dlcp2023:restricted:review
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| dlcp2023:restricted:review [02/10/2023 23:04] – [Track 2. Machine Learning in Natural Sciences] admin | dlcp2023:restricted:review [05/03/2025 17:31] (current) – ↷ Links adapted because of a move operation admin | ||
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| ====== Review status ====== | ====== Review status ====== | ||
| - | Шаблон рецензии в формате {{ :dlcp2023: | + | Шаблон рецензии в формате {{ dlcp2023: |
| Шаблон письма с просьбой провести рецензирование: | Шаблон письма с просьбой провести рецензирование: | ||
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| ^ Corresponding Author and Article Title ^ Date of review | ^ Corresponding Author and Article Title ^ Date of review | ||
| - | | M.I.Petrovskiy. DEEP LEARNING METHODS FOR THE TASKS OF CREATING " | + | | M.I.Petrovskiy. DEEP LEARNING METHODS FOR THE TASKS OF CREATING " |
| ==== Track 1. Machine Learning in Fundamental Physics ==== | ==== Track 1. Machine Learning in Fundamental Physics ==== | ||
| ^ Corresponding Author and Article Title ^ Date of review | ^ Corresponding Author and Article Title ^ Date of review | ||
| - | | Ju.Dubenskaya et al., Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks | + | | Ju.Dubenskaya et al., Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks |
| - | | R.R.Fitagdinov. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks | + | | R.R.Fitagdinov. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks |
| - | | K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data | 21/09/2023 | Dudko \\ {{ :dlcp2023: | + | | K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data | 21/09/2023 | Dudko \\ {{ dlcp2023: |
| - | | E.O.Gres. The selection of gamma events from IACT images with deep learning methods | 30/09/2023 | Dudko \\ {{ :dlcp2023: | + | | E.O.Gres. The selection of gamma events from IACT images with deep learning methods | 30/09/2023 | Dudko \\ {{ dlcp2023: |
| - | | A.Kryukov. Preliminary results of convolutional neural network models in HiSCORE experiment | 21/09/2023 | **Dolenko< | + | | A.Kryukov. Preliminary results of convolutional neural network models in HiSCORE experiment | 03/10/2023 | Dolenko \\ {{ dlcp2023: |
| - | | A.Kryukov. The use of conditional variational autoencoders for simulation of EASs images from IACTs | 21/09/2023 | Review || | + | | A.Kryukov. The use of conditional variational autoencoders for simulation of EASs images from IACTs | 21/ |
| - | | V.S.Latypova / Method for separating extensive air showers by primary mass using machine learning for a SPHERE-type Cherenkov telescope | + | | V.S.Latypova / Method for separating extensive air showers by primary mass using machine learning for a SPHERE-type Cherenkov telescope |
| - | | A.Y.Leonov. Deep Learning for Angle of Arrival Prediction in the Baikal Neutrino Telescope | + | | A.Y.Leonov. Deep Learning for Angle of Arrival Prediction in the Baikal Neutrino Telescope |
| - | | A.V. Matseiko. Application of machine learning methods in Baikal-GVD: | + | | A.V. Matseiko. Application of machine learning methods in Baikal-GVD: |
| - | | A.D.Zaborenko. Novelty Detection Neural Networks for Model-Independent New Physics Search | 21/09/2023 | Ilyin \\ {{ :dlcp2023: | + | | A.D.Zaborenko. Novelty Detection Neural Networks for Model-Independent New Physics Search | 21/09/2023 | Ilyin \\ {{ dlcp2023: |
| ==== Track 2. Machine Learning in Natural Sciences ==== | ==== Track 2. Machine Learning in Natural Sciences ==== | ||
| ^ Corresponding Author and Article Title ^ Date of review | ^ Corresponding Author and Article Title ^ Date of review | ||
| - | | M.Borisov. Estimating cloud base height from all-sky imagery using artificial neural networks | + | | M.Borisov. Estimating cloud base height from all-sky imagery using artificial neural networks |
| - | | S.Dolenko (A.Guskov). Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy | + | | S.Dolenko (A.Guskov). Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy |
| - | | I.M.Gadzhiev. Classification Approach to Prediction of Geomagnetic Disturbances | + | | I.M.Gadzhiev. Classification Approach to Prediction of Geomagnetic Disturbances |
