Search Results for author:
Found 17 papers, 8 papers with code
MERIT: Multimodal Wearable Vital Sign Waveform Monitoring
Cardiovascular disease (CVD) is the leading cause of death and premature mortality worldwide, with occupational environments significantly influencing CVD risk, underscoring the need for effective cardiac monitoring and early warning systems.
Factors in Fashion: Factor Analysis towards the Mode
The modal factor model represents a new factor model for dimension reduction in high dimensional panel data.
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization
Through the integration of vector quantization (VQ), we empower the flow models to distinguish different concepts of multi-class normal data in an unsupervised manner, resulting in a novel flow-based unified method, named VQ-Flow.
ComKD-CLIP: Comprehensive Knowledge Distillation for Contrastive Language-Image Pre-traning Model
In this paper, we propose a novel approach, ComKD-CLIP: Comprehensive Knowledge Distillation for Contrastive Language-Image Pre-traning Model, which aims to comprehensively distill the knowledge from a large teacher CLIP model into a smaller student model, ensuring comparable performance with significantly reduced parameters.
SentenceVAE: Enable Next-sentence Prediction for Large Language Models with Faster Speed, Higher Accuracy and Longer Context
Current large language models (LLMs) primarily utilize next-token prediction method for inference, which significantly impedes their processing speed.
Static and multivariate-temporal attentive fusion transformer for readmission risk prediction
Methods:We propose a novel static and multivariate-temporal attentive fusion transformer (SMTAFormer) to predict short-term readmission of ICU patients by fully leveraging the potential of demographic and dynamic temporal data.
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories.
StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging
In this paper, we introduce StreakNet-Arch, a novel signal processing architecture designed for Underwater Carrier LiDAR-Radar (UCLR) imaging systems, to address the limitations in scatter suppression and real-time imaging.
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Specifically, we introduce a concise multi-scale module to merge attentive features from quadruplet attention layers, and produces attribution maps.
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies
Unlike existing SSAD methods that resort to strict loss supervision, AnoOnly suspends it and introduces a form of weak supervision for normal data.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
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L3C-Stereo: Lossless Compression for Stereo Images
To tackle this, we propose L3C-Stereo, a multi-scale lossless compression model consisting of two main modules: the warping module and the probability estimation module.
Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization
Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering.
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.
Automated Measurements of Key Morphological Features of Human Embryos for IVF
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy.
A novel multimodal approach for hybrid brain-computer interface
It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals.
Human-Computer Interaction Signal Processing
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others.
DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
