Semantic Scholar extracted view of "Streamflow Data Infilling Using Machine Learning Techniques with Gamma Test" by Saad Dahmani et al.
اقرأ أكثرStreamflow Data Infilling Using Machine Learning Techniques with Gamma Test Dahmani, Saad; Latif, Sarmad Dashti; Abstract. Publication: Water Resources Management. Pub Date: January 2024 DOI: 10.1007/s11269-023-03694-8 …
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اقرأ أكثرIn machine learning tasks, quality of the models depends very much on the data. But the data itself in real problems are rarely perfect. As a rule, the volume of data itself is small, the number of parameters available for analysis is limited, and the data includes noise and exhibits gaps.
اقرأ أكثرMost recently, machine learning-based methods have been increasingly used in infilling missing data for hydrological series such as k-nearest neighbors (KNN), random forest (RF), streamflow hydrology estimate using machine learning (SHEM), multiple imputations by chained equations (MICE), extreme gradient boosting (XGB), …
اقرأ أكثرAbstract We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document.While infilling could enable rich functionality especially for writing assistance tools, more attention has been devoted to language modeling—a special case of infilling where text is predicted at the end of a …
اقرأ أكثرText-infilling language models can predict appropriate text to infill the mask tokens in the input prompt. While generating, text-infilling language models can apply different masking strategies, which may lead to the generated article having different characteristics. ... If the synthetic machine data is generated by an arbitrary masking ...
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اقرأ أكثرThis work proposes a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation which aims to fill missing segments in a manually revised translation for a given source sentence by casting this task as a sequence-to-sequence task. Interactive neural machine translation (INMT) is able to guarantee high-quality …
اقرأ أكثر3 code implementations in PyTorch. We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document. While infilling could enable rich functionality especially for writing assistance tools, more attention has been devoted to language modeling---a special case of infilling where text is predicted at the …
اقرأ أكثرQuasi-static infilling simulations on 3D reconstructions of the separator structure indicate that there can be up to 30% gas entrapment upon infilling due to the geometry of the separator, which results in a reduction of effective transport by >40%. Considering the dynamics of the electrolyte (e.g., viscosity) and the infilling process …
اقرأ أكثرAndam-Akorf ul, "Comparis on of Missin g Data Infilling Mechanis ms for Recovering a Real-World S ingle Station Str eamflow Observation", Int. J. Enviro n. Res. Publ ic Health, vo l. 18, no ...
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اقرأ أكثرIn this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation. The key idea to BiTIIMT is Bilingual Text-infilling (BiTI) which aims to fill missing segments in a manually revised translation for a given source sentence. We propose a simple yet effective solution by casting this task as a ...
اقرأ أكثرBefore infilling real gaps, a first analysis was performed on synthetic gaps, in order to test the tool's capabilities and, at the same time, to build a knowledge database with synthetic gaps appropriately created and infilled. Conclusions. In this paper, we presented gapIt, a tool for infilling gaps in hydrological discharge time series.
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اقرأ أكثرStreamflow Data Infilling Using Machine Learning . T echniques with Gamma Test. Saad Dahmani 1 · Sarmad Dashti Latif 2,3. Received: 3 February 2023 / Accepted: 4 December 2023
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اقرأ أكثرIn other words, infilling perplexity is equivalent to taking the per-token perplexity after the [S E P] token. In Figure 1 D, we also compared IgLM infilling perplexity to methods using only preceding context (IgLM [pre], IgLM-S [pre], ProGen2-base, ProGen2-OAS). For these methods, rather than compute perplexity using our infilling …
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اقرأ أكثرBy capitalizing on existing resources and infilling vacant spaces, infill projects contribute to the creation of vibrant, livable, and resilient urban communities. Challenges of Infill Construction. While infill construction brings many benefits to urban development, it also presents a set of unique challenges that need to be addressed.
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اقرأ أكثرThere have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), …
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اقرأ أكثر2.1 Neural Machine Translation Neural machine translation (NMT) (Sutskever et al.,2014;Bahdanau et al.,2015;Vaswani et al., 2017) is based on a sequence to sequence model which adopts an encoder-decoder architecture. The encoder summarizes the source sentence into an intermediate representation, and the decoder gener-ates the target …
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