arXiv research

VaSST uses soft symbolic trees for probabilistic symbolic regression.

A new method for spotting symbols in CAD images reduces annotation costs and improves accuracy.

Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may …

MESSY estimation recovers symbolic density functions from samples using maximum entropy.

Meta-learning symbolic default hyperparameters from dataset properties.

Improved symbolic regression finds optimal formulas robust to noise.

PySR method automates discovering equations from data in chaotic dynamics and epidemics.

ISR creates analytical relationships from data via invertible maps.

Improves neural network search in combinatorial spaces of mathematical symbols.

Bayesian symbolic regression automates model discovery from data.

Symbolic LSTM improves time series forecasting by reducing hyperparameter sensitivity.

The symbolic dynamics technique is well-known for low-dimensional dynamical systems and chaotic maps, and lies at the roots of the thermodynamic formalism of dynamical systems. Here we show that this technique can also be successfully applied to time series generated by complex systems of much higher dimensionality. Ou…

OccamNet finds interpretable symbolic fits to data efficiently.

Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.

This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.

Secure Multiparty Computation protects data privacy in Symbolic Regression.

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks su…

SATNet solves the Symbol Grounding Problem, enabling self-supervised learning.

Develops a Bayesian framework for symbolic regression of scientific expressions.

PAN+SR tackles scalable symbolic regression for large $p$ datasets.

How can neural networks perform so well on compositional tasks even though they lack explicit compositional representations? We use a novel analysis technique called ROLE to show that recurrent neural networks perform well on such tasks by converging to solutions which implicitly represent symbolic structure. This meth…

QABBA improves time series storage efficiency while preserving shape information.

Neural-symbolic model improves link prediction in knowledge graphs.

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

Extract symbolic models from deep learning with inductive biases.

A hybrid algorithm combines optimization and enumeration for symbolic regression.

Paper introduces a new symbol map for differential symmetry breaking operators.

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. …

New CR hypersurfaces in complex space with specific properties.

For an arbitrary Riemannian manifold $X$ and Hermitian vector bundles $E$ and $F$ over $X$ we define the notion of the normal symbol of a pseudodifferential operator $P$ from $E$ to $F$. The normal symbol of $P$ is a certain smooth function from the cotangent bundle $T^*X$ to the homomorphism bundle $Hom (E,F)$ and dep…

Active learning improves SR by proposing experiments in data-limited settings.

Based on the ideas of Optimal Control, we introduce the new basic characteristic of a bracket generating distribution, the Jacobi symbol. In contrast to the classical Tanaka symbol, the set of Jacobi symbols is discrete and classifiable. We give an explicit and unified algebraic procedure for the construction of the ca…

DeepSIC learns to detect multiple symbols in MIMO systems without assuming a specific channel model.

Symbolic data analysis (SDA) is an emerging area of statistics concerned with understanding and modelling data that takes distributional form (i.e. symbols), such as random lists, intervals and histograms. It was developed under the premise that the statistical unit of interest is the symbol, and that inference is requ…

Enhances Cox model for survival analysis with symbolic non-linear log-risk functions.

Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.

We introduce mod 3 triple Milnor invariants and triple cubic residue symbols for certain primes of the Eisenstein number field $\mathbb{Q}(\sqrt{-3})$, following the analogies between knots and primes. Our triple symbol generalizes both the cubic residue symbol and Rédei's triple symbol, and describes the decomposition…

FIGARO generates symbolic music with fine-grained control.

Efficiently searches through Gaussian process kernels using symbolic representation and Bayesian optimization.

For phase-space manifolds which are compact Kaehler manifolds relations between the Berezin-Toeplitz quantization and the quantization with the help of Berezin's coherent states and symbols are studied. First the results on the Berezin-Toeplitz quantization of arbitrary compact Kaehler manifolds due to Bordemann, Meinr…

EPSTE: A geometric token and deep learning approach to estimating transfer entropy in neuroimaging time series

A formula for Rademacher symbols in triangle groups is provided.

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this propert…

Complex $b$-$6j$ symbols relate to hyperbolic tetrahedron volumes and determinants.

The paper classifies symbols of differential operators on vector bundles.

NeSS combines neural and symbolic approaches for better compositional generalization.

Defines transverse symbols for foliated manifolds and proves their K-homology class.