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ORCIDMahesan Niranjan
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- affiliation: University of Southampton, UK
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2020 – today
- 2024
[j66]Ioan Ieremie
, Rob M. Ewing, Mahesan Niranjan:
Protein language models meet reduced amino acid alphabets. Bioinform. 40(2) (2024)- [j65]Keqiang Fan, Xiaohao Cai, Mahesan Niranjan:
Discrepancy-based diffusion models for lesion detection in brain MRI. Comput. Biol. Medicine 181: 109079 (2024) - [j64]Andrei C. Rusu, Katayoun Farrahi, Mahesan Niranjan:
EpiCURB: Learning to Derive Epidemic Control Policies. IEEE Pervasive Comput. 23(1): 57-62 (2024) - [c77]Yihong Wu, Yuwen Heng, Mahesan Niranjan, Hansung Kim:
SliceFormer: Deep Dense Depth Estimation from a Single Indoor Omnidirectional Image Using a Slice-Based Transformer. ICEIC 2024: 1-4 - [c76]Junyu Mao, Stuart E. Middleton, Mahesan Niranjan:
Do Prompt Positions Really Matter? NAACL-HLT (Findings) 2024: 4102-4130 - [i15]Keqiang Fan, Xiaohao Cai, Mahesan Niranjan:
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging. CoRR abs/2404.02656 (2024) - [i14]Keqiang Fan, Xiaohao Cai, Mahesan Niranjan:
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI. CoRR abs/2405.04974 (2024) - 2023
- [j63]Alex Thomas, Mahesan Niranjan, Julian Legg:
Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning. Sensors 23(23): 9455 (2023) - [c75]Yihong Wu, Yuwen Heng, Mahesan Niranjan, Hansung Kim:
Depth Estimation for a Single Omnidirectional Image with Reversed-Gradient Warming-up Thresholds Discriminator. ICASSP 2023: 1-5 - [c74]Keqiang Fan, Xiaohao Cai, Mahesan Niranjan:
IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer. ICONIP (6) 2023: 57-71 - [i13]Junyu Mao, Stuart E. Middleton, Mahesan Niranjan:
Prompt position really matters in few-shot and zero-shot NLU tasks. CoRR abs/2305.14493 (2023) - [i12]Jiahui Liu, Xiaohao Cai, Mahesan Niranjan:
GO-LDA: Generalised Optimal Linear Discriminant Analysis. CoRR abs/2305.14568 (2023) - [i11]Jiahui Liu, Keqiang Fan, Xiaohao Cai, Mahesan Niranjan:
Few-shot Learning for Inference in Medical Imaging with Subspace Feature Representations. CoRR abs/2306.11152 (2023) - [i10]Keqiang Fan, Xiaohao Cai, Mahesan Niranjan:
IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer. CoRR abs/2308.05633 (2023) - [i9]Yihong Wu, Yuwen Heng, Mahesan Niranjan, Hansung Kim:
Depth Insight - Contribution of Different Features to Indoor Single-image Depth Estimation. CoRR abs/2311.10042 (2023) - [i8]Jiahui Liu, Xiaohao Cai, Mahesan Niranjan:
Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes. CoRR abs/2311.14733 (2023) - 2022
- [j62]Ioan Ieremie, Rob M. Ewing, Mahesan Niranjan:
TransformerGO: predicting protein-protein interactions by modelling the attention between sets of gene ontology terms. Bioinform. 38(8): 2269-2277 (2022) - [j61]Rubaiyat Mohammad Khondaker, Stephen Gow, Samantha Kanza, Jeremy G. Frey, Mahesan Niranjan:
Robustness under parameter and problem domain alterations of Bayesian optimization methods for chemical reactions. J. Cheminformatics 14(1): 59 (2022) - [j60]Omar Shetta, Mahesan Niranjan, Srinandan Dasmahapatra:
