Objects in freely-drawn sketches often have no spatial or temporal separation, making object recognition difficult. We present a two-step stroke-grouping algorithm that first clas...
Eric Jeffrey Peterson, Thomas F. Stahovich, Eric D...
Dimensionality reduction plays an important role in many machine learning and pattern recognition tasks. In this paper, we present a novel dimensionality reduction algorithm calle...
Some real-world problems are partially decomposable, in that they can be decomposed into a set of coupled subproblems, that are each relatively easy to solve. However, when these ...
Search based solvers for Quantified Boolean Formulas (QBF) have adapted the SAT solver techniques of unit propagation and clause learning to prune falsifying assignments. The tech...
The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic...
We present and implement a Weighted Partial MaxSAT solver based on successive calls to a SAT solver. We prove the correctness of our algorithm and compare our solver with other We...
We consider in this paper the size of a Decomposable Negation Normal Form (DNNF) that respects a given vtree (known as structured DNNF). This representation of propositional knowl...
Multi-instance learning, as other machine learning tasks, also suffers from the curse of dimensionality. Although dimensionality reduction methods have been investigated for many ...
Wei Ping, Ye Xu, Kexin Ren, Chi-Hung Chi, Shen Fur...