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pro vyhledávání: '"Boris, N"'
This paper presents an enhanced N-BEATS model, N-BEATS*, for improved mid-term electricity load forecasting (MTLF). Building on the strengths of the original N-BEATS architecture, which excels in handling complex time series data without requiring pr
Externí odkaz:
http://arxiv.org/abs/2412.02722
Autor:
Glavas, Theodore, Chataoui, Joud, Regol, Florence, Jabbour, Wassim, Valkanas, Antonios, Oreshkin, Boris N., Coates, Mark
The vast size of Large Language Models (LLMs) has prompted a search to optimize inference. One effective approach is dynamic inference, which adapts the architecture to the sample-at-hand to reduce the overall computational cost. We empirically exami
Externí odkaz:
http://arxiv.org/abs/2410.20022
Autor:
Slautin, Boris N., Liu, Yu, Dec, Jan, Shvartsman, Vladimir V., Lupascu, Doru C., Ziatdinov, Maxim, Kalinin, Sergei V.
We have developed a Bayesian optimization (BO) workflow that integrates intra-step noise optimization into automated experimental cycles. Traditional BO approaches in automated experiments focus on optimizing experimental trajectories but often overl
Externí odkaz:
http://arxiv.org/abs/2410.02717
Online deep learning solves the problem of learning from streams of data, reconciling two opposing objectives: learn fast and learn deep. Existing work focuses almost exclusively on exploring pure deep learning solutions, which are much better suited
Externí odkaz:
http://arxiv.org/abs/2405.18281
Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Distributional forecasting is used to control and mitigate risks associated with this uncertainty. Recent progress in deep
Externí odkaz:
http://arxiv.org/abs/2404.17451
Autor:
Slautin, Boris N., Liu, Yongtao, Funakubo, Hiroshi, Vasudevan, Rama K., Ziatdinov, Maxim A., Kalinin, Sergei V.
Scientific advancement is universally based on the dynamic interplay between theoretical insights, modelling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integratio
Externí odkaz:
http://arxiv.org/abs/2404.12899
Autor:
Slautin, Boris N., Pratiush, Utkarsh, Ivanov, Ilia N., Liu, Yongtao, Pant, Rohit, Zhang, Xiaohang, Takeuchi, Ichiro, Ziatdinov, Maxim A., Kalinin, Sergei V.
The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection methods, or several characterization tools to explore identical sample
Externí odkaz:
http://arxiv.org/abs/2402.02198
The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-l
Externí odkaz:
http://arxiv.org/abs/2402.00071
Autor:
Latosh, Boris N
Publikováno v:
Symmetry 2024, 16(1), 117
The article reviews recent progress in computational quantum gravity caused by the framework that efficiently computes Feynman's rules. The framework is implemented in the FeynGrav package, which extends the functionality of the widely used FeynCalc
Externí odkaz:
http://arxiv.org/abs/2401.05608