Convex Multi-View Clustering Via Robust Low Rank Approximation With Application to Multi-Omic Data. IEEE ACM Trans. Comput. Biol. Bioinform. 19(6): 3340-3352 (2022) - [c73]Mona Alawadh, Yihong Wu, Yuwen Heng, Luca Remaggi, Mahesan Niranjan, Hansung Kim:
Room Acoustic Properties Estimation from a Single 360° Photo. EUSIPCO 2022: 857-861 - [c72]Shengyu Lu, Sasan Mahmoodi, Mahesan Niranjan:
Robust 3D rotation invariant local binary pattern for volumetric texture classification. ICPR 2022: 578-584 - [c71]Junwen Wang, Xin Du, Katayoun Farrahi, Mahesan Niranjan:
Deep Cascade Learning for Optimal Medical Image Feature Representation. MLHC 2022: 54-78 - [c70]Andrei C. Rusu, Katayoun Farrahi, Mahesan Niranjan:
Flattening the Curve Through Reinforcement Learning Driven Test and Trace Policies. PervasiveHealth 2022: 174-206 - 2021
- [j59]Xin Du, Katayoun Farrahi, Mahesan Niranjan:
Information Bottleneck Theory Based Exploration of Cascade Learning. Entropy 23(10): 1360 (2021) - [j58]Samantha Kanza, Colin Leonard Bird, Mahesan Niranjan, William McNeill, Jeremy Graham Frey:
The AI for Scientific Discovery Network+. Patterns 2(1): 100162 (2021) - [c69]Yihong Wu, Yuwen Heng, Mahesan Niranjan, Hansung Kim:
Depth Estimation from a Single Omnidirectional Image using Domain Adaptation. CVMP 2021: 3:1-3:9 - [c68]Premananth Gowtham, Mahesan Niranjan, Kaneswaran Anantharajah:
Automated gastrointestinal abnormalities detection from endoscopic images. ICIIS 2021: 191-196 - 2020
- [j57]Tristan Millington, Mahesan Niranjan:
Partial correlation financial networks. Appl. Netw. Sci. 5(1): 11 (2020) - [j56]Francisco Belchí Guillamón, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan:
A Numerical Measure of the Instability of Mapper-Type Algorithms. J. Mach. Learn. Res. 21: 202:1-202:45 (2020) - [j55]Donya Rahmani, Mahesan Niranjan, Damien Fay, Akiko Takeda, Jacek Brodzki:
Estimation of Gaussian mixture models via tensor moments with application to online learning. Pattern Recognit. Lett. 131: 285-292 (2020) - [i7]Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon S. Hare:
Understanding and Enhancing Mixed Sample Data Augmentation. CoRR abs/2002.12047 (2020) - [i6]Manuel Nunes, Enrico H. Gerding, Frank McGroarty, Mahesan Niranjan:
Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box. CoRR abs/2005.02217 (2020) - [i5]Tristan Millington, Mahesan Niranjan:
Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation. CoRR abs/2005.03963 (2020)
2010 – 2019
- 2019
- [j54]Gregory M. Parkes, Mahesan Niranjan:
Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-'omics analysis. BMC Bioinform. 20(1): 536:1-536:13 (2019) - [j53]Manuel Nunes, Enrico H. Gerding, Frank McGroarty, Mahesan Niranjan:
A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting. Expert Syst. Appl. 119: 362-375 (2019) - [c67]Xin Du, Katayoun Farrahi, Mahesan Niranjan:
Transfer learning across human activities using a cascade neural network architecture. UbiComp 2019: 35-44 - [c66]Guillermo Romero Moreno, Mahesan Niranjan, Adam Prügel-Bennett:
Saliency Map on Cnns for Protein Secondary Structure Prediction. ICASSP 2019: 1249-1253 - [c65]Bahman Asadi, Mahesan Niranjan:
Representation-dimensionality Trade-off in Biological Sequence-based Inference. IJCNN 2019: 1-7 - [c64]Pratheeba Jeyananthan, Mahesan Niranjan:
Classification and Regression Analysis of Lung Tumors from Multi-level Gene Expression Data. IJCNN 2019: 1-8 - [c63]Tristan Millington, Mahesan Niranjan:
