The impact of delayed sensor-alarm data upon a diagnostic inference engine appears not to be well appreciated. In this paper we illustrate the effect of sensor latency, and we pro...
Ozgur Erdinc, Craig Brideau, Peter Willett, Thia K...
Malicious users can exploit the correlation among data to infer sensitive information from a series of seemingly innocuous data accesses. Thus, we develop an inference violation d...
This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model...
Nancy Cartwright relies upon an inference pattern known as inference to the best causal explanation (IBCE) to support a limited form of entity realism, according to which we are wa...
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
Automated deduction methods should be specified not procedurally, but declaratively, as inference systems which are proved correct regardless of implementation details. Then, di...
Background: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-pro...
Jean-Philippe Vert, Jian Qiu, William Stafford Nob...
: Combining multiple data sources, each with its own features, to achieve optimal inference has received a lot of attention in recent years. In inference from multiple data sources...
Shankara B. Subramanya, Zheshen Wang, Baoxin Li, H...
While quantitative probabilistic networks (QPNs) allow the expert to state influences between nodes in the network as influence signs, rather than conditional probabilities, infer...
Background: The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of m...