arXiv research

New method improves probability estimation accuracy by up to 50%.

Study explains Zipf's law using geometric mechanisms from a finite alphabet.

New algorithms improve time series classification accuracy and efficiency while enhancing interpretability.

Study measures entropy to assess market efficiency of ETFs.

A simple text model shows word lengths follow Zipf's law.

Symbolic Data Analysis is based on special descriptions of data - symbolic objects (SO). Such descriptions preserve more detailed information about units and their clusters than the usual representations with mean values. A special kind of symbolic object is a representation with frequency or probability distributions …

Simplified Ricci curvature for spherical fluid dynamics models.

Co-eye combines multiple symbolic representations to improve time series classification accuracy.

A system is presented that segments, clusters and predicts musical audio in an unsupervised manner, adjusting the number of (timbre) clusters instantaneously to the audio input. A sequence learning algorithm adapts its structure to a dynamically changing clustering tree. The flow of the system is as follows: 1) segment…

VaSST uses soft symbolic trees for probabilistic symbolic regression.

New DL algorithm estimates OFDM channels without pilots.

Unified framework integrates symbolic planning and HRL for robust decision-making.

EML-CD discovers causal mechanisms from neural networks in a structured way.

Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure …

New method for constructing frames for vector distributions with specific symbols.

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…

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

Geometric symbols help compute heat invariants.

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…

Symbolic dynamics applied to share prices reveals complex, non-Markovian patterns.

TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.

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…

Profile entropy measures learnability and compressibility of discrete distributions.

New technique finds globally optimal symbolic equations.

A formula for Rademacher symbols in triangle groups is provided.

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.

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

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

Paper proposes a bijective approach for signal/symbol translation using variational auto-encoders.

The Wodzicki residue and the cut-off integral extend to classical symbol-valued forms. We show that they obey a Stokes' type property and that the extended Wodzicki residue can be interpreted as a complex residue like the ordinary one. In the case of cut-off integrals, Stokes' property (i.e. vanishing on exact forms) o…

Perceptor Gradients learns symbolic representations from raw data.

Bayesian method estimates coverage from sketching imperfect data.

Bayesian symbolic regression automates model discovery from data.

Study compares symbolic and distributional methods for relational learning.

New SDA models for big data analysis using aggregated symbols.

Symbolic regression finds simple formulas for implied volatility.

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

The study improves pitch detection in polyphonic music by learning harmonic priors.

S2KAN integrates symbolic primitives into neural network activations for improved interpretability.

A new risk distribution model reduces inflated risk capital calculations.

Neural networks learn symbolic structure to perform compositional tasks.

Symbolic LSTM improves time series forecasting by reducing hyperparameter sensitivity.

Introduces canonical connections for sub-Riemannian manifolds with constant symbol.

Meta-learning symbolic default hyperparameters from dataset properties.

ISR creates analytical relationships from data via invertible maps.