Quantifying Influence in Financial Markets via Partial Correlation Network Inference. ISPA 2019: 306-311 - [c62]Luis Montesdeoca, Steven Squires, Mahesan Niranjan:
Variational Autoencoder for Non-Negative Matrix Factorization with Exogenous Inputs Applied to Financial Data Modelling. ISPA 2019: 312-317 - [c61]Luis Montesdeoca, Mahesan Niranjan:
On Comparing the Influences of Exogenous Information on Bitcoin Prices and Stock Index Values. MARBLE 2019: 93-100 - [i4]Ethan Harris, Mahesan Niranjan, Jonathon S. Hare:
A Biologically Inspired Visual Working Memory for Deep Networks. CoRR abs/1901.03665 (2019) - [i3]Steven Squires, Adam Prügel-Bennett, Mahesan Niranjan:
Minimum description length as an objective function for non-negative matrix factorization. CoRR abs/1902.01632 (2019) - [i2]Francisco Belchí Guillamón, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan:
A numerical measure of the instability of Mapper-type algorithms. CoRR abs/1906.01507 (2019) - [i1]Steven Squires, Adam Prügel-Bennett, Mahesan Niranjan:
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation. CoRR abs/1906.05912 (2019) - 2018
- [j52]Mariam Pirashvili, Lee Steinberg, Francisco Belchí Guillamón, Mahesan Niranjan, Jeremy G. Frey, Jacek Brodzki:
Improved understanding of aqueous solubility modeling through topological data analysis. J. Cheminformatics 10(1): 54:1-54:14 (2018) - [j51]Enrique S. Marquez, Jonathon S. Hare, Mahesan Niranjan:
Deep Cascade Learning. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5475-5485 (2018) - 2017
- [j50]Steven Squires, Adam Prügel-Bennett, Mahesan Niranjan:
Rank Selection in Nonnegative Matrix Factorization using Minimum Description Length. Neural Comput. 29(8): 2164-2176 (2017) - [c60]Steven Squires, Rob M. Ewing, Adam Prügel-Bennett, Mahesan Niranjan:
A Method of Integrating Spatial Proteomics and Protein-Protein Interaction Network Data. ICONIP (5) 2017: 782-790 - [c59]Tristan Millington, Mahesan Niranjan:
Robust Portfolio Risk Minimization Using the Graphical Lasso. ICONIP (2) 2017: 863-872 - [c58]Steven Squires, Luis Montesdeoca, Adam Prügel-Bennett, Mahesan Niranjan:
Non-Negative Matrix Factorization with Exogenous Inputs for Modeling Financial Data. ICONIP (2) 2017: 873-881 - 2016
- [j49]Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael J. Bennett, Mahesan Niranjan:
Rotation invariant texture descriptors based on Gaussian Markov random fields for classification. Pattern Recognit. Lett. 69: 15-21 (2016) - [c57]Luis Montesdeoca, Mahesan Niranjan:
Extending the feature set of a data-driven artificial neural network model of pricing financial options. SSCI 2016: 1-6 - 2015
- [j48]Haifen Chen, Jing Guo, Shital K. Mishra, Paul Robson, Mahesan Niranjan, Jie Zheng:
Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development. Bioinform. 31(7): 1060-1066 (2015) - [j47]Yawwani Gunawardana, Shuhei Fujiwara, Akiko Takeda, Jeongmin Woo, Christopher H. Woelk, Mahesan Niranjan:
Outlier detection at the transcriptome-proteome interface. Bioinform. 31(15): 2530-2536 (2015) - [j46]Thilini Nadungodage, Ruvan Weerasinghe, Mahesan Niranjan:
Speaker Adaptation Applied to Sinhala Speech Recognition. Int. J. Comput. Linguistics Appl. 6(1): 117-129 (2015) - [j45]Jonathon S. Hare, Sina Samangooei, Mahesan Niranjan, Nicholas Gibbins:
