Binding Dynamics in Rotating Features

• URL: http://arxiv.org/abs/2402.05627v1
• Date: Thu, 8 Feb 2024 12:31:08 GMT
• Title: Binding Dynamics in Rotating Features
• Authors: Sindy L\"owe, Francesco Locatello, Max Welling
• Abstract summary: We propose an alternative "cosine binding" mechanism, which explicitly computes the alignment between features and adjusts weights accordingly. This allows us to draw direct connections to self-attention and biological neural processes, and to shed light on the fundamental dynamics for object-centric representations to emerge in Rotating Features.
• Score: 72.80071820194273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In human cognition, the binding problem describes the open question of how the brain flexibly integrates diverse information into cohesive object representations. Analogously, in machine learning, there is a pursuit for models capable of strong generalization and reasoning by learning object-centric representations in an unsupervised manner. Drawing from neuroscientific theories, Rotating Features learn such representations by introducing vector-valued features that encapsulate object characteristics in their magnitudes and object affiliation in their orientations. The "$\chi$-binding" mechanism, embedded in every layer of the architecture, has been shown to be crucial, but remains poorly understood. In this paper, we propose an alternative "cosine binding" mechanism, which explicitly computes the alignment between features and adjusts weights accordingly, and we show that it achieves equivalent performance. This allows us to draw direct connections to self-attention and biological neural processes, and to shed light on the fundamental dynamics for object-centric representations to emerge in Rotating Features.

Related papers

• 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)
• Rotating Features for Object Discovery [74.1465486264609]
  We present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations. Together, these advancements enable us to scale distributed object-centric representations from simple toy to real-world data.
  arXiv  Detail & Related papers  (2023-06-01T12:16:26Z)
• Robust and Controllable Object-Centric Learning through Energy-based Models [95.68748828339059]
  ours is a conceptually simple and general approach to learning object-centric representations through an energy-based model. We show that ours can be easily integrated into existing architectures and can effectively extract high-quality object-centric representations.
  arXiv  Detail & Related papers  (2022-10-11T15:11:15Z)
• Sparse Relational Reasoning with Object-Centric Representations [78.83747601814669]
  We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric representations. We find that increasing sparsity, especially on features, improves the performance of some models and leads to simpler relations.
  arXiv  Detail & Related papers  (2022-07-15T14:57:33Z)
• Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning [79.4957965474334]
  Key goal of unsupervised representation learning is "inverting" a data generating process to recover its latent properties. This paper asks, "Can we instead identify latent properties by leveraging knowledge of the mechanisms that govern their evolution?" We provide a complete characterization of the sources of non-identifiability as we vary knowledge about a set of possible mechanisms.
  arXiv  Detail & Related papers  (2021-10-29T14:04:08Z)
• Structure-Regularized Attention for Deformable Object Representation [17.120035855774344]
  Capturing contextual dependencies has proven useful to improve the representational power of deep neural networks. Recent approaches that focus on modeling global context, such as self-attention and non-local operation, achieve this goal by enabling unconstrained pairwise interactions between elements. We consider learning representations for deformable objects which can benefit from context exploitation by modeling the structural dependencies that the data intrinsically possesses.
  arXiv  Detail & Related papers  (2021-06-12T03:10:17Z)
• On the Binding Problem in Artificial Neural Networks [12.04468744445707]
  We argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information. We propose a unifying framework that revolves around forming meaningful entities from unstructured sensory inputs. We believe that a compositional approach to AI, in terms of grounded symbol-like representations, is of fundamental importance for realizing human-level generalization.
  arXiv  Detail & Related papers  (2020-12-09T18:02:49Z)
• Binding and Perspective Taking as Inference in a Generative Neural Network Model [1.0323063834827415]
  generative encoder-decoder architecture adapts its perspective and binds features by means of retrospective inference. We show that the resulting gradient-based inference process solves the perspective taking and binding problem for known biological motion patterns.
  arXiv  Detail & Related papers  (2020-12-09T16:43:26Z)
• Learning Intermediate Features of Object Affordances with a Convolutional Neural Network [1.52292571922932]
  We train a deep convolutional neural network (CNN) to recognize affordances from images and to learn the underlying features or the dimensionality of affordances. We view this representational analysis as the first step towards a more formal account of how humans perceive and interact with the environment.
  arXiv  Detail & Related papers  (2020-02-20T19:04:40Z)

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.