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2020 – today
- 2024
[j40]Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan
:
Discovering symbolic laws directly from trajectories with hamiltonian graph neural networks. Mach. Learn. Sci. Technol. 5(3): 35049 (2024)- [c30]Nimesh Agrawal, Anuj Kumar Sirohi, Sandeep Kumar, Jayadeva:
No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. AAAI 2024: 10775-10783 - [c29]Shruti Pandey, Jayadeva, Smruti R. Sarangi:
HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation. ASPDAC 2024: 497-502 - [c28]Suresh Bishnoi, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan:
BroGNet: Momentum-Conserving Graph Neural Stochastic Differential Equation for Learning Brownian Dynamics. ICLR 2024 - 2023
- [j39]Debanjali Sarkar, Taimoor Khan, Jayadeva, Ahmed A. Kishk:
Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas. IEEE Access 11: 19645-19656 (2023) - [c27]Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan:
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems. ICLR 2023 - [i25]Suresh Bishnoi, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan:
Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics. CoRR abs/2306.11435 (2023) - [i24]Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan:
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks. CoRR abs/2307.05299 (2023) - [i23]Mohd Zaki, Jayadeva, Mausam, N. M. Anoop Krishnan:
MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models. CoRR abs/2308.09115 (2023) - [i22]Nimesh Agrawal, Anuj Kumar Sirohi, Jayadeva, Sandeep Kumar:
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. CoRR abs/2312.10080 (2023) - 2022
- [j38]Shruti Sharma, Santanu Chaudhury, Jayadeva:
Block Sparse Variational Bayes Regression Using Matrix Variate Distributions With Application to SSVEP Detection. IEEE Trans. Neural Networks Learn. Syst. 33(1): 351-365 (2022) - [i21]Shruti Pandey, Jayadeva, Smruti R. Sarangi:
A Novel Meta-predictor based Algorithm for Testing VLSI Circuits. CoRR abs/2207.11312 (2022) - [i20]Suresh Bishnoi, Skyler Badge, Jayadeva, N. M. Anoop Krishnan:
Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors. CoRR abs/2210.10507 (2022) - [i19]Mohd Zaki, Siddhant Sharma, Sunil Kumar Gurjar, Raju Goyal, Jayadeva, N. M. Anoop Krishnan:
Cementron: Machine Learning the Constituent Phases in Cement Clinker from Optical Images. CoRR abs/2211.03223 (2022) - 2021
- [j37]Prashant Gupta, Aashi Jindal, Jayadeva, Debarka Sengupta:
ComBI: Compressed Binary Search Tree for Approximate k-NN Searches in Hamming Space. Big Data Res. 25: 100223 (2021) - [j36]Skyler Badge, Sumit Soman, Suresh Chandra, Jayadeva:
Kernel optimization using conformal maps for the minimal complexity machine. Eng. Appl. Artif. Intell. 106: 104493 (2021) - [j35]Prashant Gupta, Aashi Jindal, Jayadeva, Debarka Sengupta:
Linear time identification of local and global outliers. Neurocomputing 429: 141-150 (2021) - [j34]Mayank Sharma, Sumit Soman, Jayadeva:
Minimal Complexity Machines Under Weight Quantization. IEEE Trans. Computers 70(8): 1189-1198 (2021) - [c26]Aashi Jindal, Prashant Gupta, Debarka Sengupta, Jayadeva:
Enhash: A Fast Streaming Algorithm For Concept Drift Detection. ESANN 2021 - [i18]Kartikeya Badola, Sameer Ambekar, Himanshu Pant, Sumit Soman, Anuradha Sural, Rajiv Narang, Suresh Chandra, Jayadeva:
Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images. CoRR abs/2102.07975 (2021) - 2020
- [j33]Jayadeva, Sumit Soman, Himanshu Pant, Mayank Sharma:
QMCM: Minimizing Vapnik's bound on the VC dimension. Neurocomputing 399: 352-360 (2020) - [j32]Sumit Soman, Jayadeva, Rajat Thakur, Mayank Sharma, Suresh Chandra:
Sparsity in function and derivative approximation via the empirical feature space. Inf. Sci. 512: 402-415 (2020) - [j31]Himanshu Pant, Sumit Soman, Jayadeva, Amit Bhaya:
Neurodynamical classifiers with low model complexity. Neural Networks 132: 405-415 (2020) - [i17]Aashi Jindal, Prashant Gupta, Debarka Sengupta, Jayadeva:
Enhash: A Fast Streaming Algorithm For Concept Drift Detection. CoRR abs/2011.03729 (2020) - [i16]Himanshu Pant, Jayadeva, Sumit Soman:
Complexity Controlled Generative Adversarial Networks. CoRR abs/2011.10223 (2020)
2010 – 2019
- 2019
- [j30]Jayadeva, Himanshu Pant, Mayank Sharma, Sumit Soman:
Twin Neural Networks for the classification of large unbalanced datasets. Neurocomputing 343: 34-49 (2019) - [c25]Sumit Soman, Jayadeva:
Learning from Low Training Data using Classifiers with Derivative Constraints. COMAD/CODS 2019: 86-93 - [i15]Mayank Sharma, Aayush Yadav, Sumit Soman, Jayadeva:
Effect of Various Regularizers on Model Complexities of Neural Networks in Presence of Input Noise. CoRR abs/1901.11458 (2019) - [i14]Mayank Sharma, Suraj Tripathi, Abhimanyu Dubey, Jayadeva, Sai Guruju, Nihal Goalla:
Smaller Models, Better Generalization. CoRR abs/1908.11250 (2019) - [i13]Prashant Gupta, Aashi Jindal, Jayadeva, Debarka Sengupta:
Guided Random Forest and its application to data approximation. CoRR abs/1909.00659 (2019) - 2018
- [j29]Jayadeva, Sumit Soman, Soumya Saxena:
EigenSample: A non-iterative technique for adding samples to small datasets. Appl. Soft Comput. 70: 1064-1077 (2018) - [j28]Jayadeva, Mayank Sharma, Sumit Soman, Himanshu Pant:
Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature Space - Submitted to the Special Issue on "Off the Mainstream: Advances in Neural Networks and Machine Learning for Pattern Recognition". Neural Process. Lett. 48(2): 881-913 (2018) - [j27]Udit Kumar, Sumit Soman, Jayadeva:
Eigen-MM: EigenAnt Modified Mtsls1 for local search. Swarm Evol. Comput. 43: 166-183 (2018) - [c24]Shruti Sharma, Santanu Chaudhury, Jayadeva:
Variational Bayes Block Sparse Modeling with Correlated Entries. ICPR 2018: 1313-1318 - [c23]Shruti Sharma, Santanu Chaudhury, Jayadeva, Snigdha Bhagat:
Sparse Signal Recovery for Multiple Measurement Vectors with Temporally Correlated Entries: A Bayesian Perspective. ICVGIP 2018: 66:1-66:8 - [c22]Shruti Sharma, Santanu Chaudhury, Jayadeva:
Temporal Modeling of EEG Signals using Block Sparse Variational Bayes Framework. ICVGIP 2018: 71:1-71:7 - [c21]Himanshu Pant, Sumit Soman, Jayadeva, Mayank Sharma:
Twin Neural Networks for Efficient EEG Signal Classification. IJCNN 2018: 1-7 - [c20]Mayank Sharma, Sumit Soman, Jayadeva, Himanshu Pant:
Non-Mercer Large Scale Multiclass Least Squares Minimal Complexity Machines. IJCNN 2018: 1-8 - [c19]Shruti Sharma, Santanu Chaudhury, Jayadeva:
Some Comments on Variational Bayes Block Sparse Modeling with Correlated Entries. RRPR 2018: 110-117 - [i12]Mayank Sharma, Jayadeva, Sumit Soman:
Radius-margin bounds for deep neural networks. CoRR abs/1811.01171 (2018) - 2017
- [b1]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Twin Support Vector Machines - Models, Extensions and Applications. Studies in Computational Intelligence 659, Springer 2017, ISBN 978-3-319-46184-7, pp. 1-206 - [j26]Pawas Gupta, Sanjit S. Batra, Jayadeva:
Sparse short-term time series forecasting models via minimum model complexity. Neurocomputing 243: 1-11 (2017) - [j25]Mayank Sharma, Jayadeva, Sumit Soman, Himanshu Pant:
Large-Scale Minimal Complexity Machines Using Explicit Feature Maps. IEEE Trans. Syst. Man Cybern. Syst. 47(10): 2653-2662 (2017) - [i11]Himanshu Pant, Jayadeva, Sumit Soman, Mayank Sharma:
Scalable Twin Neural Networks for Classification of Unbalanced Data. CoRR abs/1705.00347 (2017) - [i10]Jayadeva, Himanshu Pant, Mayank Sharma, Abhimanyu Dubey, Sumit Soman, Sai Guruju, Nihal Goalla:
Learning Neural Network Classifiers with Low Model Complexity. CoRR abs/1707.09933 (2017) - [i9]Disha Shrivastava, Santanu Chaudhury, Jayadeva:
A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark. CoRR abs/1708.05840 (2017) - 2016
- [j24]Jayadeva, Suresh Chandra, Sanjit S. Batra, Siddarth Sabharwal:
