Core Concepts
The author explores how a relational bottleneck in neural networks can enhance learning efficiency, generalization, and the emergence of abstract representations, aligning network performance with human cognitive biases.
Abstract
The content delves into the impact of a relational bottleneck on neural networks' ability to learn factorized representations conducive to compositional coding. By introducing simple inductive biases, the paper demonstrates improved generalization and alignment with human-like behavioral biases. The study highlights how this approach promotes efficient learning of abstract representations without explicit symbolic primitives.
The research investigates how a relational bottleneck enhances learning efficiency and flexibility in neural networks by focusing on relations among inputs. By inducing factorized representations through this mechanism, the study shows improved generalization performance and alignment with human cognitive biases. The findings suggest that this approach may provide insights into how humans construct factorized representations of their environments.
Furthermore, the study compares the performance of networks with and without a relational bottleneck in tasks involving similarity judgments over pairs of inputs varying along orthogonal dimensions. Results indicate that the relational network learns orthogonal representations more efficiently than the standard network, leading to better generalization and alignment with human behavioral biases. This suggests that a relational bottleneck can facilitate the discovery of low-dimensional, abstract representations essential for flexible processing.
Overall, the content emphasizes how introducing a relational bottleneck in neural networks can enhance learning efficiency, promote factorized representations conducive to compositional coding, and align network performance with human cognitive biases.
Stats
Networks trained with the relational bottleneck developed orthogonal representations of feature dimensions latent in the dataset.
Relational architectures exhibited faster learning rates and out-of-distribution generalization compared to standard feedforward networks.
Relational models learned well-structured representations fundamental for compositional coding.
Relational architectures showed robust encoding of stimulus regularity compared to standard contrastive models.
Quotes
"Imposing a simple form of the relational bottleneck improves sample efficiency and generalization."
"The presence of a relational bottleneck encourages emergent symbols through binding in external memory."
"Relational architectures may insulate agents from overfitting risks common in traditional architectures."