Search Results for author: Thomas Vandal

Found 8 papers, 3 papers with code

Spectral Synthesis for Satellite-to-Satellite Translation

1 code implementation12 Oct 2020 Thomas Vandal, Daniel McDuff, Weile Wang, Andrew Michaelis, Ramakrishna Nemani

These satellites have different vantage points above the earth and different spectral imaging bands resulting in inconsistent imagery from one to another.

Cloud Detection Spectral Reconstruction +2

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

no code implementations13 Jun 2020 Nicholas Gao, Max Wilson, Thomas Vandal, Walter Vinci, Ramakrishna Nemani, Eleanor Rieffel

Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes.

Quantum Machine Learning Vocal Bursts Intensity Prediction

A framework for deep learning emulation of numerical models with a case study in satellite remote sensing

1 code implementation29 Oct 2019 Kate Duffy, Thomas Vandal, Weile Wang, Ramakrishna Nemani, Auroop R. Ganguly

A difficult test for deep learning-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner.

Cloud Detection Computational Efficiency

Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow

no code implementations28 Jul 2019 Thomas Vandal, Ramakrishna Nemani

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the trade-offs to spatial, spectral and temporal resolutions of observations.

Optical Flow Estimation

Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning

no code implementations23 Apr 2019 Max Wilson, Thomas Vandal, Tad Hogg, Eleanor Rieffel

We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model.

Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning

1 code implementation13 Feb 2018 Thomas Vandal, Evan Kodra, Jennifer Dy, Sangram Ganguly, Ramakrishna Nemani, Auroop R. Ganguly

Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes.

Management Super-Resolution +1

Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation

no code implementations13 Feb 2017 Thomas Vandal, Evan Kodra, Auroop R. Ganguly

Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future.

BIG-bench Machine Learning

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