Detection of social events in streams of social multimedia. Int. J. Multim. Inf. Retr. 4(4): 289-302 (2015) - [j44]Abdullah Alrajeh, Mahesan Niranjan:
Scalable Reordering Models for SMT based on Multiclass SVM. Prague Bull. Math. Linguistics 103: 65-84 (2015) - [j43]Viraj Welgama, Ruvan Weerasinghe, Mahesan Niranjan:
Defining the Gold Standard Definitions for the Morphology of Sinhala Words. Res. Comput. Sci. 90: 163-171 (2015) - [c56]Randil Pushpananda, Ruvan Weerasinghe, Mahesan Niranjan:
Statistical Machine Translation from and into Morphologically Rich and Low Resourced Languages. CICLing (1) 2015: 545-556 - [c55]Avi Rosenfeld, David G. Graham, Rifat A. Hamoudi, Rommel Butawan, Victor Eneh, Saif Khan, Haroon Miah, Mahesan Niranjan, Laurence B. Lovat:
MIAT: A novel attribute selection approach to better predict upper gastrointestinal cancer. DSAA 2015: 1-7 - 2014
- [j42]Kaya Kuru, Mahesan Niranjan, Yusuf Tunca, Erhan Osvank, Tayyaba Azim:
Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. Artif. Intell. Medicine 62(2): 105-118 (2014) - [j41]Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael J. Bennett, Mahesan Niranjan:
Gaussian Markov random field based improved texture descriptor for image segmentation. Image Vis. Comput. 32(11): 884-895 (2014) - [c54]Randil Pushpananda, Ruvan Weerasinghe, Mahesan Niranjan:
Sinhala-Tamil Machine Translation: Towards better Translation Quality. ALTA 2014: 129-133 - [c53]Abdullah Alrajeh, Mahesan Niranjan:
Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression. EMNLP 2014: 1758-1763 - [c52]Abdullah Alrajeh, Akiko Takeda, Mahesan Niranjan:
Memory-efficient large-scale linear support vector machine. ICMV 2014: 944527 - [c51]Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael J. Bennett, Mahesan Niranjan:
An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation. ICPRAM 2014: 139-146 - [c50]Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael J. Bennett, Mahesan Niranjan:
Quantitative Analysis of Pulmonary Emphysema using Isotropic Gaussian Markov Random Fields. VISAPP (3) 2014: 44-53 - [c49]Tayyaba Azim, Mahesan Niranjan:
Computational Models of Object Recognition - Goal, Role and Success. VISAPP (1) 2014: 179-186 - [c48]Tayyaba Azim, Mahesan Niranjan:
Texture Classification with Fisher Kernel Extracted from the Continuous Models of RBM. VISAPP (2) 2014: 684-690 - [c47]Abdullah Alrajeh, Mahesan Niranjan:
Bayesian Reordering Model with Feature Selection. WMT@ACL 2014: 477-485 - 2013
- [j40]Yawwani Gunawardana, Mahesan Niranjan:
Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes. Bioinform. 29(23): 3060-3066 (2013) - [j39]Akiko Takeda, Mahesan Niranjan, Jun-ya Gotoh, Yoshinobu Kawahara:
Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios. Comput. Manag. Sci. 10(1): 21-49 (2013) - [j38]Taihai Chen, Evangelos B. Mazomenos, Koushik Maharatna, Srinandan Dasmahapatra, Mahesan Niranjan:
Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems. IEEE J. Emerg. Sel. Topics Circuits Syst. 3(1): 75-85 (2013) - [j37]Ali Hassan, Robert I. Damper, Mahesan Niranjan:
On Acoustic Emotion Recognition: Compensating for Covariate Shift. IEEE Trans. Speech Audio Process. 21(7): 1458-1468 (2013) - [c46]Tim Matthews, Mark S. Nixon, Mahesan Niranjan:
