M. Niranjan

发表

On-line Q-learning using connectionist systems

Mahesan Niranjan, Gavin Adrian Rummery, M. Niranjan, 1994 .

Generalising the nodes of the error propagation network

M. Niranjan, F. Fallside, A. Robinson, 1989, International 1989 Joint Conference on Neural Networks.

Temporal decomposition: a framework for enhanced speech recognition

Mahesan Niranjan, Frank Fallside, M. Niranjan, 1989, International Conference on Acoustics, Speech, and Signal Processing,.

Global optimisation of neural network models via sequential sampling-importance resampling

Mahesan Niranjan, S. E. Johnson, Joao F. G. De Freitas, 1998, ICSLP.

A numerical measure of the instability of Mapper-type algorithms pdf

Mahesan Niranjan, Jacek Brodzki, Francisco Belchí Guillamón, 2019, J. Mach. Learn. Res..

Neural networks and radial basis functions in classifying static speech patterns

Mahesan Niranjan, Frank Fallside, M. Niranjan, 1990 .

A Function Estimation Approach to Sequential Learning with Neural Networks

Visakan Kadirkamanathan, Mahesan Niranjan, M. Niranjan, 1993, Neural Computation.

Pruning with Replacement on Limited Resource Allocating Networks by F-Projections

Mahesan Niranjan, Christophe Molina, M. Niranjan, 1996, Neural Computation.

Sequential Monte Carlo Methods to Train Neural Network Models

Arnaud Doucet, Andrew H. Gee, Mahesan Niranjan, 2000, Neural Computation.

Nonlinear State Space Estimation With Neural Networks And The Em Algorithm

Andrew H. Gee, Mahesan Niranjan, Joao F. G. De Freitas, 1999 .

Structure learning for natural language processing

Mahesan Niranjan, Sandor Szedmak, Yizhao Ni, 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

Speech Modelling: Models, Parameter Estimation and Its Application to Speech Enhancement

M. Niranjan, A. Robinson, S. Smith, 1999 .

Subspace techniques in speech enhancement

M. Niranjan, G. Smith, T. Robinson, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

Genome-Wide Posttranscriptional Dysregulation by MicroRNAs in Human Asthma as Revealed by Frac-seq

M. Niranjan, P. Howarth, R. Chambers, 2018, The Journal of Immunology.

Genome-Wide Posttranscriptional Dysregulation by MicroRNAs in Human Asthma as Revealed by Frac-seq

M. Niranjan, P. Howarth, R. Chambers, 2017, The Journal of Immunology.

Title Asymptotic mean and variance of gini correlation for bivariatenormal samples

M. Niranjan, Y. Hung, Weichao Xu, 2010 .

Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality

M. Niranjan, Omar Shetta, 2020, Royal Society Open Science.

SUBSPACE MODELS FOR SPEECH TRANSITIONS USING PRINCIPAL

M. Niranjan, Curvesk Reinhard, 1998 .

D5.4: Sequential Algorithms

M. Niranjan, 2014 .

Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning

Neil D. Lawrence, Mahesan Niranjan, Joab R. Winkler, 2004 .

D3.1.1 Regression models of trends in streaming data

Trevor Cohn, Mahesan Niranjan, Sina Samangooei, 2012 .

The Role of Regulated mRNA Stability in Establishing Bicoid Morphogen Gradient in Drosophila Embryonic Development

Mahesan Niranjan, M. Niranjan, Wei Liu, 2011, PloS one.

Speech Processing Using Bayesian Inference

W. J. Fitzgerald, M. Niranjan, M. Niranjan, 1993 .

Ranking the Eeect of Diierent Features on the Classiication of Discrete Valued Data

M. Niranjan, R. Prager, C. Dance, 2007 .

USING UPPER BOUNDS ON ATTAINABLEDISCRIMINATION TO SELECT DISCRETEVALUED

M. Niranjan, R. Prager, C. Dance, 1996 .

Convex Multi-View Clustering Via Robust Low Rank Approximation with Application to Multi-Omic Data.

M. Niranjan, S. Dasmahapatra, Omar Shetta, 2021, IEEE/ACM transactions on computational biology and bioinformatics.

Evaluating a Machine Learning Approach to Sinhala Morphological Analysis

M. Niranjan, R. Weerasinghe, V. Welgama, 2013 .

Supplementary material from "Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality"

Omar Shetta, Mahesan Niranjan, M. Niranjan, 2020 .

