Abdul-Saboor Sheikh

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Slif uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. Slif originally focused on fluorescence microscopy images. We have now extended it to classify panels into more(More)
An increasing number of experimental studies indicate that perception encodes a posterior probability distribution over possible causes of sensory stimuli, which is used to act close to optimally in the environment. One outstanding difficulty with this hypothesis is that the exact posterior will in general be too complex to be represented directly, and thus(More)
We study inference and learning based on a sparse coding model with ‘spike-and-slab’ prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the application of a more flexible(More)
We define and discuss the first sparse coding algorithm based on closedform EM updates and continuous latent variables. The underlying generative model consists of a standard ‘spike-and-slab’ prior and a Gaussian noise model. Closed-form solutions for Eand M-step equations are derived by generalizing probabilistic PCA. The resulting EM algorithm can take(More)
The SLIF project combines text-mining and image processing to extract structured information from biomedical literature. SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the images are classified according to their type (e.g., micrograph(More)
The Structured Literature Image Finder tackles two related problems posed by the vastness of the biomedical literature: how to make it more accessible to scientists in the field and how to take advantage of the primary data often locked inside papers. Towards this goal, the slif project developed an innovative combination of text and image processing(More)
Modelling natural images with sparse coding (SC) has faced two main challenges: flexibly representing varying pixel intensities and realistically representing lowlevel image components. This paper proposes a novel multiple-cause generative model of low-level image statistics that generalizes the standard SC model in two crucial points: (1) it uses a(More)
Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this(More)
Classifiers for the semi-supervised setting often combine strong supervised models with additional learning objectives to make use of unlabeled data. This results in powerful though very complex models that are hard to train and that demand additional labels for optimal parameter tuning, which are often not given when labeled data is very sparse. We here(More)