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Astronomy
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Astronomy is the study of everything in the universe beyond Earth’s atmosphere. That includes objects we can see with our naked eyes, like the Sun, the Moon, the planets, and the stars. It also contains objects we can only see with telescopes or other instruments, like faraway galaxies and tiny particles. And it even includes questions about things we can't see, like dark matter and energy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AliAlkadhim/ALFFI
공식 코드Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods
https://github.com/AnaryaRay1/naz
공식 코드https://github.com/COINtoolbox/RESSPECT
공식 코드Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
https://github.com/DataSlingers/unsupervised-workflow-astro
공식 코드Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
https://github.com/ESA-Datalabs/llm-usage-astronomy
공식 코드Delving into the Utilisation of ChatGPT in Scientific Publications in Astronomy
https://github.com/LLNL/fastcam
공식 코드https://github.com/MilesCranmer/gnn_resource_allocation
공식 코드https://github.com/Mr8ND/cdetools_applications
공식 코드https://github.com/ReginaMayhem/ClusterBusters
공식 코드Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
https://github.com/Rishit-dagli/Astroformer
공식 코드Astroformer: More Data Might not be all you need for Classification
https://github.com/StevenDillmann/ml-xraytransients-mnras
공식 코드https://github.com/Tanveer81/deep_horizon
공식 코드Prediction of soft proton intensities in the near-Earth space using machine learning
https://github.com/UVA-MLSys/AI-for-Astronomy
공식 코드Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI
https://github.com/aangelopoulos/ppi_py
공식 코드https://github.com/aangelopoulos/prediction-powered-inference
공식 코드https://github.com/abdollahmasoud/ijcnn-2023
공식 코드Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods
https://github.com/akolmus/swiftsky
공식 코드Swift sky localization of gravitational waves using deep learning seeded importance sampling
https://github.com/arunsehrawat/interferometric-neural-networks
공식 코드https://github.com/ayushbits/pe-ocr-sanskrit
공식 코드A Benchmark and Dataset for Post-OCR text correction in Sanskrit
https://github.com/bioinf-jku/SNNs
공식 코드https://github.com/biovault/spidr
공식 코드Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images
https://github.com/bjmch/dl-radiotransient
공식 코드Deep learning-based deconvolution for interferometric radio transient reconstruction
https://github.com/bochencaltech/scotopic
공식 코드https://github.com/bperezorozco/ordinal_tsf
공식 코드MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting
https://github.com/bstienen/active-learning
공식 코드Constraining the Parameters of High-Dimensional Models with Active Learning
https://github.com/chenruirae/galaxysd
공식 코드Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
https://github.com/cylumn/daare
공식 코드Removing Radio Frequency Interference from Auroral Kilometric Radiation with Stacked Autoencoders
https://github.com/dalya/Sequencer
공식 코드Extracting the main trend in a dataset: the Sequencer algorithm
https://github.com/datasig-ac-uk/signova
공식 코드https://github.com/devinamhn/radiogalaxies-bnns
공식 코드Evaluating Bayesian deep learning for radio galaxy classification
https://github.com/dr4thmos/solo-learn-radio
공식 코드Self-supervised learning for radio-astronomy source classification: a benchmark
https://github.com/enceladecandy/iris
공식 코드https://github.com/erusseil/mvsr-analysis
공식 코드https://github.com/fishszh/hisf
공식 코드Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning Method
https://github.com/gbredell/w_dip
공식 코드Wiener Guided DIP for Unsupervised Blind Image Deconvolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.