Search Results - "Tripathy, Aalap"

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  1. 1

    Web GIS in practice X: a Microsoft Kinect natural user interface for Google Earth navigation by Boulos, Maged N Kamel, Blanchard, Bryan J, Walker, Cory, Montero, Julio, Tripathy, Aalap, Gutierrez-Osuna, Ricardo

    “…This paper covers the use of depth sensors such as Microsoft Kinect and ASUS Xtion to provide a natural user interface (NUI) for controlling 3-D…”
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    Journal Article
  2. 2

    Common Metadata Framework: Integrated Framework for Trustworthy Artificial Intelligence Pipelines by Koomthanam, Annmary Justine, Tripathy, Aalap, Serebryakov, Sergey, Nayak, Gyanaranjan, Foltin, Martin, Bhattacharya, Suparna

    Published in IEEE internet computing (01-05-2024)
    “…In this article, we present the capabilities of the Common Metadata Framework (CMF) to enable trustworthy artificial intelligence (AI). CMF is a decentralized…”
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    Journal Article
  3. 3
  4. 4

    Knowledge Graph Empowered Machine Learning Pipelines for Improved Efficiency, Reusability, and Explainability by Venkataramanan, Revathy, Tripathy, Aalap, Foltin, Martin, Yip, Hong Yung, Justine, Annmary, Sheth, Amit, Sheth, Amit

    Published in IEEE internet computing (01-01-2023)
    “…Artificial intelligence (AI) pipelines are complex, heavily parameterized, and expensive to execute in terms of time and computational resources. Consequently,…”
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    Journal Article
  5. 5

    Towards FAIR Workflows for Federated Experimental Sciences by Saranathan, Gayathri, Martin, Foltin, Tripathy, Aalap, Justine, Annmary, Ziatdinov, Maxim, Ghosh, Ayana, Roccapriore, Kevin, Bhattacharya, Supama, Faraboschi, Paolo

    “…A de-centralized, peer-to-peer AI metadata framework is demonstrated which can enable end-to-end metadata & lineage tracking for distributed Machine Learning…”
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    Conference Proceeding
  6. 6

    Distributed Collaborative Filtering on a Single Chip Cloud Computer by Tripathy, Aalap, Patra, Atish, Mohan, Suneil, Mahapatra, Rabi

    “…Many-cores on chip have now become a reality. They necessitate the revisit of several layers of a cloud infrastructure. For this to happen, parallel…”
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    Conference Proceeding
  7. 7

    Designing a Collaborative Filtering Recommender on the Single Chip Cloud Computer by Tripathy, Aalap, Patra, Atish, Mohan, Suneil, Mahapatra, Rabi

    “…Fast response requirements for big-data applications on cloud infrastructures continues to grow. At the same time, many cores on-chip have now become a…”
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    Conference Proceeding
  8. 8

    A reconfigurable computing architecture for semantic information filtering by Tripathy, Aalap, Ka Chon Ieong, Patra, Atish, Mahapatra, Rabi

    “…The increasing amount of information accessible to a user digitally makes information retrieval & filtering difficult, time consuming and ineffective. New…”
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    Conference Proceeding
  9. 9

    Optimizing a Collaborative Filtering Recommender for Many-Core Processors by Tripathy, A., Mohan, S., Mahapatra, R.

    “…The web is moving from an era of "search" to that of "discovery". Collaborative filtering (CF) recommender systems are now commonly used to predict user's…”
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    Conference Proceeding
  10. 10

    A parallel architecture for meaning comparison by Mohan, Suneil, Biswas, Amitava, Tripathy, Aalap, Pannigrahy, Jagannath, Mahapatra, Rabi

    “…In this paper we present a fine grained parallel architecture that performs meaning comparison using vector cosine similarity (dot product). Meaning comparison…”
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    Conference Proceeding
  11. 11

    Optimizing a Semantic Comparator Using CUDA-enabled Graphics Hardware by Tripathy, A., Mohan, S., Mahapatra, R.

    “…Emerging semantic search techniques require fast comparison of large "concept trees". This paper addresses the challenges involved in fast computation of…”
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    Conference Proceeding
  12. 12

    Parallel Processor Core for Semantic Search Engines by Mohan, S., Tripathy, A., Biswas, A., Mahapatra, R.

    “…Superior and fast semantic comparison improves the quality of web-search. Semantic comparison involves dot product computation of large sparse tensors which is…”
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    Conference Proceeding
  13. 13

    Semantic Key for Meaning Based Searching by Biswas, A., Mohan, S., Tripathy, A., Panigrahy, J., Mahapatra, R.

    “…To enable meaning based search, computers have to store and compare meaning of user's search intentions and objects being searched. To address the key…”
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    Conference Proceeding