Search Results - subject:"Deep-Learning"

Quantile preferences in portfolio choice: A Q-DRL approach to dynamic diversification

Sarkany, Attila; Janásek, Lukáš; Baruník, Jozef - 2024

We develop a novel approach to understand the dynamic diversification of decision makers with quantile preferences. Due to unavailability of analytical solutions to such complex problems, we suggest to approximate the behavior of agents with a Quantile Deep Reinforcement Learning (Q-DRL)...

Persistent link: https://www.econbiz.de/10014577259

Quantile preferences in portfolio choice : a Q-DRL approach to dynamic diversification

Sarkany, Attila; Janásek, Lukáš; Baruník, Jozef - 2024

Persistent link: https://www.econbiz.de/10014532001

LocalGLMnet : interpretable deep learning for tabular data

Richman, Ronald; Wüthrich, Mario V. - In: Scandinavian actuarial journal 2023 (2023) 1, pp. 71-95

Persistent link: https://www.econbiz.de/10013491052

Hidden value : provenance as a source for economic and social history

Rother, Lynn; Mariani, Fabio; Koss, Max - In: Jahrbuch für Wirtschaftsgeschichte 64 (2023) 1, pp. 111-142

deep learning to address natural language processing tasks for transforming provenance text into structured data, such as …

Persistent link: https://www.econbiz.de/10014292980

Food Security in the Economy of the Future : Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning

Popkova, Elena G. (ed.); Sergi, Bruno S. (ed.) - 2023

Revolution -- Chapter 1: Strategic Directions for Smart Agriculture Based on Deep Learning for Future Risk Management of Food … Level of Food Security as a Result of the Transition of Food-Importing Countries to Agriculture 4.0 based on Deep Learning … Economy and Climate Risk Management for Enterprises of Agriculture 4.0 based on Deep Learning. By Tatiana N. Litvinova …

Persistent link: https://www.econbiz.de/10014239909

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A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code

Askitas, Nikos - 2024

This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your...

Persistent link: https://www.econbiz.de/10014567597

Surrogate modelling of a detailed farm‐level model using deep learning

Shang, Linmei; Wang, Jifeng; Schäfer, David; Heckelei, … - In: Journal of Agricultural Economics 75 (2024) 1, pp. 235-260

challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep … learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the …

Persistent link: https://www.econbiz.de/10014469431

Forecasting in financial accounting with artificial intelligence : a systematic literature review and future research agenda

Kureljusic, Marko; Karger, Erik - In: Journal of applied accounting research 25 (2024) 1, pp. 81-104

Persistent link: https://www.econbiz.de/10014506027

Meta-heuristics and deep learning for energy applications : review and open research challenges (2018-2023)

Hosseini, Eghbal; Al-Ghaili, Abbas M.; Kadir, Dler Hussein - In: Energy strategy reviews 53 (2024), pp. 1-23

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the … complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data … into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The …

Persistent link: https://www.econbiz.de/10014634031

LSTM-ARIMA as a hybrid approach in algorithmic investment strategies

Kashif, Kamil; Ślepaczuk, Robert - 2024

Persistent link: https://www.econbiz.de/10014634690

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