations of the clustering algorithms.
DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning
Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks.
Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation
Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for the tracking and diagnosis of progression.
DARWIN Series: Domain Specific Large Language Models for Natural Science
To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.
Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines
The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions
Skin cancer holds the highest incidence rate among all cancers globally.
LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms
The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms.
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness.
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance.
Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey
Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications.
Distributed Optimization of Graph Convolutional Network using Subgraph Variance
We first propose a graph augmentation-based partition (GAD-Partition) that can divide original graph into augmented subgraphs to reduce communication by selecting and storing as few significant nodes of other processors as possible while guaranteeing the accuracy of the training.
Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction
These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread.
DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media
Twitter is currently a popular online social media platform which allows users to share their user-generated content.
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS).
BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition
In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially.
Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions
Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous success stories.
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction.
Leveraging Big Data Analytics in Healthcare Enhancement: Trends, Challenges and Opportunities
It promises us the power of early detection, prediction, prevention and helps us to improve the quality of life.
