Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
In this paper we survey work being conducted at Imperial College on the use of machine learning to build Systems Biology models of the effects of toxins on biochemical pathways. Se...
In this paper, we introduce an interdisciplinary project, involving researchers from the fields of Physical Therapy, Computer Science, Psychology, Communication and Cell Neurobiol...
Shih-Ching Yeh, Albert A. Rizzo, Weirong Zhu, Jill...
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...