https://github.com/vmos1/cnn_supernova_b_paper
공식 코드Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks
Papers with Code 아카이브
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/waller-lab/encodinginformation
공식 코드https://github.com/yalavarthivk/GraFITi
공식 코드https://github.com/yalavarthivk/dcsf
공식 코드DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series
https://github.com/yalavarthivk/profiti
공식 코드Probabilistic Forecasting of Irregular Time Series via Conditional Flows
https://github.com/yalavarthivk/tripletformer
공식 코드Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series
https://github.com/yudongyao/autophasenn
공식 코드https://github.com/zhangyk8/DirMS
공식 코드Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
https://gitlab.com/makhlaghi/iau-symposium-355
공식 코드Carving out the low surface brightness universe with NoiseChisel
https://zenodo.org/record/4247552
공식 코드https://zenodo.org/record/4288837
공식 코드MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning
https://github.com/1Reinier/Reservoir
Efficient Optimization of Echo State Networks for Time Series Datasets
https://github.com/2023-MindSpore-4/Code11/tree/main/snn_mlp
https://github.com/2023-MindSpore-4/Code7/tree/main/snn_mlp
https://github.com/BenjiKCF/SNN-with-embeddings-for-Malware-Prediction
https://github.com/BioImageInformatics/tfmodels
https://github.com/IftachSadeh/ANNZ
https://github.com/IshaFaodail/DeepCNN_denoising
https://github.com/KamilDeja/e2e_sinkhorn_autoencoder
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Self_Normalizing_Neural_Networks
https://github.com/Mind23-2/MindCode-88/tree/main/snn_mlp
https://github.com/PurdueDualityLab/TensorFlowModels
https://github.com/TalRemez/deep_class_aware_denoising
https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/anand0xff/FourierOpticsJHU2021
https://github.com/blackPacha/VAE_ABIDE1
https://github.com/blindedjoy/RcTorch
Efficient Optimization of Echo State Networks for Time Series Datasets
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/chongduan/MRI-AUTOMAP
https://github.com/cosmostat/wf-psf
Rethinking data-driven point spread function modeling with a differentiable optical model
https://github.com/dmbernaal/Daedalus
https://github.com/domschl/syncognite
https://github.com/esa/AnomalyMatch
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
https://github.com/georgestein/galaxy_search
Self-Supervised Representation Learning for Astronomical Images
https://github.com/hemingkx/improved-gwcnn
Convolutional neural networks: a magic bullet for gravitational-wave detection?
https://github.com/hombit/light-curve
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.