Bharat Singh

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We present a multi-stream bi-directional recurrent neural network for fine-grained action detection. Recently, twostream convolutional neural networks (CNNs) trained on stacked optical flow and image frames have been successful for action recognition in videos. Our system uses a tracking algorithm to locate a bounding box around the person, which provides a(More)
BACKGROUND AND OBJECTIVE In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the(More)
Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The(More)
Complex event retrieval is a challenging research problem, especially when no training videos are available. An alternative to collecting training videos is to train a large semantic concept bank a priori. Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the(More)
Recognition of medical concepts is a basic step in information extraction from clinical records. We wished to improve on the performance of a variety of concept recognition systems by combining their individual results. We selected two dictionary-based systems and five statistical-based systems that were trained to annotate medical problems, tests, and(More)
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks. This is largely because conventional optimization algorithms rely on stochastic gradient methods that don’t scale well to large numbers of cores in a cluster setting. Furthermore, the convergence of all(More)
The problem of searching occurrences of a pattern P[0...m-1] in the text T[0...n-1>with m ≤ n, where the symbols of P and T are drawn from some alphabet Σ of size σ, is called exact string matching problem. In the present day, pattern matching is a powerful tool in locating nucleotide or amino acid sequence patterns in the biological(More)
Word matching problem is to find all the occurrences of a pattern P[0…m-1] in the text T[0…n-1], where P neither contains any white space nor preceded and followed by space. In the multi-patterns word matching problem, all the occurrences of multiple word P0, P1, P2 …Pr-1, (r≥1) in the given text T are to be reported. In the(More)
Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) withM are suppressed. This process is recursively applied on the(More)