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aleksciprijanovic

My Website

astrobenchmarking

AstroHackWeek2020 Hack proposal #Astro-MNIST

basic-algorithms-for-classification-from-scratch

Basic alrothms for classification, clustering and Neural Networks (me practicing stuff with sentdex).

bldap-python-data-science

Intro to Python / data science curriculum for Berkeley Lab Director's Apprenticeship Program

cat-vs-dog-tflearn-

CNN for differentiating between cats and dogs in TensorFlow.

concrete-dropout---mergers

cosmo1gal

Data and scripts used for the cosmology with one galaxy paper

dann---deepmerge

Domain Adversarial Neural Network approach to galaxy merger classification using pristine and noisy images together

deepadversaries

Studying robustness of deep learning algorithms to adversarial attacks that may naturally arise from scientific data pipelines, such as LSST.

deepmerge-can

Using Contrastive Domain Discrepancy for class-aware domain adaptation to study galaxy mergers.

deepmerge-mocks

deepmerge-public

DeepMerge: An implementation of neural network models for classifying merging galaxies

deepmergedomainadaptation

Fisher deep domain adaptation for astronomy - galaxy mergers

deepshadows

Repository for the project "DeepShadows: Separating LSBGs from artifacts using Deep Learning"

deepshadows_ii

demo

demo

descqa

A validation framework and tests for mock galaxy catalogs and beyond

fisherda

code for "Fisher Deep Domain Adaptation"

hacks-collector

Astro Hack Week Hacks Collector: a repository for past and present dotAstronomy hacks

hackweek-toolkit

A toolkit to get you started with organizing your own hack week!

imnn

Uses neural networks to extract sufficient statistics from data by maximising the Fisher information

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

ptexperiments

PyTorch experiments

random

Random usefull programs

scalable-pytorch-sinkhorn

tutorial-notebooks

Tutorial Jupyter Notebooks for Data Preview 0, created and maintained by the Rubin Observatory Community Engagement Team.

wassdistance_sinkhorn

Approximating Wasserstein distances with PyTorch