1,727,801 research outputs found
Aryan Nations (Australia) - Aryan Nations stands for ...
No full textAryan Nations (Australia) - Aryan Nations stands for ... [ca. 1970s
Aryan Nations (Australia) - Death of the white race
No full textAryan Nations (Australia) - Death of the white race [ca. 1970s
Aryan Nations (Australia) - Life of the white race
No full textAryan Nations (Australia) - Life of the white race [ca. 1970s
Alignment and Ergativity in New Indo-Aryan Languages /
No full textThe book provides an overview of the alignment patterns found in modern Indo-Aryan languages. The analysis of the patterns of case marking and agreement leads to a balanced view on the concept of ergativity and evaluates its value for typological linguistics. The book offers an extensive discussion of previous approaches to ergativity. It analyzes four Indo-Aryan languages– Asamiya, Nepali, Rajasthani and Kashmiri– on the basis of text corpora. Examples from other Indo-Aryan languages are also adduced. The book is a thorough synchronic study of alignment patterns in Indo-Aryan languages.The book provides an overview of the alignment patterns found in modern Indo-Aryan languages. The analysis of the patterns of case marking and agreement leads to a balanced view on the concept of ergativity and evaluates its value for typological linguistics. The book offers an extensive discussion of previous approaches to ergativity. It analyzes four Indo-Aryan languages– Asamiya, Nepali, Rajasthani and Kashmiri– on the basis of text corpora. Examples from other Indo-Aryan languages are also adduced. The book is a thorough synchronic study of alignment patterns in Indo-Aryan languages.Electronic reproduction.Mode of access: Internet via World Wide Web.Saartje Verbeke, Ghent University, Belgium.Description based on online resource; title from PDF title page (publisher’s Web site, viewed March 24, 2015
aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow: Final Release
No full textThis repository contains the implementation of paper Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting with different loss functions in Tensorflow. We have compared 14 regression loss functions performance on 4 different datasets
aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-PyTorch: Final Release
No full textThis repository compares the performance of 8 different regression loss functions used in Time Series Forecasting using Temporal Fusion Transformers
Ab initio perspective of ultra-scaled CMOS from 2D-material fundamentals to dynamically doped transistors
No full textRecommended from our members
Official Publication of the Liberal Arts Council
No full textContents: October 25, 2020 Poems // by Aryan Bhalla -- October 31, 2020 The Only Thing to Fear Is Fear Itself: Horror Movies and the Social Consciousness // by Alyssa HiarkerLiberal Art
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