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Found 16 papers, 3 papers with code
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Deep semi-supervised learning (SSL) has been shown very effectively.
MDHP-Net: Detecting Injection Attacks on In-vehicle Network using Multi-Dimensional Hawkes Process and Temporal Model
We then propose an injection attack detector, MDHP-Net, which integrates optimal MDHP parameters with MDHP-LSTM blocks to enhance temporal feature extraction.
KGV: Integrating Large Language Models with Knowledge Graphs for Cyber Threat Intelligence Credibility Assessment
To the best of our knowledge, this work is the first to create a dataset on threat intelligence reliability verification, providing a reference for future research.
Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning
In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results.
FactFinders at CheckThat! 2024: Refining Check-worthy Statement Detection with LLMs through Data Pruning
The rapid dissemination of information through social media and the Internet has posed a significant challenge for fact-checking, among others in identifying check-worthy claims that fact-checkers should pay attention to, i. e. filtering claims needing fact-checking from a large pool of sentences.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Real-Time Pill Identification for the Visually Impaired Using Deep Learning
Keywords-Deep Learning; YOLO Framework; Mobile Application; Visual Impairment; Pill Identification; Healthcare
Breast Cancer Image Classification Method Based on Deep Transfer Learning
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed.
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example
To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection.
Survival Prediction Across Diverse Cancer Types Using Neural Networks
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
REFRAME: Reflective Surface Real-Time Rendering for Mobile Devices
This work tackles the challenging task of achieving real-time novel view synthesis for reflective surfaces across various scenes.
SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic
Scalable service-Oriented Middleware over IP (SOME/IP) is an Ethernet communication standard protocol in the Automotive Open System Architecture (AUTOSAR), promoting ECU-to-ECU communication over the IP stack.
Learning Image Deraining Transformer Network with Dynamic Dual Self-Attention
Recently, Transformer-based architecture has been introduced into single image deraining task due to its advantage in modeling non-local information.
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
Then these negative adversaries are trained end-to-end together with the backbone representation network to enhance the discriminative information and promote factor disentanglement performance by maximizing the adversarial contrastive loss.
Unpaired Deep Image Deraining Using Dual Contrastive Learning
Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible.
Estimating the black hole spin for the X-ray binary MAXI J1820+070
MAXI J1820+070 is a newly-discovered black hole X-ray binary, whose dynamical parameters, namely the black hole mass, the inclination angle and the source distance, have been estimated recently.
High Energy Astrophysical Phenomena
