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Full-Text Articles in Physical Sciences and Mathematics
A Study Of Machine Learning Techniques For Dynamical System Prediction, Rishi Pawar
A Study Of Machine Learning Techniques For Dynamical System Prediction, Rishi Pawar
Theses and Dissertations
Dynamical Systems are ubiquitous in mathematics and science and have been used to model many important application problems such as population dynamics, fluid flow, and control systems. However, some of them are challenging to construct from the traditional mathematical techniques. To combat such problems, various machine learning techniques exist that attempt to use collected data to form predictions that can approximate the dynamical system of interest. This thesis will study some basic machine learning techniques for predicting system dynamics from the data generated by test systems. In particular, the methods of Dynamic Mode Decomposition (DMD), Sparse Identification of Nonlinear Dynamics …
Dictionary-Based Data Generation For Fine-Tuning Bert For Adverbial Paraphrasing Tasks, Mark Anthony Carthon
Dictionary-Based Data Generation For Fine-Tuning Bert For Adverbial Paraphrasing Tasks, Mark Anthony Carthon
Theses and Dissertations
Recent advances in natural language processing technology have led to the emergence of
large and deep pre-trained neural networks. The use and focus of these networks are on transfer
learning. More specifically, retraining or fine-tuning such pre-trained networks to achieve state
of the art performance in a variety of challenging natural language processing/understanding
(NLP/NLU) tasks. In this thesis, we focus on identifying paraphrases at the sentence level using
the network Bidirectional Encoder Representations from Transformers (BERT). It is well
understood that in deep learning the volume and quality of training data is a determining factor
of performance. The objective of …
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, Tobias Michael Furtwaengler
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, Tobias Michael Furtwaengler
Theses and Dissertations
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedging and pricing of financial derivatives with neural networks. We utilize the concept of convex risk measures to define optimal hedging strategies without strong assumptions on the underlying market dynamics. Furthermore, the setting allows the incorporation of market frictions and thus the determination of optimal hedging strategies and prices even in incomplete markets. We then use the approximation capabilities of neural networks to find close-to optimal estimates for these strategies.
We will elaborate on the theoretical foundations of this approach and carry out implementations …
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, Tobias Michael Furtwaengler
Model-Independent Estimation Of Optimal Hedging Strategies With Deep Neural Networks, Tobias Michael Furtwaengler
Theses and Dissertations
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedging and pricing of financial derivatives with neural networks. We utilize the concept of convex risk measures to define optimal hedging strategies without strong assumptions on the underlying market dynamics. Furthermore, the setting allows the incorporation of market frictions and thus the determination of optimal hedging strategies and prices even in incomplete markets. We then use the approximation capabilities of neural networks to find close-to optimal estimates for these strategies.
We will elaborate on the theoretical foundations of this approach and carry out implementations …