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2020 – today
- 2024
[c47]Abisha Thapa Magar
, Stephen E. Fancsali, Vasile Rus, April Murphy, Steven Ritter, Deepak Venugopal:
Learning Representations for Math Strategies using BERT. L@S 2024: 514-518- [i6]Abisha Thapa Magar, Anup Shakya, Somdeb Sarkhel, Deepak Venugopal:
Verifying Relational Explanations: A Probabilistic Approach. CoRR abs/2401.02703 (2024) - [i5]Anup Shakya, Vasile Rus, Deepak Venugopal:
Mastery Guided Non-parametric Clustering to Scale-up Strategy Prediction. CoRR abs/2401.10210 (2024) - 2023
- [c46]Abisha Thapa Magar, Anup Shakya, Somdeb Sarkhel, Deepak Venugopal:
Verifying Relational Explanations: A Probabilistic Approach. IEEE Big Data 2023: 108-115 - [c45]Anup Shakya, Vasile Rus, Deepak Venugopal:
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data. EDM 2023 - [c44]Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel, Deepak Venugopal:
On the Verification of Embeddings with Hybrid Markov Logic. ICDM 2023: 1301-1306 - [i4]Anup Shakya, Vasile Rus, Deepak Venugopal:
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data. CoRR abs/2308.03892 (2023) - [i3]Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel, Deepak Venugopal:
On the verification of Embeddings using Hybrid Markov Logic. CoRR abs/2312.08287 (2023) - 2022
- [c43]Monika Shah, Somdeb Sarkhel, Deepak Venugopal:
Evaluating Captioning Models using Markov Logic Networks. IEEE Big Data 2022: 127-134 - [c42]William Britton, Somdeb Sarkhel, Deepak Venugopal:
Question Modifiers in Visual Question Answering. LREC 2022: 1472-1479 - 2021
- [j6]Sambriddhi Mainali, Max H. Garzon, Deepak Venugopal, Kalidas Jana, Ching-Chi Yang, Nirman Kumar, Dale Bowman, Lih-Yuan Deng:
An Information-theoretic approach to dimensionality reduction in data science. Int. J. Data Sci. Anal. 12(3): 185-203 (2021) - [c41]Mohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal:
Contrastive Learning in Neural Tensor Networks using Asymmetric Examples. IEEE BigData 2021: 28-39 - [c40]Khan Mohammad Al Farabi, Somdeb Sarkhel, Sanorita Dey, Deepak Venugopal:
Interpretable Explanations for Probabilistic Inference in Markov Logic. IEEE BigData 2021: 1256-1264 - [c39]Leigh M. Harrell-Williams, Christian Mueller, Stephen Fancsali, Steven Ritter, Xiaofei Zhang, Deepak Venugopal:
The Nature of Achievement Goal Motivation Profiles: Exploring Situational Motivation in An Algebra-Focused Intelligent Tutoring System. EDM (Workshops) 2021 - [c38]Vasile Rus, Stephen E. Fancsali, Philip I. Pavlik Jr., Deepak Venugopal, Arthur C. Graesser, Steven Ritter, Dale Bowman, The L. D. I. Team:
The Learner Data Institute - Conceptualization: A Progress Report. EDM (Workshops) 2021 - [c37]Anup Shakya, Vasile Rus, Deepak Venugopal:
Student Strategy Prediction using a Neuro-Symbolic Approach. EDM 2021 - [c36]Deepak Venugopal, Vasile Rus, Anup Shakya:
Neuro-Symbolic Models: A Scalable, Explainable Framework for Strategy Discovery from Big Edu-Data. EDM (Workshops) 2021 - 2020
- [j5]Ahmed M. Mahfouz, Abdullah Abuhussein, Deepak Venugopal, Sajjan G. Shiva:
Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset. Future Internet 12(11): 180 (2020) - [c35]Mohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal:
Augmenting Deep Learning with Relational Knowledge from Markov Logic Networks. IEEE BigData 2020: 54-63 - [c34]Saikat Das, Deepak Venugopal, Sajjan G. Shiva, Frederick T. Sheldon:
Empirical Evaluation of the Ensemble Framework for Feature Selection in DDoS Attack. CSCloud/EdgeCom 2020: 56-61 - [c33]Anik Khan, Kishor Datta Gupta, Deepak Venugopal, Nirman Kumar:
CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space. IJCNN 2020: 1-8 - [c32]Saikat Das, Namita Agarwal, Deepak Venugopal, Frederick T. Sheldon, Sajjan G. Shiva:
Taxonomy and Survey of Interpretable Machine Learning Method. SSCI 2020: 670-677 - [i2]Anik Khan, Kishor Datta Gupta, Deepak Venugopal, Nirman Kumar:
CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space. CoRR abs/2007.02248 (2020)
2010 – 2019
- 2019
- [j4]Naveen Kumar, Deepak Venugopal, Liangfei Qiu, Subodha Kumar:
Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models. J. Manag. Inf. Syst. 36(4): 1313-1346 (2019) - [c31]Mohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal:
On Lifted Inference Using Neural Embeddings. AAAI 2019: 7916-7923 - [c30]Craig Kelly, Somdeb Sarkhel, Deepak Venugopal:
Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models. AISTATS 2019: 2907-2915 - [c29]Ahmed M. Mahfouz, Deepak Venugopal, Sajjan G. Shiva:
Comparative Analysis of ML Classifiers for Network Intrusion Detection. ICICT (2) 2019: 193-207 - [c28]Khan Mohammad Al Farabi, Somdeb Sarkhel, Sanorita Dey, Deepak Venugopal:
Fine-Grained Explanations Using Markov Logic. ECML/PKDD (2) 2019: 614-629 - [c27]Saikat Das, Ahmed M. Mahfouz, Deepak Venugopal, Sajjan G. Shiva:
DDoS Intrusion Detection Through Machine Learning Ensemble. QRS Companion 2019: 471-477 - 2018
- [j3]Naveen Kumar, Deepak Venugopal, Liangfei Qiu, Subodha Kumar:
Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning. J. Manag. Inf. Syst. 35(1): 350-380 (2018) - [c26]Mohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal:
Learning Mixtures of MLNs. AAAI 2018: 6359-6366 - [c25]Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal:
Efficient Weight Learning in High-Dimensional Untied MLNs. AISTATS 2018: 1637-1645 - [c24]Mohammad Maminur Islam, Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal:
Scaling up Inference in MLNs with Spark. IEEE BigData 2018: 118-125 - [c23]Christopher Kent, Deepak Venugopal:
Fine-Grained Crime Prediction in an Urban Neighborhood. ISC2 2018: 1-2 - 2017
- [j2]Deepak Venugopal:
Advances in Inference Methods for Markov Logic Networks. IEEE Intell. Informatics Bull. 18(2): 13-19 (2017) - [c22]Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate:
Efficient Inference for Untied MLNs. IJCAI 2017: 4617-4624 - [c21]Mohammad Maminur Islam, Mohammad Khan Al Farabi, Deepak Venugopal:
Adaptive blocked Gibbs sampling for inference in probabilistic graphical models. IJCNN 2017: 262-269 - 2016
- [c20]Somdeb Sarkhel, Deepak Venugopal, Tuan Anh Pham, Parag Singla, Vibhav Gogate:
Scalable Training of Markov Logic Networks Using Approximate Counting. AAAI 2016: 1067-1073 - [c19]Deepak Venugopal, Vasile Rus:
Joint Inference for Mode Identification in Tutorial Dialogues. COLING 2016: 2000-2011 - [c18]Jing Lu, Deepak Venugopal, Vibhav Gogate, Vincent Ng:
Joint Inference for Event Coreference Resolution. COLING 2016: 3264-3275 - [c17]Deepak Venugopal, Somdeb Sarkhel, Kyle Cherry:
Non-parametric Domain Approximation for Scalable Gibbs Sampling in MLNs. UAI 2016 - 2015
- [c16]Deepak Venugopal, Somdeb Sarkhel, Vibhav Gogate:
Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs. AAAI 2015: 3606-3612 - [c15]Deepak Venugopal:
Scaling-Up Inference in Markov Logic. AAAI 2015: 4259-4260 - 2014
- [c14]Deepak Venugopal, Vibhav Gogate:
Evidence-Based Clustering for Scalable Inference in Markov Logic. StarAI@AAAI 2014 - [c13]Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate:
Lifted MAP Inference for Markov Logic Networks. AISTATS 2014: 859-867 - [c12]Deepak Venugopal, Chen Chen, Vibhav Gogate, Vincent Ng:
Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features. EMNLP 2014: 831-843 - [c11]Deepak Venugopal, Vibhav Gogate:
Scaling-up Importance Sampling for Markov Logic Networks. NIPS 2014: 2978-2986 - [c10]Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate:
An Integer Polynomial Programming Based Framework for Lifted MAP Inference. NIPS 2014: 3302-3310 - [c9]Deepak Venugopal, Vibhav Gogate:
Evidence-Based Clustering for Scalable Inference in Markov Logic. ECML/PKDD (3) 2014: 258-273 - 2013
- [c8]Deepak Venugopal, Vibhav Gogate:
GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models. AAAI 2013: 897-904 - [c7]Deepak Venugopal, Vibhav Gogate:
Dynamic Blocking and Collapsing for Gibbs Sampling. UAI 2013 - [i1]Deepak Venugopal, Vibhav Gogate:
Dynamic Blocking and Collapsing for Gibbs Sampling. CoRR abs/1309.6870 (2013) - 2012
- [c6]Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal:
Advances in Lifted Importance Sampling. AAAI 2012: 1910-1916 - [c5]Deepak Venugopal, Vibhav Gogate:
On Lifting the Gibbs Sampling Algorithm. NIPS 2012: 1664-1672 - [c4]Deepak Venugopal, Vibhav Gogate:
On Lifting the Gibbs Sampling Algorithm. StarAI@UAI 2012
2000 – 2009
- 2008
- [j1]Deepak Venugopal, Guoning Hu:
Efficient signature based malware detection on mobile devices. Mob. Inf. Syst. 4(1): 33-49 (2008) - 2007
- [c3]Guoning Hu, Deepak Venugopal:
A Malware Signature Extraction and Detection Method Applied to Mobile Networks. IPCCC 2007: 19-26 - 2006
- [c2]Deepak Venugopal, Guoning Hu, Nicoleta Roman:
Intelligent virus detection on mobile devices. PST 2006: 65 - [c1]Deepak Venugopal:
An efficient signature representation and matching method for mobile devices. WICON 2006: 16
Coauthor Index
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last updated on 2024-10-07 21:26 CEST by the dblp team
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