arXiv research

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

Symbolic grounding in causal dynamics achieves near-infinite temporal consistency.

Cosmos models scenes using neural encodings and symbolic attributes for compositional generalization.

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…

Develops a Bayesian framework for symbolic regression of scientific expressions.

Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand. Human communication is primarily based on symbolic abstractions of object properties, rather than precise quantitative measures. A comprehensive robotic framework thus…

ARTEMIS combines deep learning and symbolic reasoning for financial predictions.

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

A framework isolates VQA reasoning from perception for better model evaluation.

Human ability at solving complex tasks is helped by priors on object and event semantics of their environment. This paper investigates the use of similar prior knowledge for transfer learning in Reinforcement Learning agents. In particular, the paper proposes to use a first-order-logic language grounded in deep neural …

HAL learns hierarchical affordances to prune impossible subtasks, improving reinforcement learning efficiency.

Model-based machine learning improves communication systems.

We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representations can be learned in a task-independent egocentric space specific to the agent that, when grounded with problem-specific information, are …

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

Transformers learn multi-step reasoning through gradient descent.

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this i…

SymNet uses neural models to solve RMDPs efficiently.

A two-step approach efficiently selects hyperparameters for FCMs.

VaSST uses soft symbolic trees for probabilistic symbolic regression.

DCR improves interpretability of concept-based models by using neural networks to build rule structures.

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.

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 …

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…

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…

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…

Paper investigates differentiable fuzzy implications and their suitability for learning.

A formula for Rademacher symbols in triangle groups is provided.

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…

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.

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

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…

Bayesian symbolic regression automates model discovery from data.

Symbolic regression finds simple formulas for implied volatility.

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

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

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.

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

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

Quantum $6j$-symbols linked to tetrahedra angles and volumes.