Zobrazeno 1 - 10
of 448
pro vyhledávání: '"Malevich, P."'
Proactive Detection and Calibration of Seasonal Advertisements with Multimodal Large Language Models
Autor:
Eghbalzadeh, Hamid, Shao, Shuai, Verma, Saurabh, Mani, Venugopal, Wang, Hongnan, Madia, Jigar, Karpinchyk, Vitali, Malevich, Andrey
A myriad of factors affect large scale ads delivery systems and influence both user experience and revenue. One such factor is proactive detection and calibration of seasonal advertisements to help with increasing conversion and user satisfaction. In
Externí odkaz:
http://arxiv.org/abs/2411.00780
Autor:
Sancak, Kaan, Hua, Zhigang, Fang, Jin, Xie, Yan, Malevich, Andrey, Long, Bo, Balin, Muhammed Fatih, Çatalyürek, Ümit V.
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph Transformers (GTs) were intro
Externí odkaz:
http://arxiv.org/abs/2406.12059
Unlocking the potential of terahertz (THz) and millimetre (mm) waves for next generation communications and imaging applications requires reconfigurable intelligent surfaces (RIS) with programmable elements that can manipulate the waves in real-time.
Externí odkaz:
http://arxiv.org/abs/2404.17979
Autor:
Fu, Dongqi, Hua, Zhigang, Xie, Yan, Fang, Jin, Zhang, Si, Sancak, Kaan, Wu, Hao, Malevich, Andrey, He, Jingrui, Long, Bo
Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of graphs. Graph transformer conv
Externí odkaz:
http://arxiv.org/abs/2403.16030
Autor:
D. R. Gergel, S. B. Malevich, K. E. McCusker, E. Tenezakis, M. T. Delgado, M. A. Fish, R. E. Kopp
Publikováno v:
Geoscientific Model Development, Vol 17, Pp 191-227 (2024)
Global climate models (GCMs) are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and fut
Externí odkaz:
https://doaj.org/article/5501458b06db414aab87a7d6fc5b40a8
Autor:
Steiner, Pietro, Adnan, Saqeeb, Ergoktas, Muhammed Said, Barrier, Julien, Yu, Xiaoxiao, Orts, Vicente, Bakan, Gokhan, Aze, Jonathan, Malevich, Yury, Wang, Kaiyuan, Cataldi, Pietro, Bisset, Mark, Balci, Sinan, Suzer, Sefik, Khafizov, Marat, Kocabas, Coskun
The ability to control heat transport with electrical signals has been an outstanding challenge due to the lack of efficient electrothermal materials. Previous attempts have mainly concentrated on phase-change and layered materials and encountered va
Externí odkaz:
http://arxiv.org/abs/2202.10342
Autor:
Zeng, Hanqing, Zhang, Muhan, Xia, Yinglong, Srivastava, Ajitesh, Malevich, Andrey, Kannan, Rajgopal, Prasanna, Viktor, Jin, Long, Chen, Ren
Publikováno v:
Advances in Neural Information Processing Systems, 2021
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponential expansion of the scope (i.e., receptive field). Beyond just a few la
Externí odkaz:
http://arxiv.org/abs/2201.07858
Publikováno v:
π-Economy, Vol 17, Iss 2 (2024)
In the modern world, the concept of a “green economy” is becoming increasingly important, becoming a key aspect of the sustainable development strategy. Global challenges associated with climate change, depletion of natural resources and the thre
Externí odkaz:
https://doaj.org/article/9c045d7129c449e892fc48ae4170fb9e
Autor:
Aleksandr Babkin, Elena Shkarupeta, Ekaterina Malevskaia-Malevich, Ekaterina Pogrebinskaya, Louise Batukova
Publikováno v:
International Journal of Technology, Vol 14, Iss 8, Pp 1769-1778 (2023)
The primary objective of this research endeavor is the conceptualization and operationalization of the 'Circular Maturity' construct within the context of industrial ecosystems. A comprehensive evaluative framework is developed, designed to assess
Externí odkaz:
https://doaj.org/article/a8983f39409646ba895644d9bc92b8d4
Autor:
Zeng, Hanqing, Zhang, Muhan, Xia, Yinglong, Srivastava, Ajitesh, Malevich, Andrey, Kannan, Rajgopal, Prasanna, Viktor, Jin, Long, Chen, Ren
While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to three layers. Deep GNNs fundamentally need to address: 1). expressivity ch
Externí odkaz:
http://arxiv.org/abs/2012.01380