Search Results for author:
Found 55 papers, 14 papers with code
ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling
The research fields of parametric face models and 3D face reconstruction have been extensively studied.
Empowering AI drug discovery with explicit and implicit knowledge
Further studies show that benefiting from explicit and implicit knowledge, our framework achieves a deeper understanding of molecules and shows promising potential in facilitating drug discovery applications.
Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
Several approximate inference methods have been proposed for deep discrete latent variable models.
Unsupervised Deep Learning for IoT Time Series
IoT time series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security.
Asynchronous Distributed Bilevel Optimization
Bilevel optimization plays an essential role in many machine learning tasks, ranging from hyperparameter optimization to meta-learning.
AI Empowered Net-RCA for 6G
6G is envisioned to offer higher data rate, improved reliability, ubiquitous AI services, and support massive scale of connected devices.
SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving
Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks.
Distributed Distributionally Robust Optimization with Non-Convex Objectives
Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e. g., network behavior analysis, risk management, etc.
SIND: A Drone Dataset at Signalized Intersection in China
Then, the behaviors of traffic light violations in SIND are recorded.
ADTR: Anomaly Detection Transformer with Feature Reconstruction
Second, CNN tends to reconstruct both normal samples and anomalies well, making them still hard to distinguish.
Trustworthy Federated Learning via Blockchain
In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server.
Task-aware Similarity Learning for Event-triggered Time Series
The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series.
A Holistic Robust Motion Controller Framework for Autonomous Platooning
The output from UCL will be delivered to the lower-level motion-planning layer (LML) as reference signals.
A Unified Model for Multi-class Anomaly Detection
For example, when learning a unified model for 15 categories in MVTec-AD, we surpass the second competitor on the tasks of both anomaly detection (from 88. 1% to 96. 5%) and anomaly localization (from 89. 5% to 96. 8%).
Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis
By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived.
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging
With the aim of obtaining an outstanding performance with less time cost, we propose a novel model in a vertically federated setting termed as Federated Gradient Boosting Forest (FedGBF).
Few-shot Object Counting with Similarity-Aware Feature Enhancement
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i. e., described by one or several support images) occurring in the query image.
Ranked #2 on
Object Counting
on CARPK
Arbitrary Handwriting Image Style Transfer
This paper proposed a method to imitate handwriting style by style transfer.
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost
In this paper, we studied the efficiency problem of adapting widely used XGBoost model in real-world applications to vertical federated learning setting.
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
With this key observation, we protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs.
SiamCorners: Siamese Corner Networks for Visual Tracking
By tracking a target as a pair of corners, we avoid the need to design the anchor boxes.
A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations
To address the issue, we propose a novel two-level routing component to the queueing network model.
Interaction with an obstacle in the 2d focusing nonlinear Schrödinger equation
Moreover, we show that the sharp threshold for global existence vs. finite time blow-up solutions in the mass critical case in the presence of the obstacle is the same as the one given by Weinstein for {\rm{NLS}} in the whole Euclidean space $\R^d$.
Analysis of PDEs Numerical Analysis Numerical Analysis
Waveform Optimization with Multiple Performance Metrics for Broadband Joint Communication and Radar Sensing
For radar sensing, we consider optimizing one of two metrics, the mutual information or the Cramer-Rao bound.
Uplink Sensing in Perceptive Mobile Networks with Asynchronous Transceivers
We first adopt a cross-antenna cross-correlation (CACC) operation to remove the sensing ambiguity associated with both TOs and CFOs.
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths.
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties
A recent proposal generalizes Nesterov's AG method to the nonconvex setting.
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
Thermal Infrared Object Tracking
Vocal Bursts Intensity Prediction
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition
Action recognition with skeleton data is a challenging task in computer vision.
Ranked #57 on
Skeleton Based Action Recognition
on NTU RGB+D
Communication-Efficient Edge AI Inference Over Wireless Networks
Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e. g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future.
Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
Intelligent Internet-of-Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence".
Weak Radio Frequency Signal Detection Based on Piezo-Opto-Electro-Mechanical System: Architecture Design and Sensitivity Prediction
We propose a novel radio-frequency (RF) receiving architecture based on micro-electro-mechanical system (MEMS) and optical coherent detection module.
Communication-Efficient Edge AI: Algorithms and Systems
By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round.
An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition
Dynamic movements of human skeleton have attracted more and more attention as a robust modality for action recognition.
Inexact Primal-Dual Gradient Projection Methods for Nonlinear Optimization on Convex Set
In this paper, we propose a novel primal-dual inexact gradient projection method for nonlinear optimization problems with convex-set constraint.
Hybrid Beamforming for Terahertz Multi-Carrier Systems over Frequency Selective Fading
We consider a three-dimensional wideband THz channel by incorporating the joint effect of molecular absorption, high sparsity, and multi-path fading, and consider the carrier frequency offset in multi-carrier systems.
Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference
Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge.
Group-wise Correlation Stereo Network
Previous works built cost volumes with cross-correlation or concatenation of left and right features across all disparity levels, and then a 2D or 3D convolutional neural network is utilized to regress the disparity maps.
Federated Learning via Over-the-Air Computation
Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center.
Multi-task Prediction of Patient Workload
This issue becomes more critical for the healthcare facilities that provide service for chronic disease treatment because of the need for continuous treatments over the time.
Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses
A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand.
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
Ranked #7 on
Text-To-SQL
on SParC
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
Ranked #10 on
Semantic Parsing
on spider
Active Learning for Wireless IoT Intrusion Detection
This paper discusses the human-in-the-loop active learning approach for wireless intrusion detection.
Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction
The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community.
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer
Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare.
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups.
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
We propose a novel investment decision strategy (IDS) based on deep learning.
Computational Finance
SAFS: A Deep Feature Selection Approach for Precision Medicine
In this paper, we propose a new deep feature selection method based on deep architecture.
Autoencoder Regularized Network For Driving Style Representation Learning
In this paper, we study learning generalized driving style representations from automobile GPS trip data.
Exploring Topic Discriminating Power of Words in Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA) and its variants have been widely used to discover latent topics in textual documents.
Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs
Reducing potentially preventable readmissions has been identified as an important issue for decreasing Medicare costs and improving quality of care provided by hospitals.
Applications 62N01, 62N02, 62P10
A predictive analytics approach to reducing avoidable hospital readmission
We further propose a tree based classification method to estimate the predicted probability of readmission that can directly incorporate patient's history of readmission and risk factors changes over time.