| - | | V.Golikov. Client-server application for automated estimation of the material composition of bottom sediments in the >0.1 mm fraction from microphotography using modern deep learning methods | 21/09/2023 | Ilyin \\ {{ :dlcp2023: | + | | V.Golikov. Client-server application for automated estimation of the material composition of bottom sediments in the >0.1 mm fraction from microphotography using modern deep learning methods | 21/09/2023 | Ilyin \\ {{ dlcp2023: |
| - | | A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements | | Demichev \\ {{ :dlcp2023: | + | | A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements | | Demichev \\ {{ dlcp2023: |
| - | | I.Khabutdinov. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models | + | | I.Khabutdinov. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models |
| - | | M.Krinitsky. Estimating significant wave height from X-band navigation radar using convolutional neural networks | + | | M.Krinitsky. Estimating significant wave height from X-band navigation radar using convolutional neural networks |
| - | | M.A.Ledovskikh. Recognition of skin lesions from image | 21/09/2023 | Krinitsky \\ {{ :dlcp2023: | + | | M.A.Ledovskikh. Recognition of skin lesions from image | 21/09/2023 | Krinitsky \\ {{ dlcp2023: |
| - | | A.V.Orekhov. Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve | 13/ | + | | A.V.Orekhov. Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve | 13/ |
| - | | S.A.Pavlov. Application of Machine Learning Methods to Numerical Simulation of Hypersonic Flow | 05/ | + | | S.A.Pavlov. Application of Machine Learning Methods to Numerical Simulation of Hypersonic Flow | 05/ |
| - | | V.Y.Rezvov. Improvement of the AI-based estimation of signifi cant wave height based on preliminary training on synthetic X-band radar sea clutter images | + | | V.Y.Rezvov. Improvement of the AI-based estimation of signifi cant wave height based on preliminary training on synthetic X-band radar sea clutter images |
| - | | O.E.Sarmanova. Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions | 21/ | + | | O.E.Sarmanova. Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions | 21/ |
| - | | A.Savin. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches | + | | A.Savin. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches |
| - | | A.Tyshko. Automatic detection of acoustic signals from white whales and bottle-nosed dolphins | < | + | | A.Tyshko. Automatic detection of acoustic signals from white whales and bottle-nosed dolphins | < |
| - | | A.V. Vorobev. Machine learning for diagnostics of space weather effects in the Arctic region | + | | A.V. Vorobev. Machine learning for diagnostics of space weather effects in the Arctic region |
| ==== Track 3. Modern Machine Learning Methods ==== | ==== Track 3. Modern Machine Learning Methods ==== | ||
| ^ Corresponding Author and Article Title ^ Date of review | ^ Corresponding Author and Article Title ^ Date of review | ||
| - | | N.Y.Bykov / Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems | + | | N.Y.Bykov / Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems |
| - | | S.Dolenko. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm | + | | S.Dolenko. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm |
| - | | I.Isaev. The study of the integration of physical methods in the neural network solution of the inverse problem of exploration geophysics with variable physical properties of the medium | + | | I.Isaev. The study of the integration of physical methods in the neural network solution of the inverse problem of exploration geophysics with variable physical properties of the medium |
| - | | Z.Kurdoshev. The importance of the number of overfits in time series forecasting by some optimizers and loss functions in neural networks | + | | Z.Kurdoshev. The importance of the number of overfits in time series forecasting by some optimizers and loss functions in neural networks |
| - | | D.N.Polyakov / Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation | + | | D.N.Polyakov / Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation |
| ==== Withdrawn papers ==== | ==== Withdrawn papers ==== | ||
dlcp2023/restricted/review.1696277068.txt.gz · Last modified: 02/10/2023 23:04 by admin