Search Results for author:
Found 4 papers, 0 papers with code
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm
Bio-inspired Address Event Representation (AER) sensors have attracted significant popularity owing to their low power consumption, high sparsity, and high temporal resolution.
Theoretical Insight into Batch Normalization: Data Dependant Auto-Tuning of Regularization Rate
Batch normalization is widely used in deep learning to normalize intermediate activations.
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data
Compared to competing supervised approaches, ours is a task-agnostic approach ideally suited for the event domain, where task specific labeled data is scarce.
EvAn: Neuromorphic Event-based Anomaly Detection
Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events, thus resulting in significant advantages over conventional cameras in terms of low power utilization, high dynamic range, and no motion blur.
