It has always been difficult for language understanding systems to handle spontaneous speech with satisfactory robustness, primarily due to such problems as the fragments, disflue...
Bor-shen Lin, Berlin Chen, Hsin-Min Wang, Lin-Shan...
—Applying nonequilibrium statistical mechanics we focus on nonequilibrium corrections Δs to entropy and energy of the fluid in terms of the nonequilibrium density distribution f...
Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two...
Probabilistic models of languages are fundamental to understand and learn the profile of the subjacent code in order to estimate its entropy, enabling the verification and predicti...
Models of language learning play a central role in a wide range of applications: from psycholinguistic theories of how people acquire new word knowledge, to information systems th...