Search Results for author:
Found 14 papers, 4 papers with code
Development of a deep learning-based tool to assist wound classification
This paper presents a deep learning-based wound classification tool that can assist medical personnel in non-wound care specialization to classify five key wound conditions, namely deep wound, infected wound, arterial wound, venous wound, and pressure wound, given color images captured using readily available cameras.
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Reinforcement learning constantly deals with hard integrals, for example when computing expectations in policy evaluation and policy iteration.
Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data
To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread.
HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks
Many types of physics-informed neural network models have been proposed in recent years as approaches for learning solutions to differential equations.
End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
Towards Question-based Recommender Systems
Previous conversational recommender systems ask users to express their preferences over items or item facets.
Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements through Case Studies
Traffic evacuation plays a critical role in saving lives in devastating disasters such as hurricanes, wildfires, floods, earthquakes, etc.
One-to-one Mapping for Unpaired Image-to-image Translation
Here we propose a self-inverse network learning approach for unpaired image-to-image translation.
Generative Adversarial Network
Image-to-Image Translation
+1
Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.
Scalable Realistic Recommendation Datasets through Fractal Expansions
A larger version features 655 billion ratings, 7 million items and 17 million users.
A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net
Random initialization is usally used to initialize the model weights in the U-net.
Prostate Segmentation using 2D Bridged U-net
To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