Classification and Regression Analysis of Lung Tumors from Multi-level Gene Expression Data

Mahesan Niranjan, Pratheeba Jeyananthan, M. Niranjan, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

CI-SpliceAI—Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites

M. Niranjan, D. Baralle, J. Lord, 2022, PloS one.

On Sequential Bayesian Logistic Regression

M. Niranjan, 1999 .

Sequential Inference and Learning

Nando de Freitas, Mahesan Niranjan, Arnaud Doucet, 1998, NIPS 1998.

Clinical validation of an artificial neural network trained to identify acute allograft rejection in liver transplant recipients

M. Niranjan, A. Trull, G. A. Alexander, 2001, Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society.

TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms

M. Niranjan, R. Ewing, Ioana-Anca Ieremie, 2022, Bioinform..

Robustness under parameter and problem domain alterations of Bayesian optimization methods for chemical reactions

M. Niranjan, J. Frey, Samantha Kanza, 2022, Journal of Cheminformatics.

Markov chain Monte Carlo estimation for the seasonal autoregressive process with application to pitch modelling

Simon J. Godsill, J. Vermaak, M Niranjan, 1998 .

Speech enhancement based on artificial neural networks forhearing-impaired listeners using auditory inspired features

Xin Yang, Mahesan Niranjan, Jessica J. M. Monaghan, 2016 .

Room Acoustic Properties Estimation from a Single 360° Photo

Mona I. Alawadh, M. Niranjan, Hansung Kim, 2022, 2022 30th European Signal Processing Conference (EUSIPCO).

A Review of Codebook Models in Patch-Based Visual Object Recognition

M. Niranjan, A. Ramanan, 2011, Journal of Signal Processing Systems.

Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors.

M. Niranjan, S. Mahmoodi, E. Dennison, 2022, Bone.

Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics

Visakan Kadirkamanathan, Guido Sanguinetti, Mark Girolami, 2009 .

Book Reviews

Antony Galton, Mahesan Niranjan, Barbara Gorayska, 1953, Artificial Intelligence Review.

Design, construction and evaluation of systems to predict risk in obstetrics.

D R Lovell, B Rosario, M Niranjan, 1997, International journal of medical informatics.

Neurocomputing applications in post-operative liver transplant monitoring

V. F. Hughes, M. Niranjan, R. Prager, 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

Minimum description length as an objective function for non-negative matrix factorization pdf

Adam Prügel-Bennett, Mahesan Niranjan, Steven Squires, 2019, ArXiv.

Parameter Estimation in Computational Biology by Approximate Bayesian Computation coupled with Sensitivity Analysis

Xin Liu, Mahesan Niranjan, M. Niranjan, 2017, 1704.09021.

State and parameter estimation of the heat shock response system using Kalman and particle filters

Xin Liu, Mahesan Niranjan, M. Niranjan, 2012, Bioinform..

Estimating a State-Space Model from Point Process Observations: A Note on Convergence

Mahesan Niranjan, Ke Yuan, M. Niranjan, 2010, Neural Computation.

Asymptotic Mean and Variance of Gini Correlation for Bivariate Normal Samples

Minfen Shen, Yeung Sam Hung, Mahesan Niranjan, 2010, IEEE Transactions on Signal Processing.

Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations

Mark A. Girolami, Mahesan Niranjan, Ke Yuan, 2012, Neural Computation.

Sequential Bayesian computation of logistic regression models

Mahesan Niranjan, M. Niranjan, 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

Genomic Analysis of the Function of the Transcription Factor gata3 during Development of the Mammalian Inner Ear

Mahesan Niranjan, Marta Milo, Matthew Holley, 2009, PloS one.

Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development

Jing Guo, Mahesan Niranjan, Shital K. Mishra, 2015, Bioinform..

Stability and similarity in financial networks—How do they change in times of turbulence?

M. Niranjan, Tristan Millington, 2021, Physica A: Statistical Mechanics and its Applications.

Partial correlation financial networks

Mahesan Niranjan, Tristan Millington, M. Niranjan, 2020, Appl. Netw. Sci..

Financial news predicts stock market volatility better than close price

Adam Atkins, Mahesan Niranjan, Enrico H. Gerding, 2018, The Journal of Finance and Data Science.

Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster

Mahesan Niranjan, Alvis Brazma, Steven Russell, 2007, PLoS Comput. Biol..

Outlier detection at the transcriptome-proteome interface

Akiko Takeda, Mahesan Niranjan, Yawwani Gunawardana, 2015, Bioinform..

Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes

Mahesan Niranjan, Yawwani Gunawardana, M. Niranjan, 2013, Bioinform..

Enriching Texture Analysis with Semantic Data

Mark S. Nixon, Mahesan Niranjan, Tim Matthews, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

FMix: Enhancing Mixed Sample Data Augmentation

Jonathon S. Hare, Mahesan Niranjan, Antonia Marcu, 2020 .