Enriching Texture Analysis with Semantic Data. CVPR 2013: 1248-1255 - [c45]Piyushkumar A. Mundra, Jie Zheng, Mahesan Niranjan, Roy E. Welsch, Jagath C. Rajapakse:
Inferring Time-Delayed Gene Regulatory Networks Using Cross-Correlation and Sparse Regression. ISBRA 2013: 64-75 - [c44]Sina Samangooei, Jonathon S. Hare, David Dupplaw, Mahesan Niranjan, Nicholas Gibbins, Paul H. Lewis, Jamie Davies, Neha Jain, John Preston:
Social Event Detection Via Sparse Multi-modal Feature Selection and Incremental Density Based Clustering. MediaEval 2013 - [c43]Tayyaba Azim, Mahesan Niranjan:
Inducing discrimination in biologically inspired models of visual scene recognition. MLSP 2013: 1-6 - [c42]B. Mayurathan, U. A. J. Pinidiyaarachchi, Mahesan Niranjan:
Compact codebook design for visual scene recognition by Sequential Input Space Carving. MLSP 2013: 1-6 - 2012
- [j36]Wei Liu, Mahesan Niranjan:
Gaussian process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile. Bioinform. 28(3): 366-372 (2012) - [j35]Xin Liu, Mahesan Niranjan:
State and parameter estimation of the heat shock response system using Kalman and particle filters. Bioinform. 28(11): 1501-1507 (2012) - [j34]Ke Yuan, Mark A. Girolami, Mahesan Niranjan:
Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations. Neural Comput. 24(6): 1462-1486 (2012) - [j33]Amirthalingam Ramanan, Mahesan Niranjan:
A Review of Codebook Models in Patch-Based Visual Object Recognition. J. Signal Process. Syst. 68(3): 333-352 (2012) - [c41]Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael J. Bennett, Mahesan Niranjan:
Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters. BMVC 2012: 1-11 - [c40]Kaya Kuru, Mahesan Niranjan, Yusuf Tunca:
Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology. ICMLA (2) 2012: 164-169 - [c39]Taihai Chen, Evangelos B. Mazomenos, Koushik Maharatna, Srinandan Dasmahapatra, Mahesan Niranjan:
On the Trade-Off of Accuracy and Computational Complexity for Classifying Normal and Abnormal ECG in Remote CVD Monitoring Systems. SiPS 2012: 37-42 - 2011
- [j32]Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan:
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. J. Mach. Learn. Res. 12: 1-30 (2011) - [j31]Andrew Zammit-Mangion, Ke Yuan, Visakan Kadirkamanathan, Mahesan Niranjan, Guido Sanguinetti:
Online Variational Inference for State-Space Models with Point-Process Observations. Neural Comput. 23(8): 1967-1999 (2011) - 2010
- [j30]Salih Tuna, Mahesan Niranjan:
Reducing the algorithmic variability in transcriptome-based inference. Bioinform. 26(9): 1185-1191 (2010) - [j29]Ivan Markovsky, Mahesan Niranjan:
Approximate low-rank factorization with structured factors. Comput. Stat. Data Anal. 54(12): 3411-3420 (2010) - [j28]Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan:
The application of structured learning in natural language processing. Mach. Transl. 24(2): 71-85 (2010) - [j27]Ke Yuan, Mahesan Niranjan:
Estimating a State-Space Model from Point Process Observations: A Note on Convergence. Neural Comput. 22(8): 1993-2001 (2010) - [j26]Weichao Xu, Y. S. Hung, Mahesan Niranjan, Minfen Shen:
Asymptotic mean and variance of Gini correlation for bivariate normal samples. IEEE Trans. Signal Process. 58(2): 522-534 (2010) - [j25]Salih Tuna, Mahesan Niranjan:
Inference from Low Precision Transcriptome Data Representation. J. Signal Process. Syst. 58(3): 267-279 (2010) - [c38]Yizhao Ni, Mahesan Niranjan:
