arXiv Machine Learning
Enhancing Transformer Representations of Symbolic ODE Expressions
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VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees
arXiv:2602. 23561v2 Announce Type: replace-cross Abstract: Symbolic regression (SR) has gained recent traction in AI-driven scientific discovery for learning closed-form physical laws.
arXiv Machine Learning
Jul 27
Breaking the Data Barrier in Learning Symbolic Computation: A Case Study on Variable Ordering Suggestion for Cylindrical Algebraic Decomposition
arXiv:2601. 13731v2 Announce Type: replace-cross Abstract: Symbolic computation, powered by modern computer algebra systems, has important applications in mathematical reasoning through exact deep computations.
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MoNo: Multiscale Optimal Transport Neural Operator for Solving PDEs on General Geometries
arXiv:2608. 09764v1 Announce Type: cross Abstract: Transformer-based neural operators have achieved substantial progress in solving Partial Differential Equations (PDEs) by projecting spatial observations into compact latent tokens and learning physical interactions in latent spaces.
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SJEPA: Learning Elegant Latent Dynamics with Hybrid Symbolic-Neural Predictors
arXiv:2608. 04060v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn abstract states by predicting target embeddings from context embeddings, but their transition models are typically opaque neural maps.
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Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms
arXiv:2505. 23696v2 Announce Type: replace Abstract: Solving systems of polynomial equations, particularly those with finitely many solutions, is a crucial challenge across many scientific fields.
arXiv Machine Learning
Sep 2
Neural Symbollic Regression Using Deep Learning and Sparse Modelling
Neural Symbolic Regression (NSR) uses neural networks as functional preconditioners to learn smooth, noise‑robust approximations of target functions in an interaction‑aware nonlinear feature space. A subsequent LASSO step extracts sparse, interpretable closed‑form expressions, while distributed hyperparameter optimization with Ray Tune and ASHA scheduling improves predictive accuracy and symbolic fidelity. Experiments on the Nguyen benchmark demonstrate that NSR outperforms SINDy and untuned neural baselines in RMSE, noise robustness, and out‑of‑distribution generalization, with ablation studies highlighting the importance of feature interactions, neural depth, and tuning strategies.