Zobrazeno 1 - 10
of 26
pro vyhledávání: '"MAHADIK, KANAK"'
Autor:
Xie, Kaige, Yu, Tong, Wang, Haoliang, Wu, Junda, Zhao, Handong, Zhang, Ruiyi, Mahadik, Kanak, Nenkova, Ani, Riedl, Mark
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To efficiently learn from few-shot samples, previous works have utilized massiv
Externí odkaz:
http://arxiv.org/abs/2305.12077
We study the correlated stochastic knapsack problem of a submodular target function, with optional additional constraints. We utilize the multilinear extension of submodular function, and bundle it with an adaptation of the relaxed linear constraints
Externí odkaz:
http://arxiv.org/abs/2207.01551
Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- an
Externí odkaz:
http://arxiv.org/abs/2112.10068
Deep probabilistic forecasting techniques have recently been proposed for modeling large collections of time-series. However, these techniques explicitly assume either complete independence (local model) or complete dependence (global model) between
Externí odkaz:
http://arxiv.org/abs/2010.07373
Traditional methods for demand forecasting only focus on modeling the temporal dependency. However, forecasting on spatio-temporal data requires modeling of complex nonlinear relational and spatial dependencies. In addition, dynamic contextual inform
Externí odkaz:
http://arxiv.org/abs/2009.12469
Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. These algorithms continuously learn latent mappings between users and items, based on contexts associated with them both. Recent recom
Externí odkaz:
http://arxiv.org/abs/2007.08061
Akademický článek
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Autor:
Xie, Kaige, Yu, Tong, Wang, Haoliang, Wu, Junda, Zhao, Handong, Zhang, Ruiyi, Mahadik, Kanak, Nenkova, Ani, Riedl, Mark
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To efficiently learn from few-shot samples, previous works have utilized massiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2541c68149b0e84b777df4425c4e6e37
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Feb2023, Vol. 17 Issue 2, p1-30, 30p
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
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