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ORCIDVineet Padmanabhan
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2020 – today
- 2025
[j25]Prasad Bhavana, Vineet Padmanabhan:
Temporal Matrix Factorization: A polynomial approach to latent factor estimation. Pattern Recognit. 157: 110905 (2025)- 2023
- [j24]Akshay Badola
, Cherian Roy, Vineet Padmanabhan, Rajendra Prasad Lal:
Decomposing the deep: finding class-specific filters in deep CNNs. Neural Comput. Appl. 35(18): 13583-13596 (2023) - [c49]Akshay Badola, Vineet Padmanabhan, Rajendra Prasad Lal:
Re-examining Class Selectivity in Deep Convolutional Networks. MIWAI 2023: 101-111 - [c48]Nageswar Rao Kota, Vineet Padmanabhan, Wilson Naik Bhukya:
Content Based Network Representational Learning for Movie Recommendation (CNMovieRec). MIWAI 2023: 112-123 - [i9]Prasad Bhavana, Vineet Padmanabhan:
GPU accelerated matrix factorization of large scale data using block based approach. CoRR abs/2304.13724 (2023) - 2022
- [j23]Sowmini Devi Veeramachaneni, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
A hinge-loss based codebook transfer for cross-domain recommendation with non-overlapping data. Inf. Syst. 107: 102002 (2022) - [j22]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
Inductive conformal recommender system. Knowl. Based Syst. 250: 109108 (2022) - [c47]Brahmani Nutakki, Akshay Badola, Vineet Padmanabhan:
Systematic Monotonicity and Consistency for Adversarial Natural Language Inference. AI 2022: 353-366 - [i8]Sowmini Devi Veeramachaneni, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data. CoRR abs/2203.13995 (2022) - 2021
- [j21]Prasad Bhavana, Vineet Padmanabhan:
Matrix factorization of large scale data using multistage matrix factorization. Appl. Intell. 51(6): 4016-4028 (2021) - [j20]Gopal N. Rai, G. R. Gangadharan, Vineet Padmanabhan, Rajkumar Buyya:
Web Service Interaction Modeling and Verification Using Recursive Composition Algebra. IEEE Trans. Serv. Comput. 14(1): 300-314 (2021) - [c46]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Haris Aziz, Vikas Kumar:
Committee Selection using Attribute Approvals. AAMAS 2021: 683-691 - [i7]Sowmini Devi Veeramachaneni, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data. CoRR abs/2108.01473 (2021) - [i6]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
Inductive Conformal Recommender System. CoRR abs/2109.08949 (2021) - [i5]Akshay Badola, Cherian Roy, Vineet Padmanabhan, Rajendra Prasad Lal:
Identifying Class Specific Filters with L1 Norm Frequency Histograms in Deep CNNs. CoRR abs/2112.07719 (2021)
2010 – 2019
- 2019
- [j19]Ayangleima Laishram, Vineet Padmanabhan:
Discovery of user-item subgroups via genetic algorithm for effective prediction of ratings in collaborative filtering. Appl. Intell. 49(11): 3990-4006 (2019) - [j18]Sowmini Devi Veeramachaneni, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
A Maximum Margin Matrix Factorization based Transfer Learning Approach for Cross-Domain Recommendation. Appl. Soft Comput. 85 (2019) - [j17]Vikas Kumar, Arun K. Pujari, Vineet Padmanabhan, Venkateswara Rao Kagita:
Group preserving label embedding for multi-label classification. Pattern Recognit. 90: 23-34 (2019) - [j16]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
Skyline recommendation with uncertain preferences. Pattern Recognit. Lett. 125: 446-452 (2019) - [c45]Ayangleima Laishram, Vineet Padmanabhan:
Enhanced Collaborative Filtering Through User-Item Subgroups, Particle Swarm Optimization and Fuzzy C-Means. Canadian AI 2019: 94-106 - [c44]Prasad Bhavana, Vineet Padmanabhan:
BMF: Matrix Factorization of Large Scale Data Using Block Based Approach. PRICAI (2) 2019: 431-436 - [i4]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar:
Committee Selection with Attribute Level Preferences. CoRR abs/1901.10064 (2019) - [i3]Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan:
Block based Singular Value Decomposition approach to matrix factorization for recommender systems. CoRR abs/1907.07410 (2019) - 2018
- [j15]Vikas Kumar, Arun K. Pujari, Vineet Padmanabhan, Sandeep Kumar Sahu, Venkateswara Rao Kagita:
Multi-label classification using hierarchical embedding. Expert Syst. Appl. 91: 263-269 (2018) - [j14]Tadiparthi V. R. Himabindu, Vineet Padmanabhan, Arun K. Pujari:
Conformal matrix factorization based recommender system. Inf. Sci. 467: 685-707 (2018) - [c43]Kollapalli Ramesh Babu, Vineet Padmanabhan:
BDI Based Performance Enhancement in DNSSEC. ICDCIT 2018: 121-126 - [i2]Thomas Cherian, Akshay Badola, Vineet Padmanabhan:
Multi-cell LSTM Based Neural Language Model. CoRR abs/1811.06477 (2018) - [i1]Vikas Kumar, Arun K. Pujari, Vineet Padmanabhan, Venkateswara Rao Kagita:
Group Preserving Label Embedding for Multi-Label Classification. CoRR abs/1812.09910 (2018) - 2017
- [j13]Arun K. Pujari, Vineet Padmanabhan, Venkateswara Rao Kagita:
Bounds on skyline probability for databases with uncertain preferences. Int. J. Approx. Reason. 80: 199-213 (2017) - [j12]Vikas Kumar, Arun K. Pujari, Sandeep Kumar Sahu, Venkateswara Rao Kagita, Vineet Padmanabhan:
Collaborative filtering using multiple binary maximum margin matrix factorizations. Inf. Sci. 380: 1-11 (2017) - [j11]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Sandeep Kumar Sahu, Vikas Kumar:
Conformal recommender system. Inf. Sci. 405: 157-174 (2017) - [j10]Vikas Kumar, Arun K. Pujari, Sandeep Kumar Sahu, Venkateswara Rao Kagita, Vineet Padmanabhan:
Proximal maximum margin matrix factorization for collaborative filtering. Pattern Recognit. Lett. 86: 62-67 (2017) - [c42]Sowmini Devi V., Vineet Padmanabhan, Arun K. Pujari:
A Matrix Factorization & Clustering Based Approach for Transfer Learning. PReMI 2017: 77-83 - [c41]Vidyadhar Rao, Rosni K. Vasu, Vineet Padmanabhan:
Divide and Transfer: Understanding Latent Factors for Recommendation Tasks. RecSysKTL 2017: 1-8 - 2016
- [c40]Ayangleima Laishram, Satya Prakash Sahu, Vineet Padmanabhan, Siba Kumar Udgata:
Collaborative Filtering, Matrix Factorization and Population Based Search: The Nexus Unveiled. ICONIP (3) 2016: 352-361 - [c39]Tadiparthi V. R. Himabindu, Vineet Padmanabhan, Arun K. Pujari, Abdul Sattar:
Prediction with Confidence in Item Based Collaborative Filtering. PRICAI 2016: 125-138 - [c38]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Vikas Kumar, Sandeep Kumar Sahu:
Threshold-Based Direct Computation of Skyline Objects for Database with Uncertain Preferences. PRICAI 2016: 193-205 - 2015
- [j9]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan:
Virtual user approach for group recommender systems using precedence relations. Inf. Sci. 294: 15-30 (2015) - [j8]Arun K. Pujari, Venkateswara Rao Kagita, Anubhuti Garg, Vineet Padmanabhan:
Efficient computation for probabilistic skyline over uncertain preferences. Inf. Sci. 324: 146-162 (2015) - [c37]Gopal N. Rai, G. R. Gangadharan, Vineet Padmanabhan:
Algebraic Modeling and Verification of Web Service Composition. ANT/SEIT 2015: 675-679 - [c36]Arun K. Pujari, Venkateswara Rao Kagita, Anubhuti Garg, Vineet Padmanabhan:
Bi-directional Search for Skyline Probability. CALDAM 2015: 250-261 - [c35]Nishma Laitonjam, Vineet Padmanabhan, Arun K. Pujari, Rajendra Prasad Lal:
Topic Modelling for Songs. ICIT 2015: 130-135 - [c34]Sandeep Kumar Sahu, Arun K. Pujari, Venkateswara Rao Kagita, Vikas Kumar, Vineet Padmanabhan:
GP-SVM: Tree Structured Multiclass SVM with Greedy Partitioning. ICIT 2015: 142-147 - [c33]Abinash Pujahari, Vineet Padmanabhan:
Group Recommender Systems: Combining User-User and Item-Item Collaborative Filtering Techniques. ICIT 2015: 148-152 - [c32]Venkateswara Rao Kagita, Krishna Charan Meka, Vineet Padmanabhan:
A Novel Social-Choice Strategy for Group Modeling in Recommender Systems. ICIT 2015: 153-158 - [c31]Vineet Padmanabhan, Pranay Goud, Arun K. Pujari, Harshit Sethy:
Learning in Real-Time Strategy Games. ICIT 2015: 165-170 - [c30]Tadiparthi V. R. Himabindu, Vineet Padmanabhan, Venkateswara Rao Kagita, Arun K. Pujari:
Recommender system algorithms: A comparative analysis based on monotonicity. ICAPR 2015: 1-6 - [c29]Sandeep Kumar Sahu, Arun K. Pujari, Vikas Kumar, Venkateswara Rao Kagita, Vineet Padmanabhan:
Greedy partitioning based tree structured multiclass SVM for Odia OCR. NCVPRIPG 2015: 1-4 - 2014
- [j7]Upendra Kumar Singh, Vineet Padmanabhan, Arun Agarwal:
Dynamic classification of ballistic missiles using neural networks and hidden Markov models. Appl. Soft Comput. 19: 280-289 (2014) - [c28]Abinash Pujahari, Vineet Padmanabhan:
An Approach to Content Based Recommender Systems Using Decision List Based Classification with k-DNF Rule Set. ICIT 2014: 260-263 - [c27]Sowmini Devi V., Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan:
Collaborative filtering by PSO-based MMMF. SMC 2014: 569-574 - 2013
- [c26]Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan:
Group Recommender Systems: A Virtual User Approach Based on Precedence Mining. Australasian Conference on Artificial Intelligence 2013: 434-440 - [c25]Abhishek Anand, Vineet Padmanabhan:
Time Series Qlet: Invariant approach for data mining. IC3 2013: 24-29 - [c24]Upendra Kumar Singh, Vineet Padmanabhan:
Training and classification of ballistic missiles using Hidden Markov model. IC3 2013: 301-306 - [c23]Vineet Padmanabhan, Prabhu Kiran, Abdul Sattar:
Group Recommender Systems - Some Experimental Results. ICAART (2) 2013: 370-376 - [c22]Upendra Kumar Singh, Vineet Padmanabhan, Arun Agarwal:
A novel method for training and classification of ballistic and quasi-ballistic missiles in real-time. IJCNN 2013: 1-8 - [c21]Upendra Kumar Singh, Vineet Padmanabhan:
Training by ART-2 and Classification of Ballistic Missiles Using Hidden Markov Model. PReMI 2013: 108-114 - [c20]Venkateswara Rao Kagita, Vineet Padmanabhan, Arun K. Pujari:
Precedence Mining in Group Recommender Systems. PReMI 2013: 701-707 - 2012
- [j6]Kaifu Zhang, Theodoros Evgeniou, Vineet Padmanabhan, Emile Richard:
Content Contributor Management and Network Effects in a UGC Environment. Mark. Sci. 31(3): 433-447 (2012) - 2011
- [j5]Pushan Dutt, Vineet Padmanabhan:
Crisis and Consumption Smoothing. Mark. Sci. 30(3): 491-512 (2011) - [c19]Vineet Padmanabhan, Abdul Sattar, Guido Governatori, Kollapalli Ramesh Babu:
Incorporating Temporal Planning Within a BDI Architecture. IICAI 2011: 1618-1636 - [c18]Vineet Padmanabhan, Siva Krishna Seemala, Wilson Naik Bhukya:
