Explanipedia

Enhancing Semantic Document Retrieval- Employing Group Steiner Tree Algorithm with Domain Knowledge Enrichment Open

Apurva Kulkarni, Chandrashekar Ramanathan, Vinu E. Venugopal · 2025

Retrieving pertinent documents from various data sources with diverse characteristics poses a significant challenge for Document Retrieval Systems. The complexity of this challenge is further compounded when accounting for the semantic rel…

Cloud-Powered Neural Networks: The Democratization of Deep Learning through Cloud Computing Open

Vinu E. Venugopal · 2025

The democratization of deep learning through cloud computing represents a transformative shift in artificial intelligence accessibility, breaking down traditional barriers that once limited participation to well-funded institutions. This a…

Stat-n-Ball: Enhancing Probabilistic Knowledge Graph Embeddings with Geometric and Confidence-Aware Models Open

Aniket Mitra, Vinu E. Venugopal · 2025

The Quantum Frontier: How Quantum Computing Will Transform Data Science Open

Vinu E. Venugopal · 2025

Quantum computing represents a paradigm shift in computational capability that promises to transform data science by addressing problems currently intractable on classical systems. As global data creation expands exponentially, quantum tec…

Table of Contents Open

Faisal Shennib, Ursula Eicker, Ketra Schmitt, Apurva Kulkarni, Chandrashekar Ramanathan , et al. · 2024

TensAIR: Real-Time Training of Neural Networks from Data-streams Open

Mauro Dalle Lucca Tosi, Vinu E. Venugopal, Martin Theobald · 2024

Online learning (OL) from data streams is an emerging area of research that encompasses numerous challenges from stream processing, machine learning, and networking. Stream-processing platforms, such as Apache Kafka and Flink, have basic e…

BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph Open

Jingjing Xu, Maria Biryukov, Martin Theobald, Vinu E. Venugopal · 2022

Answering complex questions over textual resources remains a challenge, particularly when dealing with nuanced relationships between multiple entities expressed within natural-language sentences. To this end, curated knowledge bases (KBs) …

TensAIR: Real-Time Training of Neural Networks from Data-streams Open

Mauro Dalle Lucca Tosi, Vinu E. Venugopal, Martin Theobald · 2022

Online learning (OL) from data streams is an emerging area of research that encompasses numerous challenges from stream processing, machine learning, and networking. Stream-processing platforms, such as Apache Kafka and Flink, have basic e…

Targeting a light-weight and multi-channel approach for distributed stream processing Open

Vinu E. Venugopal, Martin Theobald, Damien Tassetti, Samira Chaychi, Amal Tawakuli · 2022

Difficulty-level modeling of ontology-based factual questions Open

Vinu E. Venugopal, P. Sreenivasa Kumar · 2020

Semantics-based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty-level of these system-generated questions is helpful to effective…

AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing Open

Vinu E. Venugopal, Martin Theobald, Samira Chaychi, Amal Tawakuli · 2020

Distributed Stream Processing Systems (DSPSs) are among the currently most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics. The ma…

Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback Open

Yan Wu, Jinchuan Chen, Plarent Haxhidauti, Vinu E. Venugopal, Martin Theobald · 2020

Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both of these large-scale KBs achieve v…

Benchmarking Synchronous and Asynchronous Stream Processing Systems Open

Vinu E. Venugopal, Martin Theobald · 2020

No abstract available.

AIR: A Light-Weight Yet High-Performance Dataflow Engine based on\n Asynchronous Iterative Routing Open

Vinu E. Venugopal, Martin Theobald, Samira Chaychi, Amal Tawakuli · 2020

Distributed Stream Processing Systems (DSPSs) are among the currently most\nemerging topics in data management, with applications ranging from real-time\nevent monitoring to processing complex dataflow programs and big data\nanalytics. The…

Stress testing a deep learning algorithm for normal/abnormal classification of chest x-rays on a spectrum-biased abnormal-weighted dataset Open

Vinu E. Venugopal, Ramón Liriano González, Luis Martı́-Bonmatı́, Ángel Alberich‐Bayarri, Mitali Barnwal , et al. · 2019

Poster: ECR 2019 / C-2743 / Stress testing a deep learning algorithm for normal/abnormal classification of chest x-rays on a spectrum-biased abnormal-weighted dataset by: V. Venugopal 1, R. L. Gonzalez2, L. Marti-Bonmati2, A. Alber…

Towards radiologist-level malignancy detection on chest CT scans: a comparative study of the performance of convolutional neural networks and four thoracic radiologists Open

Vinu E. Venugopal, Anurag Vaidya, A. K. Ahuja, Yashovardhan Singh, Kiran Vaidhya , et al. · 2019

Poster: ECR 2019 / C-2065 / Towards radiologist-level malignancy detection on chest CT scans: a comparative study of the performance of convolutional neural networks and four thoracic radiologists by: V. Venugopal 1, A. VAIDYA2, A. A…

Difficulty-level Modeling of Ontology-based Factual Questions Open

Vinu E. Venugopal, P. Sreenivasa Kumar · 2017

Semantics based knowledge representations such as ontologies are found to be\nvery useful in automatically generating meaningful factual questions.\nDetermining the difficulty level of these system generated questions is helpful\nto effect…

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