Search Results for author:
Found 5 papers, 1 papers with code
Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation
In this paper a new optical-computational method is introduced to unveil images of targets whose visibility is severely obscured by light scattering in dense, turbid media.
The Intel Neuromorphic DNS Challenge
A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.
Encoding Event-Based Gesture Data With a Hybrid SNN Guided Variational Auto-encoder
Our novel approach consists of an event-based guided Variational Autoencoder (VAE) which encodes event-based data sensed by a Dynamic Vision Sensor (DVS) into a latent space representation suitable to compute the similarity of mid-air gesture data.
Encoding Event-Based Data With a Hybrid SNN Guided Variational Auto-encoder in Neuromorphic Hardware
We also implement the encoder component of the model on neuromorphic hardware and discuss the potential for our algorithm to enable real-time learning from real-world event data.
End-to-End Auditory Object Recognition via Inception Nucleus
Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum.
