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archive:dlcp:kryukov22:biblio:main [05/07/2024 19:03] – ↷ Page moved from grants:archive:dlcp:kryukov22:biblio:main to archive:dlcp:kryukov22:biblio:main adminarchive:dlcp:kryukov22:biblio:main [05/07/2024 19:19] (current) – ↷ Links adapted because of a move operation admin
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   * [[https://lilianweng.github.io/|Lil’Log]]. Мне показалось - интересные и полезные материалы по МО.   * [[https://lilianweng.github.io/|Lil’Log]]. Мне показалось - интересные и полезные материалы по МО.
-  * {{ :dlcp:kryukov22:biblio:holler_diss-hillas.pdf |Holler, dissertation. H.E.S.S}}+  * {{ archive:dlcp:kryukov22:biblio:holler_diss-hillas.pdf |Holler, dissertation. H.E.S.S}}
 ===== Обзоры статей ===== ===== Обзоры статей =====
  
-  * Yang, Z. {{ dlcp:kryukov22:biblio:zeng_pres.pdf |Generative Adversarial Networks with Constraints}} (А.Демичев) +  * Yang, Z. {{ archive:dlcp:kryukov22:biblio:zeng_pres.pdf |Generative Adversarial Networks with Constraints}} (А.Демичев) 
-  * Weiler, M. and Cesa, G. {{ dlcp:kryukov22:biblio:e2_equivariantsteerablecnns.pdf |General E(2) - Equivariant Steerable CNNs}} (А.Демичев)+  * Weiler, M. and Cesa, G. {{ archive:dlcp:kryukov22:biblio:e2_equivariantsteerablecnns.pdf |General E(2) - Equivariant Steerable CNNs}} (А.Демичев)
 ===== Astro&HEP ===== ===== Astro&HEP =====
  
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   * Atul Kumar  and et.al. SinhaSUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. **ArXiv: 2202.05012**   * Atul Kumar  and et.al. SinhaSUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. **ArXiv: 2202.05012**
   * The ATLAS Collaboration. Deep generative models for fast shower simulation in ATLAS. [[http://cdsweb.cern.ch/record/2630433/files/ATL-SOFT-PUB-2018-001.pdf|ATLAS-SOFT-PUB-2018-001]]   * The ATLAS Collaboration. Deep generative models for fast shower simulation in ATLAS. [[http://cdsweb.cern.ch/record/2630433/files/ATL-SOFT-PUB-2018-001.pdf|ATLAS-SOFT-PUB-2018-001]]
-  * E. A. Huerta and et.al. Enabling real-​time multi-​messenger astrophysics discoveries with deep learning. [[https://doi.org/10.1038/s42254-019-0097-4]], {{ dlcp:kryukov22:biblio:s42254-019-0097-4-mmastro.pdf |PDF}}+  * E. A. Huerta and et.al. Enabling real-​time multi-​messenger astrophysics discoveries with deep learning. [[https://doi.org/10.1038/s42254-019-0097-4]], {{ archive:dlcp:kryukov22:biblio:s42254-019-0097-4-mmastro.pdf |PDF}}
   * Songshaptak De and etc. Deep learning techniques for Imaging Air Cherenkov Telescopes, [[https://arxiv.org/pdf/2206.05296|]]    * Songshaptak De and etc. Deep learning techniques for Imaging Air Cherenkov Telescopes, [[https://arxiv.org/pdf/2206.05296|]] 
  
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   * [[https://learnopencv.com/conditional-gan-cgan-in-pytorch-and-tensorflow/|Conditional GAN (cGAN) in PyTorch and TensorFlow]]   * [[https://learnopencv.com/conditional-gan-cgan-in-pytorch-and-tensorflow/|Conditional GAN (cGAN) in PyTorch and TensorFlow]]
   * [[https://machinelearningmastery.com/how-to-develop-a-conditional-generative-adversarial-network-from-scratch/|How to Develop a Conditional GAN (cGAN) From Scratch]]   * [[https://machinelearningmastery.com/how-to-develop-a-conditional-generative-adversarial-network-from-scratch/|How to Develop a Conditional GAN (cGAN) From Scratch]]
-  * [[https://arxiv.org/abs/1905.06841|Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems]] ({{ dlcp:kryukov22:biblio:1905.06841-stat_constrains-gan.pdf |PDF}}) +  * [[https://arxiv.org/abs/1905.06841|Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems]] ({{ archive:dlcp:kryukov22:biblio:1905.06841-stat_constrains-gan.pdf |PDF}}) 
-  * Amin Heyrani Nobari and et.al. {{ :dlcp:kryukov22:biblio:2106.03620-continuous_cgan.pdf |PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design}} +  * Amin Heyrani Nobari and et.al. {{ archive:dlcp:kryukov22:biblio:2106.03620-continuous_cgan.pdf |PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design}} 
-  * Xin Ding and et. al. {{ :dlcp:kryukov22:biblio:ccgan_continuous_conditional_g.pdf |ccGAN: CONTINUOUS CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS FOR I MAGE GENERATION}} +  * Xin Ding and et. al. {{ archive:dlcp:kryukov22:biblio:ccgan_continuous_conditional_g.pdf |ccGAN: CONTINUOUS CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS FOR I MAGE GENERATION}} 
-  * {{ :dlcp:kryukov22:biblio:07_gans_syntheticdata_rus_pptx.pdf |Генерация синтетических данных с использованием генеративных состязательных сетей}}+  * {{ archive:dlcp:kryukov22:biblio:07_gans_syntheticdata_rus_pptx.pdf |Генерация синтетических данных с использованием генеративных состязательных сетей}}
  
