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This paper proposes a dual-domain fused LSTM (DDF-LSTM) model for time-dependent reliability analysis that integrates time-independent random variables and stochastic processes to efficiently estimate failure probabilities via Monte Carlo simulation.
This paper proposes a unified denoising diffusion framework for conditional generation of graph signals, introducing a novel U-GNN architecture that extends U-Net to graph-structured data. The method is demonstrated on stock price forecasting and wireless resource allocation tasks.
This paper introduces Branched Neural Rough Differential Equations, a method for learning manifold and Itô dynamics by combining rough path theory with neural networks, enabling the modeling of complex stochastic and geometric structures.
Jane Street offers $750k/year for quants who can apply Stochastic Processes and Markov Chains in trading, and a free MIT lecture covers similar material.