Learning a hyperplane regressor through a tight bound on the VC dimension. Neurocomputing 171: 1610-1616 (2016) - [j23]Udit Kumar, Sumit Soman, Jayadeva:
Benchmarking NLopt and state-of-the-art algorithms for continuous global optimization via IACOR. Swarm Evol. Comput. 27: 116-131 (2016) - [c18]Udit Kumar, Sumit Soman, Jayadeva:
EigenAnt assisted IACOℝ for continuous global optimization. SMC 2016: 3440-3445 - [c17]Sumit Soman, Jayadeva, Sridhar P. Arjunan, Dinesh Kant Kumar:
Improved sEMG signal classification using the Twin SVM. SMC 2016: 4507-4512 - [i8]Abhimanyu Dubey, Jayadeva, Sumeet Agarwal:
Examining Representational Similarity in ConvNets and the Primate Visual Cortex. CoRR abs/1609.03529 (2016) - 2015
- [j22]Sumit Soman, Jayadeva:
High performance EEG signal classification using classifiability and the Twin SVM. Appl. Soft Comput. 30: 305-318 (2015) - [j21]Jayadeva:
Learning a hyperplane classifier by minimizing an exact bound on the VC dimension. Neurocomputing 149: 683-689 (2015) - [c16]Udit Kumar, Jayadeva, Sumit Soman:
Enhancing IACOR Local Search by Mtsls1-BFGS for Continuous Global Optimization. GECCO 2015: 33-40 - [c15]Udit Kumar, Jayadeva, Sumit Soman:
Enhancing Incremental Ant Colony Algorithm for Continuous Global Optimization. GECCO (Companion) 2015: 1417-1418 - [c14]Jayadeva, Sumit Soman, Amit Bhaya:
The MC-ELM: Learning an ELM-like network with minimum VC dimension. IJCNN 2015: 1-7 - [i7]Jayadeva, Sanjit Singh Batra, Siddarth Sabharwal:
Learning a Fuzzy Hyperplane Fat Margin Classifier with Minimum VC dimension. CoRR abs/1501.02432 (2015) - [i6]Jayadeva, Sumit Soman, Amit Bhaya:
A Neurodynamical System for finding a Minimal VC Dimension Classifier. CoRR abs/1503.03148 (2015) - [i5]Udit Kumar, Sumit Soman, Jayadeva:
Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACOℝ. CoRR abs/1503.03175 (2015) - [i4]Phool Preet, Sanjit Singh Batra, Jayadeva:
Feature Selection for classification of hyperspectral data by minimizing a tight bound on the VC dimension. CoRR abs/1509.08112 (2015) - 2014
- [c13]Eugenius Kaszkurewicz, Amit Bhaya, Jayadeva, João Marcos Meirelles da Silva:
The Coupled EigenAnt algorithm for shortest path problems. IEEE Congress on Evolutionary Computation 2014: 1729-1735 - [i3]Jayadeva:
Learning a hyperplane classifier by minimizing an exact bound on the VC dimension. CoRR abs/1408.2803 (2014) - [i2]Jayadeva, Suresh Chandra, Siddarth Sabharwal, Sanjit S. Batra:
Learning a hyperplane regressor by minimizing an exact bound on the VC dimension. CoRR abs/1410.4573 (2014) - [i1]Jayadeva, Sanjit S. Batra, Siddarth Sabharwal:
Feature Selection through Minimization of the VC dimension. CoRR abs/1410.7372 (2014) - 2013
- [j20]Jayadeva, Sameena Shah, Amit Bhaya, Ravi Kothari, Suresh Chandra:
Ants find the shortest path: a mathematical proof. Swarm Intell. 7(1): 43-62 (2013) - [c12]Audrey Tan, Dinesh K. Kumar, Sridhar P. Arjunan, Jayadeva:
Computation and study of the low-frequency oscillation of surface electromyogram recorded in biceps during isometric upper limb contraction. EMBC 2013: 2128-2131 - [c11]Vasiliki N. Ikonomidou, Jayadeva, Robert W. Newcomb, Mona E. Zaghloul:
Biomedical sensor properties of flexible PolyVinyliDene flouride. PETRA 2013: 51:1-51:2 - 2012
- [j19]Sachindra Joshi, Jayadeva, Ganesh Ramakrishnan, Suresh Chandra:
Using Sequential Unconstrained Minimization Techniques to simplify SVM solvers. Neurocomputing 77(1): 253-260 (2012) - 2011
- [j18]Mittul Singh, Jivitej Chadha, Puneet Ahuja, Jayadeva, Suresh Chandra:
Reduced twin support vector regression. Neurocomputing 74(9): 1474-1477 (2011) - [c10]Jayadeva, Sameena Shah, Ravi Kothari, Suresh Chandra:
Debugging ants: How ants find the shortest route. ICICS 2011: 1-5 - [c9]Sameena Shah, Jayadeva, Ravi Kothari, Suresh Chandra:
M-Unit EigenAnt: An Ant Algorithm to Find the M Best Solutions. AAAI 2011: 732-737 - 2010
- [j17]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Learning the optimal kernel for Fisher discriminant analysis via second order cone programming. Eur. J. Oper. Res. 203(3): 692-697 (2010) - [j16]Sameena Shah, Ravi Kothari, Jayadeva, Suresh Chandra:
