Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Pritom Saha"'
Autor:
Md. Naimul Haque, Rahid Hassan Galib, Pritom Saha, Foysal Hossain, Mizanur Rahman, Nasir Ahmed, Jalis Ahosan
Publikováno v:
Journal of Asian Architecture and Building Engineering, Vol 0, Iss 0, Pp 1-14 (2024)
Flexural strengthening of reinforced concrete (RC) beams is an important research topic due to its huge practical demand. In the present study, a new flexural strengthening method for RC beams, called the Shear-key Mounted Unbonded Rebar (SMUR) techn
Externí odkaz:
https://doaj.org/article/044856ddaf864358bc908ff099f76077
Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due to the need to accurately capture diverse facets of input queries. Exis
Externí odkaz:
http://arxiv.org/abs/2410.15511
Topic modeling is a powerful technique for uncovering hidden themes within a collection of documents. However, the effectiveness of traditional topic models often relies on sufficient word co-occurrence, which is lacking in short texts. Therefore, ex
Externí odkaz:
http://arxiv.org/abs/2410.03071
Long-form question answering (LFQA) poses a challenge as it involves generating detailed answers in the form of paragraphs, which go beyond simple yes/no responses or short factual answers. While existing QA models excel in questions with concise ans
Externí odkaz:
http://arxiv.org/abs/2311.09383
Topic models are one of the compelling methods for discovering latent semantics in a document collection. However, it assumes that a document has sufficient co-occurrence information to be effective. However, in short texts, co-occurrence information
Externí odkaz:
http://arxiv.org/abs/2310.15420
Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limitations, including insensitivit
Externí odkaz:
http://arxiv.org/abs/2310.04978
In this work, we study the problem of unsupervised open-domain keyphrase generation, where the objective is a keyphrase generation model that can be built without using human-labeled data and can perform consistently across domains. To solve this pro
Externí odkaz:
http://arxiv.org/abs/2306.10755
We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a semantic space with a reference representation. It then uses the axes
Externí odkaz:
http://arxiv.org/abs/2210.08559
In the current world, OLAP (Online Analytical Processing) is used intensively by modern organizations to perform ad hoc analysis of data, providing insight for better decision making. Thus, the performance for OLAP is crucial; however, it is costly t
Externí odkaz:
http://arxiv.org/abs/2204.07125
Autor:
Akash, Pritom Saha, Huang, Jie, Chang, Kevin Chen-Chuan, Li, Yunyao, Popa, Lucian, Zhai, ChengXiang
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain. Such a task is crucial for many downstream tasks in natural language processing. To co
Externí odkaz:
http://arxiv.org/abs/2203.10365