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Learning in the Absence of Training Data
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Abstract
Individual chapters are listed in the "Chapters" tab
Suggested Citation
DOI: 10.1007/978-3-031-31011-9
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Book Chapters
The following chapters of this book are listed in IDEAS- Dalia Chakrabarty, 2023. "Bespoke Learning to Generate Originally-Absent Training Data," Springer Books, in: Learning in the Absence of Training Data, chapter 0, pages 1-22, Springer.
- Dalia Chakrabarty, 2023. "Learning the Temporally-Evolving Evolution-Driving Function of a Dynamical System, to Forecast Future States: Forecasting New COVID19 Infection Numbers," Springer Books, in: Learning in the Absence of Training Data, chapter 0, pages 23-100, Springer.
- Dalia Chakrabarty, 2023. "Potential to Density via Poisson Equation: Application to Bespoke Learning of Gravitational Mass Density in Real Galaxy," Springer Books, in: Learning in the Absence of Training Data, chapter 0, pages 101-151, Springer.
- Dalia Chakrabarty, 2023. "Bespoke Learning in Static Systems: Application to Learning Sub-surface Material Density Function," Springer Books, in: Learning in the Absence of Training Data, chapter 0, pages 153-188, Springer.
- Dalia Chakrabarty, 2023. "Bespoke Learning of Disease Progression Using Inter-Network Distance: Application to Haematology-Oncology: Joint Work with Dr. Kangrui Wang, Dr. Akash Bhojgaria and Dr. Joydeep Chakrabartty," Springer Books, in: Learning in the Absence of Training Data, chapter 0, pages 189-217, Springer.
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