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Found 13 papers, 4 papers with code
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.
Rethinking the Sample Relations for Few-Shot Classification
MGRCL categorizes sample relations into three types: intra-sample relation of the same sample under different transformations, intra-class relation of homogenous samples, and inter-class relation of inhomogeneous samples.
DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference
However, the accuracy of the segmentation results is often limited by insufficient supervision and the complex nature of medical imaging.
Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object Detection
Explicitly our method focuses on enhancing labeling using synthetic scenes crafted from 3D shapes generated via random prompts.
LiDAR-Net: A Real-scanned 3D Point Cloud Dataset for Indoor Scenes
In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3. 6 billion precisely point-level annotated points covering an expansive area of 30000m^2.
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.
Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations.
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
In addition, supervised graph representation learning requires labeled data, which is expensive and error-prone.
Regression-based Hypergraph Learning for Image Clustering and Classification
Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues.
Sparse Graph-based Transduction for Image Classification
Then the Laplacian eigenmapping is adopted for deriving the graph Laplacian of the graph.
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition.
Face Recognition via Globality-Locality Preserving Projections
We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data.
