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Found 12 papers, 5 papers with code
Autonomous Robot for Disaster Mapping and Victim Localization
In response to the critical need for effective reconnaissance in disaster scenarios, this research article presents the design and implementation of a complete autonomous robot system using the Turtlebot3 with Robotic Operating System (ROS) Noetic.
Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach
To address this, we propose a fully Bayesian RATR framework employing Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
Robust Survival Analysis with Adversarial Regularization
Survival Analysis (SA) is about modeling the time for an event of interest to occur, which has important applications in many fields, including medicine, defense, finance, and aerospace.
Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?
We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates.
Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network
We propose to integrate weapon system features (such as weapon system manufacturer, deployment time and location, storage time and location, etc.)
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples
In this paper, we propose a novel adaptation of Focal Loss for keypoint detection tasks, called Adversarial Focal Loss (AFL).
To Raise or Not To Raise: The Autonomous Learning Rate Question
There is a parameter ubiquitous throughout the deep learning world: learning rate.
Optimizing Data Augmentation Policy Through Random Unidimensional Search
It is no secret amongst deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result.
Low-light Environment Neural Surveillance
The system uses a low-light video feed processed in real-time by an optical-flow network, spatial and temporal networks, and a Support Vector Machine to identify shootings, assaults, and thefts.
Impact of Inference Accelerators on hardware selection
As opportunities for AI-assisted healthcare grow steadily, model deployment faces challenges due to the specific characteristics of the industry.
FastEstimator: A Deep Learning Library for Fast Prototyping and Productization
As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI practitioners around the world.
AI Assisted Annotator using Reinforcement Learning
Unlike other data sets, medical data annotation, which is critical to accurate ground truth, requires medical domain expertise for a better patient outcome.
