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pro vyhledávání: '"Dhama, Gaurav"'
Autor:
Chaurasiya, Deepak, Surisetty, Anil, Kumar, Nitish, Singh, Alok, Dey, Vikrant, Malhotra, Aakarsh, Dhama, Gaurav, Arora, Ankur
Entity Alignment (EA) identifies entities across databases that refer to the same entity. Knowledge graph-based embedding methods have recently dominated EA techniques. Such methods map entities to a low-dimension space and align them based on their
Externí odkaz:
http://arxiv.org/abs/2205.08777
Planning based on long and short term time series forecasts is a common practice across many industries. In this context, temporal aggregation and reconciliation techniques have been useful in improving forecasts, reducing model uncertainty, and prov
Externí odkaz:
http://arxiv.org/abs/2201.11964
In the context of time series forecasting, it is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. However, choosing among different ensembles over multiple methods remains
Externí odkaz:
http://arxiv.org/abs/2112.08052
The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vul
Externí odkaz:
http://arxiv.org/abs/2112.04236
The inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA results in almost every NLP task - Machine Translation, Text Summarizatio
Externí odkaz:
http://arxiv.org/abs/2111.15436
Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural netwo
Externí odkaz:
http://arxiv.org/abs/2111.15417
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