DiffusionNet: Discretization Agnostic Learning on Surfaces

Agnostic G1 Gregory surfaces

We discuss G smoothness conditions for rectangular and triangular Gregory patches. We then incorporate these G conditions into a surface fitting algorithm. Knowledge of the patch type is inconsequential to the formulation of the G conditions, hence the term agnostic G Gregory surfaces. 2012 Elsevier Inc. All rights reserved.

Agnostic G Gregory Surfaces

We discuss G smoothness conditions for rectangular and triangular Gregory patches. We then incorporate these G conditions into a surface fitting algorithm. Knowledge of the patch type is inconsequential to the formulation of the G conditions, hence the term agnostic G Gregory surfaces.

Discretization of Surfaces

Agnostic Online Learning

We study learnability of hypotheses classes in agnostic online prediction models. The analogous question in the PAC learning model [Valiant, 1984] was addressed by Haussler [1992] and others, who showed that the VC dimension characterization of the sample complexity of learnability extends to the agnostic (or ”unrealizable”) setting. In his influential work, Littlestone [1988] described a combi...

Toward Eecient Agnostic Learning

In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to signiicantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algo...