arXiv research

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

Bayesian symbolic regression automates model discovery from data.

NeSS combines neural and symbolic approaches for better compositional generalization.

Transformer models can solve complex math problems with less data.

SE-RRMs solve structured problems like Sudoku and ARC-AGI by enforcing symbol equivariance.

Hybrid model learns novel handwritten characters better than neural or symbolic models alone.

Neural-symbolic model improves link prediction in knowledge graphs.

Arguments in favor of injecting symbolic knowledge into neural architectures abound. When done right, constraining a sub-symbolic model can substantially improve its performance and sample complexity and prevent it from predicting invalid configurations. Focusing on deep probabilistic (logical) graphical models -- i.e.…

Symbolic regression improved by incorporating prior knowledge.

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

VaSST uses soft symbolic trees for probabilistic symbolic regression.

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…

Bayesian symbolic regression uncovers missing physics from data with uncertainty quantification.

Shape-constrained symbolic regression improves model extrapolation with prior knowledge.

OccamNet finds interpretable symbolic fits to data efficiently.

Extract symbolic models from deep learning with inductive biases.

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

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

CSML learns causal structures for few-shot learning.

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

Meta-learning symbolic default hyperparameters from dataset properties.

R2T hybrid model improves robust regression for asymmetric noise.

Generative Neuro-Symbolic model learns from raw data with rich conceptual representations.

Neuro-symbolic traders suppress market prices, highlighting risks to stability.

Data-driven symbol detection improves performance in complex channels.

QABBA improves time series storage efficiency while preserving shape information.

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…

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the pr…

MARLeME extracts MARL models into symbolic models for better interpretability.

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…

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 …

It is known that every ribbon category with unimodality allows symmetrized $6j$-symbols with full tetrahedral symmetries while a spherical category does not in general. We give an explicit counterexample for this, namely the category $\mathcal{E}$. We define the mirror conjugate symmetry of $6j$-symbols instead and sho…

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…

Secure Multiparty Computation protects data privacy in Symbolic Regression.

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. …

CHOOSE enhances shallow Transformers for wireless symbol detection.

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…

Develops a Bayesian framework for symbolic regression of scientific expressions.

In a noisy environment, a lossy speech signal can be automatically restored by a listener if he/she knows the language well. That is, with the built-in knowledge of a "language model", a listener may effectively suppress noise interference and retrieve the target speech signals. Accordingly, we argue that familiarity w…

Efficient algorithm for identifying causal effects in linear models.

The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such r…

FIGARO generates symbolic music with fine-grained control.

Develops experimental design for discovering missing physics in bioreactors.

This work identifies and mitigates reasoning shortcuts in Neuro-Symbolic models.

We generalize the colored Alexander invariant of knots to an invariant of graphs, and we construct a face model for this invariant by using the corresponding 6j-symbol, which comes from the non-integral representations of the quantum group U_q(sl_2). We call it the SL(2, C) quantum 6j-symbol, and show its relation to t…

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…

Develops new approach to recover CR structures from their Levi foliations.

ARTEMIS combines deep learning and symbolic reasoning for financial predictions.