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics

Mahesan Niranjan, Kaya Kuru, Tayyaba Azim, 2014, Artif. Intell. Medicine.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

Mahesan Niranjan, Yawwani Gunawardana, Ann Williams, 2017, International journal of molecular sciences.

Automatic Face Recognition Using Stereo Images

A. Samani, J. Winkler, M. Niranjan, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Parcel: Feature Subset Selection in Variable Cost Domains

Mahesan Niranjan, Richard W. Prager, Martin J. J. Scott, 1998 .

Reducing the variability in cDNA microarray image processing by Bayesian inference

Neil D. Lawrence, Mahesan Niranjan, Marta Milo, 2004, Bioinform..

Gaussian process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile

Wei Liu, Mahesan Niranjan, M. Niranjan, 2012, Bioinform..

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

Patrick S. Stumpf, Rosanna C. G. Smith, M. Niranjan, 2020, Communications Biology.

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

Katayoun Farrahi, Patrick S. Stumpf, Xin Du, 2020, Communications biology.

Mapping biology from mouse to man using transfer learning

Patrick S. Stumpf, Rosanna C. G. Smith, Jonathan J. West, 2019, bioRxiv.

Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box pdf

Enrico Gerding, Frank McGroarty, Manuel Nunes, 2020, ArXiv.

Reducing the algorithmic variability in transcriptome-based inference

Mahesan Niranjan, Salih Tuna, M. Niranjan, 2010, Bioinform..

Bayesian Reordering Model with Feature Selection

Mahesan Niranjan, Abdullah Alrajeh, M. Niranjan, 2014, WMT@ACL.

Application of neural networks to postoperative liver transplant monitoring

Mahesan Niranjan, Richard W. Prager, D. G. Melvin, 1997 .

Sequential Adaptation of Radial Basis Function Neural Networks and its Application to Time-series Prediction

Visakan Kadirkamanathan, Mahesan Niranjan, Frank Fallside, 1990, NIPS 1990.

Computational models of machine vision goal, role and success

Mahesan Niranjan, Tayyaba Azim, M. Niranjan, 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

A Biologically Inspired Visual Working Memory for Deep Networks pdf

Jonathon S. Hare, Mahesan Niranjan, Ethan Harris, 2019, ArXiv.

Computational Models of Object Recognition - Goal, Role and Success

Mahesan Niranjan, Tayyaba Azim, M. Niranjan, 2014, VISAPP.

Dynamic Learning with the EM Algorithm for Neural Networks

Andrew H. Gee, Mahesan Niranjan, João F. G. de Freitas, 2000, J. VLSI Signal Process..

The EM algorithm and neural networks for nonlinear state space estimation

Mahesan Niranjan, Jfg de Freitas, Andrew H. Gee, 1998 .

Nonlinear state space learning with EM and neural networks

J.F.G. de Freitas, M. Niranjan, A.H. Gee, 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

Inference from Low Precision Transcriptome Data Representation

Mahesan Niranjan, Salih Tuna, M. Niranjan, 2010, J. Signal Process. Syst..

A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting

Enrico Gerding, Mahesan Niranjan, Frank McGroarty, 2019, Expert Syst. Appl..

Artificial Neural Networks in Fixed Income Markets for Yield Curve Forecasting

Mahesan Niranjan, Enrico H. Gerding, Frank McGroarty, 2018 .

Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation

Mahesan Niranjan, Sándor Szedmák, Craig Saunders, 2011, J. Mach. Learn. Res..

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction

Mahesan Niranjan, Rob M Ewing, Ashley I Heinson, 2019, PloS one.

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios

Akiko Takeda, Yoshinobu Kawahara, Mahesan Niranjan, 2012, Comput. Manag. Sci..

Improved understanding of aqueous solubility modeling through topological data analysis

Mahesan Niranjan, Jeremy G. Frey, Jacek Brodzki, 2018, Journal of Cheminformatics.

The application of Bayesian inference to linear prediction of speech

Wj Fitzgerald, Mahesan Niranjan, Gmk Saleh, 1994 .

Transfer learning across human activities using a cascade neural network architecture

Katayoun Farrahi, Xin Du, Mahesan Niranjan, 2019, UbiComp.

An improved speech production model for voiced speech utilising seasonal AR-AR model and Markov chain Monte Carlo simulation

Simon J. Godsill, J. Vermaak, M Niranjan, 1998 .

