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Transfer Learning
av Qiang Yang , Yu Zhang , Wenyuan Dai m.fl.
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning …
Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic …
The idea of interfacing minds with machines has long captured the human imagination. Recent advances in neuroscience and engineering are making this a reality, opening the door to …
On social media, new forms of communication arise rapidly, many of which are intense, dispersed, and create new communities at a global scale. Such communities can act as distinct …
Empirical Translation Studies is a rapidly evolving research area. This volume, written by world-leading researchers, demonstrates the integration of two new research paradigms: …
Acta Numerica is an annual publication containing invited survey papers by leading researchers in numerical mathematics and scientific computing. The papers present overviews of …
The architectures and mechanisms underlying language processing form one important part of the general structure of cognition. This book, written by leading experts in the field, …
Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary …
This study explores an approach to text generation that interprets systemic grammar as a computational representation. Terry Patten demonstrates that systemic grammar can be easily …
Digital health translation is an important application of machine translation and multilingual technologies, and there is a growing need for accessibility in digital health …