General deep learning models have shown remarkable capability to capture intricate patterns across various data types, leading to high accuracy across various prediction tasks. However, when these models are applied to specific domains, they may encounter challenges related to data scarcity, unique characteristics, and distinct constraints. Data scarcity may constrain model generalization, increase overfitting risks, and complicate pattern extraction from limited datasets. Insensitivity to unique domain-specific characteristics can result in the oversight of key features crucial for the task. Distinct constraints may influence the design of objective functions and evaluation frameworks, thereby shaping the model’s performance and results. These challenges can restrict the efficacy of ge...[ Read more ]

General deep learning models have shown remarkable capability to capture intricate patterns across various data types, leading to high accuracy across various prediction tasks. However, when these models are applied to specific domains, they may encounter challenges related to data scarcity, unique characteristics, and distinct constraints. Data scarcity may constrain model generalization, increase overfitting risks, and complicate pattern extraction from limited datasets. Insensitivity to unique domain-specific characteristics can result in the oversight of key features crucial for the task. Distinct constraints may influence the design of objective functions and evaluation frameworks, thereby shaping the model’s performance and results. These challenges can restrict the efficacy of general deep learning models in addressing domain-specific problems, highlighting the need for domain adaptation techniques and tailored model designs to enhance performance in specialized contexts.

This thesis explores the application of deep learning methodologies across a spectrum of domain-specific subjects, including asset dependency forecasting, volatility modeling, breast cancer early detection, and influential recommender systems. In the first study, we propose innovative transformation techniques for market segmentation within the asset dependency matrix, enabling effective representation learning from volatile financial data. Additionally, we incorporate a Mix-of-Experts (MoE) architecture to address regime-switching dynamics. The second study bridges the gap between stochastic and neural network volatility modeling approaches by establishing an equivalence relationship between Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and their corresponding neural network counterparts. By integrating the GARCH structure into the neural network architecture, our framework effectively captures the inherent stylized facts of GARCH models and improves accuracy. In the third study, we propose a household solution for early breast cancer diagnosis utilizing wearable sensors and time series classification techniques. We develop novel noise-filtering and transformation pipelines to uncover highly domain-specific patterns from restricted and noisy clinical data. The fourth study introduces a novel recommendation paradigm that incorporates influential behavior to proactively guide a user’s interests through a meticulously selected items sequence. We present three tailored frameworks adapted from existing recommender systems and develop a unique offline evaluation framework and metrics for this purpose. Overall, this thesis advances knowledge in deep learning research and provides practical insights for professionals and researchers seeking to apply general deep learning techniques to solve domain-specific problems.