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Found 13 papers, 2 papers with code
Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph
Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels.
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
Importantly, all the bounds in the theorems presented in this paper are independent of the dimension $n$.
Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network
The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning
We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.
Improving Biomedical Named Entity Recognition with Syntactic Information
The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.
Ranked #1 on
Named Entity Recognition (NER)
on Species-800
A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models
For point-wise anomaly detection, our method achieves 90. 7\% AUROC on average and outperforms the baseline by 5. 2\% AUROC.
HPC AI500: A Benchmark Suite for HPC AI Systems
In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.
Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing.
A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks
In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments.
A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages.
A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments
In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment.
A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction
In this paper, we propose a novel multi-attribute tensor correlation neural network (MTCN) for face attribute prediction.
