Search Results for author:
Found 11 papers, 5 papers with code
Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods
Recommender systems based on graph neural networks perform well in tasks such as rating and ranking.
QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction
Furthermore, we integrate a Monte Carlo Dropout based uncertainty estimation module, allowing for trustworthy and risk-aware service quality prediction, which is crucial yet underexplored in existing QoS models.
CoSIL: Software Issue Localization via LLM-Driven Code Repository Graph Searching
CoSIL reduces the search space through module call graphs, iteratively searches the function call graph to obtain relevant contexts, and uses context pruning to control the search direction and manage contexts effectively.
TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content
In addition, we design a trustworthy decision mechanism that integrates subjective user perspective and objective item perspective to dynamically evaluate the uncertainty of prediction results.
Dual Latent State Learning: Exploiting Regional Network Similarities for QoS Prediction
Individual objects, whether users or services, within a specific region often exhibit similar network states due to their shared origin from the same city or autonomous system (AS).
Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision
Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise.
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution.
A Probability Distribution and Location-aware ResNet Approach for QoS Prediction
This approach considers the historical invocations probability distribution and location characteristics of users and services, and first use the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation.
A Novel Automation-Assisted Cervical Cancer Reading Method Based on Convolutional Neural Network
Our model achieves 97. 5% sensitivity (Sens) and 67. 8% specificity (Spec) on cervical cell image-level screening.
Efficient Misalignment-Robust Multi-Focus Microscopical Images Fusion
Our method provides a flexible and efficient way to integrate complementary and redundant information from multiple multi-focus ultra HD unregistered images into a fused image that contains better description than any of the individual input images.
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection.
