[Paper Review] On the Binding Problem in Artificial Neural Networks

Klaus Greff, Sjoerd van Steenkiste+1 more|arXiv (Cornell University)|Dec 9, 2020

Neural Networks and Applications353 references50 citations

TL;DR

The paper argues that neural networks fail to generalize because they cannot dynamically bind distributed information into symbol-like objects, and proposes a unifying framework around segregation, representation, and composition to address this binding problem within connectionist models.

ABSTRACT

Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network. This binding problem affects their capacity to acquire a compositional understanding of the world in terms of symbol-like entities (like objects), which is crucial for generalizing in predictable and systematic ways. To address this issue, we propose a unifying framework that revolves around forming meaningful entities from unstructured sensory inputs (segregation), maintaining this separation of information at a representational level (representation), and using these entities to construct new inferences, predictions, and behaviors (composition). Our analysis draws inspiration from a wealth of research in neuroscience and cognitive psychology, and surveys relevant mechanisms from the machine learning literature, to help identify a combination of inductive biases that allow symbolic information processing to emerge naturally in neural networks. We believe that a compositional approach to AI, in terms of grounded symbol-like representations, is of fundamental importance for realizing human-level generalization, and we hope that this paper may contribute towards that goal as a reference and inspiration.

Motivation & Objective

• Argue that lack of dynamic binding of distributed information limits symbol-like entity formation in neural networks.
• Present a unifying framework based on segregation, representation, and composition to address binding.
• Survey neuroscience, psychology, and ML mechanisms to identify inductive biases enabling emergent symbolic processing in networks.
• Advocate for grounded, compositional representations as a path to human-level generalization within neural networks.

Proposed method

• Define the binding problem in neural networks via three aspects: representation, segregation, and composition.
• Review object representations to avoid the superposition catastrophe and maintain separation of object features.
• Discuss representational formats emphasizing separation, common format, and disentanglement.
• Explore representational dynamics including temporal stability and uncertainty handling.
• Survey slot-based object representations (slots) and their variants (instance, sequential, spatial slots) as mechanisms to achieve object separation and compositionality.

Experimental results

Research questions

• RQ1 How can neural networks dynamically bind distributed information to form object-like representations?
• RQ2 What inductive biases and mechanisms enable emergent symbolic processing within connectionist systems?
• RQ3 Which representational formats and dynamics support robust segregation and flexible composition of objects?
• RQ4 Can a unified, internally grounded framework improve systematic generalization without hybrid symbolic modules?

Key findings

• Neural networks struggle with systematic generalization due to insufficient dynamic binding of distributed information.
• A unifying framework around segregation, representation, and composition can guide the development of grounded symbol-like representations.
• Object representations must be separable, share a common format, and be disentangled to support flexible reasoning and generalization.
• Temporal dynamics and uncertainty handling are crucial for stable object representations over time.
• Slot-based representations offer concrete approaches to object separation, with various instantiations and trade-offs analyzed.