Modular design patterns for neural-symbolic integration: refinement and
combination
- URL: http://arxiv.org/abs/2206.04724v1
- Date: Thu, 9 Jun 2022 18:41:15 GMT
- Title: Modular design patterns for neural-symbolic integration: refinement and
combination
- Authors: Till Mossakowski
- Abstract summary: We formalise aspects of the neural-symbol design patterns of van Bekkum et al.
These formal notions are being implemented in the heterogeneous tool set (Hets)
- Score: 0.6853165736531939
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We formalise some aspects of the neural-symbol design patterns of van Bekkum
et al., such that we can formally define notions of refinement of patterns, as
well as modular combination of larger patterns from smaller building blocks.
These formal notions are being implemented in the heterogeneous tool set
(Hets), such that patterns and refinements can be checked for well-formedness,
and combinations can be computed.
Related papers
- Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis [27.1965932507935]
We propose a novel sewing pattern generation approach based on Large Multimodal Models (LMMs)
LMM offers an intuitive interface for interpreting diverse design inputs.
pattern-making programs could serve as well-structured and semantically meaningful representations of sewing patterns.
arXiv Detail & Related papers (2024-12-11T18:26:45Z) - Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations [56.78271181959529]
Generalized Additive Models (GAMs) can capture non-linear relationships between variables and targets, but they cannot capture intricate feature interactions.
We propose Shape Expressions Arithmetic ( SHAREs) that fuses GAM's flexible shape functions with the complex feature interactions found in mathematical expressions.
We also design a set of rules for constructing SHAREs that guarantee transparency of the found expressions beyond the standard constraints.
arXiv Detail & Related papers (2024-04-15T13:44:01Z) - Commonsense Ontology Micropatterns [1.181206257787103]
We present a collection of 104 design patterns representing often occurring nouns, curated from the commonsense knowledge available in Large Language Models.
This library is ready for use with the Modular Ontology Modeling methodology.
arXiv Detail & Related papers (2024-02-28T21:23:54Z) - Hybrid Modeling Design Patterns [10.266928164137635]
We provide four base patterns that can serve as blueprints for combining data-driven components with domain knowledge into a hybrid approach.
We also present two composition patterns that govern the combination of the base patterns into more complex hybrid models.
Each design pattern is illustrated by typical use cases from application areas such as climate modeling, engineering, and physics.
arXiv Detail & Related papers (2023-12-29T15:40:38Z) - Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT)
Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives.
This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
arXiv Detail & Related papers (2023-10-03T05:40:56Z) - Mode Combinability: Exploring Convex Combinations of Permutation Aligned
Models [0.559239450391449]
We investigate convex combinations of two permutation-aligned neural network parameter vectors $Theta_A$ and $Theta_B$ of size $d$.
We show that broad regions of the hypercube form surfaces of low loss values, indicating that the notion of linear mode connectivity extends to a more general phenomenon.
arXiv Detail & Related papers (2023-08-22T15:39:29Z) - A Recursive Bateson-Inspired Model for the Generation of Semantic Formal
Concepts from Spatial Sensory Data [77.34726150561087]
This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data.
The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept.
The model is able to produce fairly rich yet human-readable conceptual representations without training.
arXiv Detail & Related papers (2023-07-16T15:59:13Z) - Toward an Over-parameterized Direct-Fit Model of Visual Perception [5.4823225815317125]
In this paper, we highlight the difference in parallel and sequential binding mechanisms between simple and complex cells.
A new proposal for abstracting them into space partitioning and composition is developed.
We show how it leads to a dynamic programming (DP)-like approximate nearest-neighbor search based on $ell_infty$-optimization.
arXiv Detail & Related papers (2022-10-07T23:54:30Z) - Low-Rank Constraints for Fast Inference in Structured Models [110.38427965904266]
This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models.
Experiments with neural parameterized structured models for language modeling, polyphonic music modeling, unsupervised grammar induction, and video modeling show that our approach matches the accuracy of standard models at large state spaces.
arXiv Detail & Related papers (2022-01-08T00:47:50Z) - Learning Neural Generative Dynamics for Molecular Conformation
Generation [89.03173504444415]
We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph.
We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
arXiv Detail & Related papers (2021-02-20T03:17:58Z) - CoSE: Compositional Stroke Embeddings [52.529172734044664]
We present a generative model for complex free-form structures such as stroke-based drawing tasks.
Our approach is suitable for interactive use cases such as auto-completing diagrams.
arXiv Detail & Related papers (2020-06-17T15:22:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.