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   * [[https://keras.io/examples/generative/vae/|Variational AutoEncoder]] in  Keras   * [[https://keras.io/examples/generative/vae/|Variational AutoEncoder]] in  Keras
   * [[https://towardsdatascience.com/understanding-conditional-variational-autoencoders-cd62b4f57bf8|Understanding Conditional Variational Autoencoders]]   * [[https://towardsdatascience.com/understanding-conditional-variational-autoencoders-cd62b4f57bf8|Understanding Conditional Variational Autoencoders]]
-  * {{ :dlcp:kryukov22:biblio:1812.02833-disentagling_vae.pdf |Disentangling Disentanglement in Variational Autoencoders}} +  * {{ archive:dlcp:kryukov22:biblio:1812.02833-disentagling_vae.pdf |Disentangling Disentanglement in Variational Autoencoders}} 
-  * {{ :dlcp:kryukov22:biblio:abcs2018_paper_58-conv_ae.pdf |Convolutional Autoencoders}}+  * {{ archive:dlcp:kryukov22:biblio:abcs2018_paper_58-conv_ae.pdf |Convolutional Autoencoders}}
 ===== Diffusion NN ===== ===== Diffusion NN =====
  
-  * {{ dlcp:kryukov22:biblio:2104.07636-difusion_gen.pdf |Image Super-Resolution via Iterative Refinement}}+  * {{ archive:dlcp:kryukov22:biblio:2104.07636-difusion_gen.pdf |Image Super-Resolution via Iterative Refinement}}
   * [[https://iterative-refinement.github.io/|Сайт проекта]]   * [[https://iterative-refinement.github.io/|Сайт проекта]]
   * Готовое [[https://github.com/openai/guided-diffusion|ПО]] на GitHub   * Готовое [[https://github.com/openai/guided-diffusion|ПО]] на GitHub
   * [[https://lilianweng.github.io/posts/2021-07-11-diffusion-models/|What are Diffusion Models?]]   * [[https://lilianweng.github.io/posts/2021-07-11-diffusion-models/|What are Diffusion Models?]]
   * [[https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/|Introduction to Diffusion Models for Machine Learning]]   * [[https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/|Introduction to Diffusion Models for Machine Learning]]
-  * [[https://arxiv.org/pdf/2105.05233|Diffusion Models Beat GANs on Image Synthesis]] ({{ dlcp:kryukov22:biblio:2105.05233-diffusion.pdf |PDF}}) +  * [[https://arxiv.org/pdf/2105.05233|Diffusion Models Beat GANs on Image Synthesis]] ({{ archive:dlcp:kryukov22:biblio:2105.05233-diffusion.pdf |PDF}}) 
-  * [[https://arxiv.org/abs/2006.11239|Denoising Diffusion Probabilistic Models]] ({{ dlcp:kryukov22:biblio:2006.11239-denoising_diffusion_model.pdf |PDF}})+  * [[https://arxiv.org/abs/2006.11239|Denoising Diffusion Probabilistic Models]] ({{ archive:dlcp:kryukov22:biblio:2006.11239-denoising_diffusion_model.pdf |PDF}})
   * [[https://ayandas.me/blog-tut/2021/12/04/diffusion-prob-models.html|An introduction to Diffusion Probabilistic Models]]   * [[https://ayandas.me/blog-tut/2021/12/04/diffusion-prob-models.html|An introduction to Diffusion Probabilistic Models]]
   * [[https://github.com/openai/guided-diffusion|Tutorial на GitHub]]   * [[https://github.com/openai/guided-diffusion|Tutorial на GitHub]]
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