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Found 10 papers, 2 papers with code
Knowledge Transfer across Multiple Principal Component Analysis Studies
In the first step, we integrate the shared subspace information across multiple studies by a proposed method named as Grassmannian barycenter, instead of directly performing PCA on the pooled dataset.
Quantification of Carbon Sequestration in Urban Forests
Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere.
PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data
An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform.
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models
Physics-informed neural networks (NN) are an emerging technique to improve spatial resolution and enforce physical consistency of data from physics models or satellite observations.
Monitoring the Impact of Wildfires on Tree Species with Deep Learning
One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species.
Lifelong Object Detection
Recent advances in object detection have benefited significantly from rapid developments in deep neural networks.
Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis
We construct a Median-of-Means (MOM) estimator for the centered log-ratio covariance matrix and propose a thresholding procedure that is adaptive to the variability of individual entries.
Methodology
Statistical inference in massive datasets by empirical likelihood
In this paper, we propose a new statistical inference method for massive data sets, which is very simple and efficient by combining divide-and-conquer method and empirical likelihood.
Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning
In this paper, we present a graph-aware encoder-decoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data.
Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning
Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging.