A Rule Based Approach to Group Recommender Systems. MIWAI 2011: 26-37 - [c17]Vineet Padmanabhan, Guido Governatori, Abdul Sattar:
Fibred BDI Logics: Completeness Preservation in the Presence of Interaction Axioms. MIWAI 2011: 63-74 - [c16]Kollapalli Ramesh Babu, Vineet Padmanabhan, Wilson Naik Bhukya:
Reasoning about DNSSEC. MIWAI 2011: 75-86
2000 – 2009
- 2009
- [j4]Guido Governatori, Vineet Padmanabhan, Antonino Rotolo, Abdul Sattar:
A defeasible logic for modelling policy-based intentions and motivational attitudes. Log. J. IGPL 17(3): 227-265 (2009) - 2008
- [j3]Rakesh Niraj, Vineet Padmanabhan, P. B. Seetharaman:
Research Note - A Cross-Category Model of Households' Incidence and Quantity Decisions. Mark. Sci. 27(2): 225-235 (2008) - [c15]Vineet Padmanabhan, Guido Governatori, Subhasis Thakur:
Knowledge Assessment: A Modal Logic Approach. PRIMA 2008: 315-322 - 2007
- [c14]Subhasis Thakur, Guido Governatori, Vineet Padmanabhan, Jenny Eriksson Lundström:
Dialogue Games in Defeasible Logic. Australian Conference on Artificial Intelligence 2007: 497-506 - [c13]Jenny Eriksson Lundström, Guido Governatori, Subhasis Thakur, Vineet Padmanabhan:
An Asymmetric Protocol for Argumentation Games in Defeasible Logic. PRIMA 2007: 219-231 - 2006
- [c12]Ruopeng Lu, Shazia Wasim Sadiq, Vineet Padmanabhan, Guido Governatori:
Using a temporal constraint network for business process execution. ADC 2006: 157-166 - [c11]Vineet Padmanabhan, Guido Governatori, Shazia Wasim Sadiq, Robert M. Colomb, Antonino Rotolo:
Process modelling: the deontic way. APCCM 2006: 75-84 - [c10]Guido Governatori, Antonino Rotolo, Vineet Padmanabhan:
The cost of social agents. AAMAS 2006: 513-520 - [c9]Vineet Padmanabhan, Guido Governatori:
A Fibred Tableau Calculus for Modal Logics of Agents. DALT 2006: 105-122 - [c8]Guido Governatori, Vineet Padmanabhan, Antonino Rotolo:
Rule-Based Agents in Temporalised Defeasible Logic. PRICAI 2006: 31-40 - [c7]Vineet Padmanabhan, Guido Governatori:
On Constructing Fibred Tableaux for BDI Logics. PRICAI 2006: 150-160 - 2005
- [c6]Kaile Su, Abdul Sattar, Kewen Wang, Xiangyu Luo, Guido Governatori, Vineet Padmanabhan:
Observation-based Model for BDI-Agents. AAAI 2005: 190-195 - 2004
- [j2]Hau L. Lee, Vineet Padmanabhan, Seungjin Whang:
Information Distortion in a Supply Chain: The Bullwhip Effect. Manag. Sci. 50(12-Supplement): 1875-1886 (2004) - [j1]Hau L. Lee, Vineet Padmanabhan, Seungjin Whang:
Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect". Manag. Sci. 50(12-Supplement): 1887-1893 (2004) - 2003
- [c5]Guido Governatori, Vineet Padmanabhan:
A Defeasible Logic of Policy-Based Intention. Australian Conference on Artificial Intelligence 2003: 414-426 - 2002
- [c4]Guido Governatori, Vineet Padmanabhan, Abdul Sattar:
A Defeasible Logic of Policy-Based Intention. Australian Joint Conference on Artificial Intelligence 2002: 723 - [c3]Guido Governatori, Vineet Padmanabhan, Abdul Sattar:
On Fibring Semantics for BDI Logics. JELIA 2002: 198-210 - 2001
- [c2]Vineet Padmanabhan, Guido Governatori, Abdul Sattar:
Actions Made Explicit in BDI. Australian Joint Conference on Artificial Intelligence 2001: 390-401 - 2000
- [c1]Vineet Padmanabhan, Abdul Sattar, Arun K. Pujari, Chinmoy Goswami:
Temporal Reasoning: A Three Way Analysis. TIME 2000: 183-189
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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