Search Results for author:
Found 2 papers, 1 papers with code
An FPGA-Accelerated Design for Deep Learning Pedestrian Detection in Self-Driving Vehicles
With the rise of self-driving vehicles comes the risk of accidents and the need for higher safety, and protection for pedestrian detection in the following scenarios: imminent crashes, thus the car should crash into an object and avoid the pedestrian, and in the case of road intersections, where it is important for the car to stop when pedestrians are crossing.
Towards Large Scale Training Of Autoencoders For Collaborative Filtering
In this paper, we apply a mini-batch based negative sampling method to efficiently train a latent factor autoencoder model on large scale and sparse data for implicit feedback collaborative filtering.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
