Constraint Breeds Generalization: Temporal Dynamics as an Inductive Bias
This work addresses the challenge of improving generalization in AI systems by introducing a novel perspective on constraints, which could impact the development of more robust machine learning models, though it appears to be an incremental advance in understanding inductive biases.
The paper tackles the problem of generalization in deep learning by proposing that physical constraints, like those in biological systems, can serve as a temporal inductive bias to improve generalization, and demonstrates through evaluations that a critical 'transition' regime maximizes generalization capability across tasks such as supervised classification and zero-shot reinforcement learning.
Conventional deep learning prioritizes unconstrained optimization, yet biological systems operate under strict metabolic constraints. We propose that these physical constraints shape dynamics to function not as limitations, but as a temporal inductive bias that breeds generalization. Through a phase-space analysis of signal propagation, we reveal a fundamental asymmetry: expansive dynamics amplify noise, whereas proper dissipative dynamics compress phase space that aligns with the network's spectral bias, compelling the abstraction of invariant features. This condition can be imposed externally via input encoding, or intrinsically through the network's own temporal dynamics. Both pathways require architectures capable of temporal integration and proper constraints to decode induced invariants, whereas static architectures fail to capitalize on temporal structure. Through comprehensive evaluations across supervised classification, unsupervised reconstruction, and zero-shot reinforcement learning, we demonstrate that a critical "transition" regime maximizes generalization capability. These findings establish dynamical constraints as a distinct class of inductive bias, suggesting that robust AI development requires not only scaling and removing limitations, but computationally mastering the temporal characteristics that naturally promote generalization.