MARKOV CHAINMONTE CARLO ESTIMATION FOR THE SEASONAL AUTOREGRESSIVE PROCESS WITH APPLICATION TO PITCHMODELLING

M. Niranjan, J. Vermaak, S. Godsill, 1998 .

A Method of Integrating Spatial Proteomics and Protein-Protein Interaction Network Data

Adam Prügel-Bennett, Mahesan Niranjan, Steven Squires, 2017, ICONIP.

A nonlinear model for time series prediction and signal interpolation

Visakan Kadirkamanathan, Mahesan Niranjan, M. Niranjan, 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

A theoretical investigation into the performance of the Hopfield model

Mahesan Niranjan, Frank Fallside, Sreeram V. B. Aiyer, 1990, IEEE Trans. Neural Networks.

Robust 3D rotation invariant local binary pattern for volumetric texture classification

M. Niranjan, S. Mahmoodi, Shengyu Lu, 2022, 2022 26th International Conference on Pattern Recognition (ICPR).

On Acoustic Emotion Recognition: Compensating for Covariate Shift

Robert I. Damper, Mahesan Niranjan, Ali Hassan, 2012, IEEE Transactions on Audio, Speech, and Language Processing.

Sequential Monte Carlo methods for optimisation of neural network models

Arnaud Doucet, Mahesan Niranjan, Jfg de Freitas, 1998 .

Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation

M. Niranjan, Tristan Millington, 2020, Physica A: Statistical Mechanics and its Applications.

Quantifying Influence in Financial Markets via Partial Correlation Network Inference

Mahesan Niranjan, Tristan Millington, M. Niranjan, 2019, 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA).

Robust Portfolio Risk Minimization Using the Graphical Lasso

Mahesan Niranjan, Tristan Millington, M. Niranjan, 2017, ICONIP.

GO-LDA: Generalised Optimal Linear Discriminant Analysis

M. Niranjan, Xiaohao Cai, Jiahui Liu, 2023, ArXiv.

Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems

Koushik Maharatna, Mahesan Niranjan, Srinandan Dasmahapatra, 2013, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

Sequential Bayesian Decoding with a Population of Neurons

Si Wu, Shun-ichi Amari, Mahesan Niranjan, 2003, Neural Computation.

Genetic Algorithm with Rank Selection optimises robust parameter estimation for systems biology models

M. Niranjan, A. Vallejo, M. Ardern-Jones, 2022, bioRxiv.

The AI for Scientific Discovery Network+

Mahesan Niranjan, Samantha Kanza, Jeremy Graham Frey, 2021, Patterns.

Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression

Mahesan Niranjan, Abdullah Alrajeh, M. Niranjan, 2014, EMNLP.

Diphone subspace mixture trajectory models for HMM complementation

Mahesan Niranjan, Klaus Reinhard, M. Niranjan, 2002, Speech Commun..

Diphone multi-trajectory subspace models

Mahesan Niranjan, Klaus Reinhard, M. Niranjan, 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

Parametric subspace modeling of speech transitions

Mahesan Niranjan, Klaus Reinhard, M. Niranjan, 1999, Speech Commun..

Parametric subspace modelling of speech transitions

Mahesan Niranjan, Klaus Reinhard, M. Niranjan, 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

Deep Cascade Learning

Jonathon S. Hare, Mahesan Niranjan, Enrique S. Marquez, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Saliency Map on Cnns for Protein Secondary Structure Prediction

Adam Prügel-Bennett, Guillermo Romero Moreno, Mahesan Niranjan, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Sequential support vector machines

M. Niranjan, M. Milo, N. de Freitas, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

A Review of Codebook Models in Patch-Based Visual Object Recognition

Amirthalingam Ramanan, Mahesan Niranjan, M. Niranjan, 2012, J. Signal Process. Syst..

A probabilistic model for the extraction of expression levels from oligonucleotide arrays.

M Milo, A Fazeli, M Niranjan, 2003, Biochemical Society transactions.

Approximate low-rank factorization with structured factors

Mahesan Niranjan, Ivan Markovsky, M. Niranjan, 2010, Comput. Stat. Data Anal..

Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study

M. Niranjan, S. Mahmoodi, E. Dennison, 2023, Aging Clinical and Experimental Research.

MIAT: A novel attribute selection approach to better predict upper gastrointestinal cancer

Avi Rosenfeld, Mahesan Niranjan, Laurence B. Lovat, 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Fully vector-quantized neural network-based code-excited nonlinear predictive speech coding

Lizhong Wu, Mahesan Niranjan, Frank Fallside, 1994, IEEE Trans. Speech Audio Process..

Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters

Sasan Mahmoodi, Mahesan Niranjan, Michael J. Bennett, 2012, BMVC.