Trail formation in ants. A generalized Polya urn process. Swarm Intell. 4(2): 145-171 (2010) - [j15]Ganesh R. Naik, Dinesh Kant Kumar, Jayadeva:
Twin SVM for gesture classification using the surface electromyogram. IEEE Trans. Inf. Technol. Biomed. 14(2): 301-308 (2010)
2000 – 2009
- 2009
- [j14]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Knowledge based proximal support vector machines. Eur. J. Oper. Res. 195(3): 914-923 (2009) - [j13]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Syst. Appl. 36(1): 132-138 (2009) - [j12]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Optimal kernel selection in twin support vector machines. Optim. Lett. 3(1): 77-88 (2009) - [c8]Jayadeva, Sameena Shah, Suresh Chandra:
Kernel Optimization Using a Generalized Eigenvalue Approach. PReMI 2009: 32-37 - [c7]Jayadeva, Sameena Shah, Suresh Chandra:
Zero Norm Least Squares Proximal SVR. PReMI 2009: 38-43 - 2008
- [j11]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives. Inf. Sci. 178(17): 3402-3414 (2008) - [j10]V. Girish, Jayadeva, Saeid Nooshabadi:
Design methodology for configurable analog to digital conversion using support vector machines. Microelectron. J. 39(5): 822-827 (2008) - [j9]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Linear potential proximal support vector machines for pattern classification. Optim. Methods Softw. 23(4): 491-500 (2008) - [c6]Sameena Shah, Ravi Kothari, Jayadeva, Suresh Chandra:
Mathematical Modeling and Convergence Analysis of Trail Formation. AAAI 2008: 170-175 - 2007
- [j8]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Twin Support Vector Machines for Pattern Classification. IEEE Trans. Pattern Anal. Mach. Intell. 29(5): 905-910 (2007) - [j7]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fuzzy multi-category proximal support vector classification via generalized eigenvalues. Soft Comput. 11(7): 679-685 (2007) - [j6]Alok Kanti Deb, Jayadeva, Madan Gopal, Suresh Chandra:
SVM-Based Tree-Type Neural Networks as a Critic in Adaptive Critic Designs for Control. IEEE Trans. Neural Networks 18(4): 1016-1030 (2007) - 2006
- [c5]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting. IJCNN 2006: 593-598 - [c4]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Regularized Least Squares Twin SVR for the Simultaneous Learning of a Function and its Derivative. IJCNN 2006: 1192-1197 - 2005
- [j5]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fuzzy linear proximal support vector machines for multi-category data classification. Neurocomputing 67: 426-435 (2005) - [c3]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fuzzy Proximal Support Vector Classification Via Generalized Eigenvalues. PReMI 2005: 360-363 - 2004
- [j4]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fast and robust learning through fuzzy linear proximal support vector machines. Neurocomputing 61: 401-411 (2004) - [j3]Jayadeva, Syed Atiqur Rahman:
A neural network with O(N) neurons for ranking N numbers in O(1/N) time. IEEE Trans. Circuits Syst. I Regul. Pap. 51-I(10): 2044-2051 (2004) - 2003
- [c2]Shantanu Chakrabartty, Gert Cauwenberghs, Jayadeva:
Sparse Probability Regression by Label Partitioning. COLT 2003: 231-242
1990 – 1999
- 1999
- [c1]Jayadeva:
Sequential Chaotic Annealing and its Application to Multilayer Channel Routing. VLSI Design 1999: 570-573 - 1994
- [j2]Jayadeva, Basabi Bhaumik:
A neural network for the Steiner minimal tree problem. Biol. Cybern. 70(5): 485-494 (1994) - 1992
- [j1]Jayadeva, Basabi Bhaumik:
Optimization with neural networks: a recipe for improving convergence and solution quality. Biol. Cybern. 67(5): 445-449 (1992)
Coauthor Index
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last updated on 2024-12-02 22:32 CET by the dblp team
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