4 citations · 7 across the 23 of their papers we have counts for
7 papers · 1 filter
Structural Learning Theory: A Metric-Topology Factorization Approach
Xin Li
Learning in structured, multi-context, or non-stationary environments involves two orthogonal difficulties. The first is \emph{metric}: once the correct context is known, how hard…
The Urysohn Ladder: Recursive Metric Contraction for Scalable Continual Learning
Xin Li
Continual learning systems face a fundamental geometric obstacle: as experience accumulates on a fixed-capacity manifold, covering numbers grow linearly with time, eventually forci…
Memory-Amortized Inference: A Topological Unification of Search, Closure, and Structure
Xin Li
Contemporary ML separates the static structure of parameters from the dynamic flow of inference, yielding systems that lack the sample efficiency and thermodynamic frugality of bio…
The Urysohn Machine: A Metric-Topological Model of Computation
Xin Li
We introduce the Urysohn Machine, an effective model of classification-oriented computation in which metric separation, frontier structure, and contraction are explicit parts of th…
Persistent Homology as a Theory of Emergent Structure
Xin Li
Why do some macroscopic structures remain identifiable even though their microscopic constituents continually change? Vortices persist while fluid parcels turn over, neural memorie…
Structural Decoupling: A Scaffold-Flow Theory of Generalization and Alignment
Xin Li
Learning in non-stationary and multi-context environments requires more than ordinary within-task generalization. A system must also discover which contexts exist, route inputs to…