We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biol...
The work presented in this paper explores a supervised method for learning a probabilistic model of a lexicon of VerbNet classes. We intend for the probabilistic model to provide ...
This paper explores automatically detecting student zoning out while performing a spoken learning task. Standard supervised machine learning techniques were used to create classiï¬...
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Topic models have recently emerged as powerful tools for modeling topical trends in documents. Often the resulting topics are broad and generic, associating large groups of people...
Vidit Jain, Erik G. Learned-Miller, Andrew McCallu...