This research will promote the development of machine learning strategies that can be applied toward general cognitive traits, measured in different ways, across different populations.
Tag: ECE
OR-DRD-AI2020: Parasitic Nematode Identification with Deep Learning
Alina Zare, a professor of electrical and computer engineering, and Peter DiGennaro, assistant professor of entomology and nematology, who will work to create a way to identify agricultural pests in soil using the university’s set of nematode images.
Adaptive Manifold Learning for Multi-Sensor Translation and Fusion given Missing Data
In this effort, PIs Alina Zare and Paul Gader are developing machine learning approaches to translate sensor data across sensor platforms for a unified analysis framework.
Cyber-Physical Systems Security through Robust Adaptive Possibilitistic Algorithms: a Cross Layered Framework
The goal of this project is to develop a cross-layer cyber-physical security framework for the smart grid. The proposed research will improve the quality of real-time monitoring of the smart grid through anomaly analysis.
Climate Adaptation and Sustainability in Switchgrass: Exploring Plant-Microbe-Soil Interactions across Continental Scale Environmental Gradients
Address sustainable switchgrass production by exploring plant systems, the plant microbiome, and ecosystem processes through the integrating lens of multiscale modeling.
Biodiversity Data from Insect Songs: New hardware and software for monitoring insect bioacoustics, and new opportunities for public outreach
Artificial neural networks meet biological neural networks: designing personalized stimulation for the data-driven control of neural dynamics
To address these issues, we will leverage the wealth of data that multi-subject experiments provide, as well as the computational resources newly available at UF.
We will develop new AI methods that utilize in-vivo neural responses to design and implement personalized stimulation in real-time.
Quantum Information Potential Fields: A Novel Uncertainty Quantification Framework for Machine Learning
This proposal uses operators from quantum theory to define uncertainty in machine learning decisions as well as in data streams. The core innovation is to use a modal decomposition of the probability density function that quantifies better the distribution tails.
A Biologically Inspired Event Memory Architecture for Machine Learning
This proposal enhances the conventional machine learning architectures with novel memories inspired by the cortical columns that form the mammalian cortex. The goal is to model cortical columns as automata that implement inference as a search process. This module will be integrated with our current cognitive architecture for autonomous vision.
Improving Reinforcement Learning Efficacy with Causality, Trajectories, and the Value of Information
Reinforcement learning (RL) is very slow and does not scale up well to high dimensional spaces. This proposal employs causality to go beyond correlation learning to reduce the amount of data necessary to train RL. Pointwise interactions with the environment will be substituted by trajectories to speed up training, while the value of information optimizes the exploration exploitation dilemma.