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- Neural networks pose challenges in finance due to small datasets and unstable outputs, requiring faster and more precise solutions.
- Alternative methods like Generalized Stochastic Sampling and Functional Tensor Train offer improved accuracy and computational efficiency for financial applications.
Deep dives
Challenges of Neural Networks in Finance
Vladimir discusses the limitations of neural networks in finance, highlighting issues such as training time, predictability, and explainability of outputs. While neural networks have found success in various applications like image recognition, finance presents unique challenges with small datasets and specific problem structures. Vladimir emphasizes the need for faster and more precise methods tailored to financial problems.
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