Search Results for author: João Caldeira

Found 6 papers, 3 papers with code

DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning

no code implementations25 Feb 2021 Zhen Lin, Nicholas Huang, Camille Avestruz, W. L. Kimmy Wu, Shubhendu Trivedi, João Caldeira, Brian Nord

We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN).

Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

1 code implementation22 Apr 2020 João Caldeira, Brian Nord

We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system.

Uncertainty Quantification

Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer

no code implementations14 Nov 2019 João Caldeira, Joshua Job, Steven H. Adachi, Brian Nord, Gabriel N. Perdue

We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems.

General Classification Image Classification +2

Improving Traffic Safety Through Video Analysis in Jakarta, Indonesia

1 code implementation30 Nov 2018 João Caldeira, Alex Fout, Aniket Kesari, Raesetje Sefala, Joseph Walsh, Katy Dupre, Muhammad Rizal Khaefi, Setiaji, George Hodge, Zakiya Aryana Pramestri, Muhammad Adib Imtiyazi

This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta.

DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks

1 code implementation2 Oct 2018 João Caldeira, W. L. Kimmy Wu, Brian Nord, Camille Avestruz, Shubhendu Trivedi, Kyle T. Story

In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet.

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