Papers

• The sharp SAT/UNSAT phase transition in random ellipsoid fitting (2026)
  Theodor Misiakiewicz et al.
  2.00
• Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks (2026)
  Ram\'on Nartallo-Kaluarachchi and Renaud Lambiotte and Alain Goriely
  1.67
• Learning and extrapolating scale-invariant processes (2026)
  Anaclara Alvez-Canepa et al.
  1.61
• Modelling financial time series with φ⁴ quantum field theory (2025)
  Dimitrios Bachtis et al.
  1.56
• Bayesian Optimization of Multi-Bit Pulse Encoding in In2O3/Al2O3 Thin-film Transistors for Temporal Data Processing (2025)
  Javier Meza-Arroyo et al.
  1.44
• Inference in Spreading Processes with Neural-Network Priors (2025)
  Davide Ghio et al.
  1.39
• Quantum generative modeling for financial time series with temporal correlations (2025)
  David Dechant et al.
  1.28
• Learning Stochastic Thermodynamics Directly from Correlation and Trajectory-Fluctuation Currents (2025)
  Jinghao Lyu and Kyle J. Ray and James P. Crutchfield
  1.11
• Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures (2025)
  T\^am Johan Nguy\^en et al.
  1.06
• Scale Dependencies and Self-Similar Models with Wavelet Scattering Spectra (2022)
  Rudy Morel et al.
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• KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design (2022)
  Ad\`ele Douin et al.
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• Spectrum of non-Hermitian deep-Hebbian neural networks (2022)
  Zijian Jiang and Ziming Chen and Tianqi Hou and Haiping Huang
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• Time-shift selection for reservoir computing using a rank-revealing QR algorithm (2022)
  Joseph D. Hart and Francesco Sorrentino and Thomas L. Carroll
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• Neuronal architecture extracts statistical temporal patterns (2023)
  Sandra Nestler et al.
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• Stochastic Interpolants: A Unifying Framework for Flows and Diffusions (2023)
  Michael S. Albergo et al.
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• Deep neural networks from the perspective of ergodic theory (2023)
  Fan Zhang
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• Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics (2023)
  Apimuk Sornsaeng et al.
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• Novel models for fatigue life prediction under wideband random loads based on machine learning (2023)
  Hong Sun et al.
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• Dynamical stability and chaos in artificial neural network trajectories along training (2024)
  Kaloyan Danovski et al.
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• Application of time-series quantum generative model to financial data (2024)
  Shun Okumura et al.
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• Hierarchical Associative Memory, Parallelized MLP-Mixer, and Symmetry Breaking (2024)
  Ryo Karakida et al.
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• Training a multilayer dynamical spintronic network with standard machine learning tools to perform time series classification (2024)
  Erwan Plouet et al.
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