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We propose DISCOVER, a discriminative model using CRFs for motif discovery.
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We propose to discover the triggering relations on network requests and leverage the structural information to identify stealthy malware activities that cannot be attributed to a legitimate cause.
In this paper we proposed to discover meaningful topics from documents and forecast their future trend.
As non-specificity and toxicity are a major drawback with conventional chemotherapeutics, we proposed to discover novel agents acting primarily through the caspase-8 axis as opposed to generalized toxicity.
In this paper, we propose a feature discovering method incorporated with a wavelet-like pattern decomposition strategy to address the image classification problem.
Figure 9 shows the RD performance of the proposed, DISCOVER, and H.264/AVC methods for two sequences.
In this work, we propose algorithms that automatically discover and exploit the hidden structure of factored MDPs.
Therefore, in this paper, we propose an approach to discover a category of relevant association rules based on multi-criteria analysis.
In this paper, we propose an algorithm to discover negative contrasts sets across groups, and establish some properties to accelerate the execution of the algorithm.
We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene, gene-disease) from literature, 3) context vector based similarity score calculation.
In order to conveniently compare and categorize the numerous potential impact solutions being discovered we propose a new hazard scale that will describe the risk posed by a particular potential impact in both absolute and relative terms.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com