dlcp2023:restricted:review
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| dlcp2023:restricted:review [02/10/2023 12:05] – [Track 2. Machine Learning in Natural Sciences] admin | dlcp2023:restricted:review [06/10/2023 09:21] – [Track 1. Machine Learning in Fundamental Physics] admin | ||
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| | K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data | 21/09/2023 | Dudko \\ {{ : | | K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data | 21/09/2023 | Dudko \\ {{ : | ||
| | E.O.Gres. The selection of gamma events from IACT images with deep learning methods | 30/09/2023 | Dudko \\ {{ : | | E.O.Gres. The selection of gamma events from IACT images with deep learning methods | 30/09/2023 | Dudko \\ {{ : | ||
| - | | 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 \\ {{ : |
| - | | 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 | ||
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| | A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements | | Demichev \\ {{ : | | A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements | | Demichev \\ {{ : | ||
| | 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 \\ {{ : | | M.A.Ledovskikh. Recognition of skin lesions from image | 21/09/2023 | Krinitsky \\ {{ : | ||
| | 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/ | ||
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| | 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 | ||
dlcp2023/restricted/review.txt · Last modified: 06/10/2023 12:30 by admin