Search Results for author:
Found 4 papers, 1 papers with code
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
F2DNet: Fast Focal Detection Network for Pedestrian Detection
However, the current two-stage detectors are inefficient as they do bounding box regression in multiple steps i. e. in region proposal networks and bounding box heads.
Ranked #2 on
Pedestrian Detection
on Caltech
(using extra training data)
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting
End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications.
KINN: Incorporating Expert Knowledge in Neural Networks
The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare.
