In this paper, we propose a new framework for the computational learning of formal grammars with positive data. In this model, both syntactic and semantic information are taken int...
Within this paper a new framework for Bayesian tracking is presented, which approximates the posterior distribution at multiple resolutions. We propose a tree-based representation...
Bjoern Stenger, Arasanathan Thayananthan, Philip H...
Accurately estimating probabilities from observations is important for probabilistic-based approaches to problems in computational biology. In this paper we present a biologically...
Eleazar Eskin, William Stafford Noble, Yoram Singe...
This work proposes a novel practical and general-purpose lossless compression algorithm named Neural Markovian Predictive Compression (NMPC), based on a novel combination of Bayesi...
In this paper, we propose a novel multi-mode/multi-corner sparse regression (MSR) algorithm to build large-scale performance models of integrated circuits at multiple working mode...
Wangyang Zhang, Tsung-Hao Chen, Ming Yuan Ting, Xi...