Exploiting Long-Range Dependencies in Protein beta-Sheet Secondary Structure Prediction. PRIB 2010: 349-357
2000 – 2009
- 2009
- [j24]Daniela Wieser, Mahesan Niranjan:
Remote Homology Detection Using a Kernel Method that Combines Sequence and Secondary-Structure Similarity Scores. Silico Biol. 9(3): 89-103 (2009) - [c37]Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan:
Handling phrase reorderings for machine translation. ACL/IJCNLP (2) 2009: 241-244 - [c36]C. Q. Chang, Y. S. Hung, Mahesan Niranjan:
Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle. ICASSP 2009: 1769-1772 - [c35]Bassam Farran, Amirthalingam Ramanan, Mahesan Niranjan:
Sequential Hierarchical Pattern Clustering. PRIB 2009: 79-88 - [c34]Salih Tuna, Mahesan Niranjan:
Cross-Platform Analysis with Binarized Gene Expression Data. PRIB 2009: 439-449 - [e2]Visakan Kadirkamanathan, Guido Sanguinetti, Mark A. Girolami, Mahesan Niranjan, Josselin Noirel:
Pattern Recognition in Bioinformatics, 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Lecture Notes in Computer Science 5780, Springer 2009, ISBN 978-3-642-04030-6 [contents] - 2008
- [j23]Renata da Silva Camargo, Mahesan Niranjan:
Mining Protein Database using Machine Learning Techniques. J. Integr. Bioinform. 5(2) (2008) - [j22]Sujimarn Suwannaroj, Mahesan Niranjan:
Enhancing Automatic Construction of Gene Subnetworks by Integrating Multiple Sources of Information. J. Signal Process. Syst. 50(3): 331-340 (2008) - [c33]Yang Zhang, Hongyu Li, Mahesan Niranjan, Peter I. Rockett:
Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering. EuroGP 2008: 325-336 - 2007
- [j21]Anastasia Samsonova, Mahesan Niranjan, Steven Russell, Alvis Brazma:
Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster. PLoS Comput. Biol. 3(7) (2007) - 2006
- [c32]Hongyu Li, Mahesan Niranjan:
Outlier Detection in Benchmark Classification Tasks. ICASSP (5) 2006: 557-560 - [c31]Anjali Bharatkumar Samani, Joab R. Winkler, Mahesan Niranjan:
Automatic Face Recognition Using Stereo Images. ICASSP (5) 2006: 913-916 - 2005
- [e1]Joab R. Winkler, Mahesan Niranjan, Neil D. Lawrence:
Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures. Lecture Notes in Computer Science 3635, Springer 2005, ISBN 3-540-29073-7 [contents] - 2004
- [j20]Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier:
Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinform. 20(4): 518-526 (2004) - 2003
- [j19]Si Wu, Danmei Chen, Mahesan Niranjan, Shun-ichi Amari:
Sequential Bayesian Decoding with a Population of Neurons. Neural Comput. 15(5): 993-1012 (2003) - [c30]Mahesan Suwannaroj, Mahesan Niranjan:
Subspaces of text discrimination with application to biological literature. NNSP 2003: 3-12 - [c29]Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier:
Bayesian processing of microarray images. NNSP 2003: 71-80 - 2002
- [j18]Klaus Reinhard, Mahesan Niranjan:
Diphone subspace mixture trajectory models for HMM complementation. Speech Commun. 38(3-4): 237-265 (2002) - 2001
- [j17]Gaafar M. K. Saleh, Mahesan Niranjan:
Speech enhancement using a Bayesian evidence approach. Comput. Speech Lang. 15(2): 101-125 (2001) - [c28]Konstantinos Koumpis, Steve Renals, Mahesan Niranjan:
Extractive summarization of voicemail using lexical and prosodic feature subset selection. INTERSPEECH 2001: 2377-2380 - 2000
