Anomaly detection has been an active research area with a wide range of potential applications.
Recently, deep learning techniques have been successfully applied for detection of diabetic retinopathy (DR).
Subspace identification (SID) has been widely used in system identification and control fields since it can estimate system models only relying on the input and output data by reliable numerical operations such as singular value decomposition (SVD).
It stands in clear contrast to the result of cross-correlation method, whose localization error is 70 m and the standard deviation is 208. 4 m. Compared with cross-correlation method, TSDEV has the same resistance to white noise, but has fewer boundary conditions and better suppression on linear drift or common noise, which leads to more precise TDE results.
Heated debates continue over the best autonomous driving framework.
We demonstrate the utility of this framework in comparing, matching, and computing geodesics between biological objects such as neurons and botanical trees.
Matrix multiplication is the bedrock in Deep Learning inference application.
For such tasks, the main requirement for intermediate representations of the data is to maintain the structure needed for output, i. e., keeping classes separated or maintaining the order indicated by the regressor.
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
In E-commerce, advertising is essential for merchants to reach their target users.
These stages usually allocate resource manually with specific computing power budgets, which requires the serving configuration to adapt accordingly.
Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight.
However, in the context of CFD, each search step requires long-lasting CFD model's iterated solving, rendering these approaches impractical with increased model complexity.
Our methods open the door for research in model contribution and credit allocation in the context of federated machine learning.
Pose estimation is a fundamental building block for robotic applications such as autonomous vehicles, UAV, and large scale augmented reality.
In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning.
For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party.