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Found 6 papers, 2 papers with code
Siamese Learning-based Monarch Butterfly Localization
A new GPS-less, daily localization method is proposed with deep learning sensor fusion that uses daylight intensity and temperature sensor data for Monarch butterfly tracking.
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics
By using a simple family of ODEs informed by the biochemistry of gene expression to constrain the likelihood of a deep generative model, we can simultaneously infer the latent time and latent state of each cell and predict its future gene expression state.
Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring
Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert.
SquiggleFilter: An Accelerator for Portable Virus Detection
The MinION is a recent-to-market handheld nanopore sequencer.
Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks
Details of Monarch butterfly migration from the U. S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration.
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks.