- [j16]João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee:
Hierarchical Bayesian Models for Regularization in Sequential Learning. Neural Comput. 12(4): 933-953 (2000) - [j15]João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee, Arnaud Doucet:
Sequential Monte Carlo Methods to Train Neural Network Models. Neural Comput. 12(4): 955-993 (2000) - [j14]David G. Melvin, Mahesan Niranjan, Richard W. Prager, Andrew K. Trull, Vikki F. Hughes:
Neuro-computing versus linear statistical techniques applied to liver transplant monitoring: a comparative study. IEEE Trans. Biomed. Eng. 47(8): 1036-1043 (2000) - [j13]João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee:
Dynamic Learning with the EM Algorithm for Neural Networks. J. VLSI Signal Process. 26(1-2): 119-131 (2000) - [c27]Klaus Reinhard, Mahesan Niranjan:
Matched filter design for diphone subspace models. ICASSP 2000: 3430-3433 - [c26]Nathan Smith, Mahesan Niranjan:
Data-dependent kernels in svm classification of speech patterns. INTERSPEECH 2000: 297-300
1990 – 1999
- 1999
- [j12]Klaus Reinhard, Mahesan Niranjan:
Parametric subspace modeling of speech transitions. Speech Commun. 27(1): 19-42 (1999) - [c25]Klaus Reinhard, Mahesan Niranjan:
Diphone multi-trajectory subspace models. ICASSP 1999: 1001-1004 - [c24]João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee:
Hybrid sequential Monte Carlo/Kalman methods to train neural networks in non-stationary environments. ICASSP 1999: 1057-1060 - [c23]Mahesan Niranjan:
Sequential Bayesian computation of logistic regression models. ICASSP 1999: 1065-1068 - [c22]Klaus Reinhard, Mahesan Niranjan:
Diphone subspace models for phone-based HMM complementation. EUROSPEECH 1999 - [c21]Gavin Smith, João F. G. de Freitas, Tony Robinson, Mahesan Niranjan:
Speech Modelling Using Subspace and EM Techniques. NIPS 1999: 796-802 - 1998
- [j11]David R. Lovell, Christopher R. Dance, Mahesan Niranjan, Richard W. Prager, Kevin J. Dalton, R. Derom:
Feature selection using expected attainable discrimination. Pattern Recognit. Lett. 19(5-6): 393-402 (1998) - [c20]Martin J. J. Scott, Mahesan Niranjan, Richard W. Prager:
Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. BMVC 1998: 1-10 - [c19]Gaafar M. K. Saleh, Mahesan Niranjan:
Speech enhancement in a Bayesian framework. ICASSP 1998: 389-392 - [c18]Jaco Vermaak, Mahesan Niranjan:
Markov chain Monte Carlo methods for speech enhancement. ICASSP 1998: 1013-1016 - [c17]Klaus Reinhard, Mahesan Niranjan:
Parametric subspace modelling of speech transitions. ICASSP 1998: 1105-1108 - [c16]João F. G. de Freitas, Sue E. Johnson, Mahesan Niranjan, Andrew H. Gee:
Global optimisation of neural network models via sequential sampling-importance resampling. ICSLP 1998 - [c15]João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee:
Global Optimisation of Neural Network Models via Sequential Sampling. NIPS 1998: 410-416 - 1997
- [j10]Sean B. Holden, Mahesan Niranjan:
Average-Case Learning Curves for Radial Basis Function Networks. Neural Comput. 9(2): 441-460 (1997) - [j9]Jenq-Neng Hwang, Sun-Yuan Kung, Mahesan Niranjan, José C. Príncipe:
The past, present, and future of neural networks for signal processing. IEEE Signal Process. Mag. 14(6): 28-48 (1997) - [j8]Mahesan Niranjan:
Examples in Medical Applications. IEEE Signal Process. Mag. 14(6): 48 (1997) - [c14]João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee:
Regularisation in Sequential Learning Algorithms. NIPS 1997: 458-464 - 1996
- [j7]Christophe Molina, Mahesan Niranjan:
Pruning with Replacement on Limited Resource Allocating Networks by F-Projections. Neural Comput. 8(4): 855-868 (1996) - [c13]Mahesan Niranjan:
Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches. NIPS 1996: 960-966 - 1995
- [j6]Sean B. Holden, Mahesan Niranjan:
On the practical applicability of VC dimension bounds. Neural Comput. 7(6): 1265-1288 (1995) - [j5]Sean B. Holden, Mahesan Niranjan:
On the statistical physics of radial basis function networks. Neural Process. Lett. 2(4): 16-19 (1995) - [c12]T. L. Burrows, Mahesan Niranjan:
Vocal tract modelling with recurrent neural networks. ICASSP 1995: 3315-3318 - [c11]Gaafar M. K. Saleh, Mahesan Niranjan, William J. Fitzgerald:
The use of maximum a posteriori parameters in linear prediction of speech. EUROSPEECH 1995: 263-268 - 1994
- [j4]Lizhong Wu, Mahesan Niranjan, Frank Fallside:
Fully vector-quantized neural network-based code-excited nonlinear predictive speech coding. IEEE Trans. Speech Audio Process. 2(4): 482-489 (1994) - [c10]Mahesan Niranjan, Ingemar J. Cox, Sunita L. Hingorani:
Recursive tracking of formants in speech signals. ICASSP (2) 1994: 205-208 - [c9]Lizhong Wu, Mahesan Niranjan:
On the design of nonlinear speech predictors with recurrent nets. ICASSP (2) 1994: 529-532 - 1993
- [j3]Visakan Kadirkamanathan, Mahesan Niranjan:
A Function Estimation Approach to Sequential Learning with Neural Networks. Neural Comput. 5(6): 954-975 (1993) - 1992
- [c8]Visakan Kadirkamanathan, Mahesan Niranjan, Frank Fallside:
Models of dynamic complexity for time-series prediction (neural networks). ICASSP 1992: 269-272 - 1991
- [c7]Mahesan Niranjan, Visakan Kadirkamanathan:
A nonlinear model for time series prediction and signal interpolation. ICASSP 1991: 1713-1716 - [c6]Visakan Kadirkamanathan, Mahesan Niranjan:
Nonlinear adaptive filtering in nonstationary environments. ICASSP 1991: 2177-2180 - 1990
- [j2]Sreeram V. B. Aiyer, Mahesan Niranjan, Frank Fallside:
A theoretical investigation into the performance of the Hopfield model. IEEE Trans. Neural Networks 1(2): 204-215 (1990) - [c5]Mahesan Niranjan, Frank Fallside:
Speech Feature Extraction Using Neural Networks. EURASIP Workshop 1990: 197-204 - [c4]Mahesan Niranjan:
CELP coding with adaptive output-error model identification. ICASSP 1990: 225-228 - [c3]Visakan Kadirkamanathan, Mahesan Niranjan, Frank Fallside:
Sequential Adaptation of Radial Basis Function Networks. NIPS 1990: 721-727
1980 – 1989
- 1989
- [c2]Mahesan Niranjan, Frank Fallside:
Temporal decomposition: a framework for enhanced speech recognition. ICASSP 1989: 655-658 - 1988
- [j1]Tony Dodd, Leonard Bolc, Paul Taylor, Judith Wusterman, Blay Whitby, Carlton McDonald, J. Garwood, Jim Sholicar, Wendy J. Milne, Michael Pengelly, Tony Priest, Antony Galton, Mahesan Niranjan, D. Rainton, J. R. Thomas, Steve Hedges, Barbara Gorayska:
Book reviews. Artif. Intell. Rev. 2(4): 253-309 (1988) - 1987
- [c1]Mahesan Niranjan, Frank Fallside:
On modelling the dynamics of speech patterns. ECST 1987: 1071-1074
Coauthor Index
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