arXiv Machine Learning July 7, 2026 By Fusheng Luo, H'elyette Geman

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage

Read the original on arXiv Machine Learning →

arXiv:2605. 12764v3 Announce Type: replace-cross Abstract: This paper introduces a physics-informed generative framework that resolves the fundamental conflict between the statistical flexibility of deep learning and the rigorous theoretical constraints of fixed-income modeling.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

arXiv Machine Learning

Aug 10

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

arXiv:2604. 00669v2 Announce Type: replace Abstract: This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India.

By Sandeep Kumar Samota, Reema Gupta, Snehashish Chakraverty

rag diffusion

arXiv Machine Learning

Jun 15

Neural ARFIMA model for forecasting BRIC exchange rates with long memory

arXiv:2509. 06697v3 Announce Type: replace-cross Abstract: Exchange rate forecasting remains a challenging problem, particularly for emerging economies, where the observed time series exhibit pronounced long-memory dependence, nonlinear dynamics, and sensitivity to macro-financial drivers.

By Donia Besher, Madhurima Panja, Shovon Sengupta, Tanujit Chakraborty

benchmarks

arXiv Machine Learning

Jun 15

Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise

arXiv:2512. 14967v2 Announce Type: replace Abstract: We present a novel numerical method for solving McKean--Vlasov forward--backward stochastic differential equations (MV--FBSDEs) with common noise, combining Picard iterations, elicitability and deep learning.

By Felipe J. P. Antunes, Yuri F. Saporito, Sebastian Jaimungal

arXiv Machine Learning

Aug 7

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.

By Guillaume Couairon, Alexis Jacq, Yu-Han Wu, Renu Singh, Yana Hasson, Quentin Berthet, Romuald Elie

diffusion fine-tuning benchmarks

arXiv Machine Learning

Jul 28

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

arXiv:2605. 05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories.

By Tianyue Yang, Xiao Xue

diffusion benchmarks

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FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

Neural ARFIMA model for forecasting BRIC exchange rates with long memory

Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting