Search Results for author:
Found 11 papers, 3 papers with code
Latent Relationship Mining of Glaucoma Biomarkers: a TRI-LSTM based Deep Learning
In this research, in contrast, we learn from cognitive science concept and study how ophthalmologists judge glaucoma detection.
Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic Citation Networks
Due to the rapid growth of scientific publications, identifying all related reference articles in the literature has become increasingly challenging yet highly demanding.
DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking
Moreover, DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets, significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA, IDF1, AssA.
Location-guided Head Pose Estimation for Fisheye Image
Camera with a fisheye or ultra-wide lens covers a wide field of view that cannot be modeled by the perspective projection.
DeconfuseTrack: Dealing with Confusion for Multi-Object Tracking
Moreover DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA IDF1 AssA.
Code Clone Detection based on Event Embedding and Event Dependency
To address this issue, we propose a code clone detection method based on semantic similarity.
Non-intrusive Balancing Transformation of Highly Stiff Systems with Lightly-damped Impulse Response
Balanced truncation (BT) is a model reduction method that utilizes a coordinate transformation to retain eigen-directions that are highly observable and reachable.
Data-driven reduced-order models via regularized operator inference for a single-injector combustion process
With appropriate regularization and an informed selection of learning variables, the reduced-order models exhibit high accuracy in re-predicting the training regime and acceptable accuracy in predicting future dynamics, while achieving close to a million times speedup in computational cost.
Computational Engineering, Finance, and Science J.2
Learning physics-based reduced-order models for a single-injector combustion process
The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis.
LoadCNN: A Low Training Cost Deep Learning Model for Day-Ahead Individual Residential Load Forecasting
As a consequence, the deep learning methods are difficult to be popularized and applied in the real smart grid environment.
A Distributed One-Step Estimator
A potential application of the one-step approach is that one can use multiple machines to speed up large scale statistical inference with little compromise in the quality of estimators.