Gaussian Markov random field based improved texture descriptor for image segmentation

Sasan Mahmoodi, Mahesan Niranjan, Michael J. Bennett, 2014, Image Vis. Comput..

Compact codebook design for visual scene recognition by Sequential Input Space Carving

B. Mayurathan, U. A. J. Pinidiyaarachchi, M. Niranjan, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Inducing discrimination in biologically inspired models of visual scene recognition

Mahesan Niranjan, Tayyaba Azim, M. Niranjan, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

An efficient and speeded-up tree for multi-class classification

A. Ramanan, P. Ranganathan, M. Niranjan, 2012, 2012 IEEE 6th International Conference on Information and Automation for Sustainability.

Speeding up multi-class texture classification by one-pass vocabulary design and decision tree

Amirthalingam Ramanan, Mahesan Niranjan, Paheerathy Ranganathan, 2011, 2011 6th International Conference on Industrial and Information Systems.

Rotation invariant texture descriptors based on Gaussian Markov random fields for classification

Sasan Mahmoodi, Mahesan Niranjan, Michael J. Bennett, 2016, Pattern Recognit. Lett..

Generative and Discriminative Reordering Models for Statistical Machine Translation

Mahesan Niranjan, Abdullah Alrajeh, M. Niranjan, 2016 .

Statistical Machine Translation from and into Morphologically Rich and Low Resourced Languages

Mahesan Niranjan, Ruvan Weerasinghe, Randil Pushpananda, 2015, CICLing.

The application of structured learning in natural language processing

Mahesan Niranjan, Sándor Szedmák, Craig Saunders, 2010, Machine Translation.

Depth Estimation from a Single Omnidirectional Image using Domain Adaptation

M. Niranjan, Hansung Kim, Yihong Wu, 2021, CVMP.

Prompt position really matters in few-shot and zero-shot NLU tasks

M. Niranjan, S. Middleton, Junyu Mao, 2023, ArXiv.

Efficient use of training data for sinhala speech recognition using active learning

Mahesan Niranjan, Ruvan Weerasinghe, Thilini Nadungodage, 2013, 2013 International Conference on Advances in ICT for Emerging Regions (ICTer).

Memory-efficient large-scale linear support vector machine

Akiko Takeda, Mahesan Niranjan, Abdullah Alrajeh, 2015, Other Conferences.

Understanding and Enhancing Mixed Sample Data Augmentation pdf

Jonathon S. Hare, Adam Prügel-Bennett, Mahesan Niranjan, 2020, ArXiv.

Comparison of Four Machine Learning Methods for Predicting PM10 Concentrations in Helsinki, Finland

M. Niranjan, M. Zickus, Alison J. Greig, 2002 .

Information Bottleneck Theory Based Exploration of Cascade Learning

Katayoun Farrahi, Mahesan Niranjan, Xin Du, 2021, Entropy.

Deep Cascade Learning for Optimal Medical Image Feature Representation

M. Niranjan, K. Farrahi, Junwen Wang, 2022, MLHC.

Resource-Allocating Codebook for patch-based face recognition

Amirthalingam Ramanan, Mahesan Niranjan, M. Niranjan, 2009, 2009 International Conference on Industrial and Information Systems (ICIIS).

Comparing solution methodologies for speech enhancement within a Bayesian framework

Simon J. Godsill, J. Vermaak, M Niranjan, 1998 .

Markov chain Monte Carlo methods for speech enhancement

Mahesan Niranjan, Jaco Vermaak, M. Niranjan, 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures

Neil D. Lawrence, Mahesan Niranjan, Joab R. Winkler, 2005, Deterministic and Statistical Methods in Machine Learning.

Online Variational Inference for State-Space Models with Point-Process Observations

Visakan Kadirkamanathan, Guido Sanguinetti, Mahesan Niranjan, 2011, Neural Computation.

Scalable Reordering Models for SMT based on Multiclass SVM

Mahesan Niranjan, Abdullah Alrajeh, M. Niranjan, 2015, Prague Bull. Math. Linguistics.

Multi-class Classification on "VINE" Structure

Mahesan Niranjan, Somjet Suppharangsan, M. Niranjan, 2011, 2011 International Conference on Future Computer Sciences and Application.

Unbalanced Decision Trees for multi-class classification

A. Ramanan, M. Niranjan, S. Suppharangsan, 2007, 2007 International Conference on Industrial and Information Systems.

Depth Estimation for a Single Omnidirectional Image with Reversed-Gradient Warming-up Thresholds Discriminator

M. Niranjan, Hansung Kim, Yihong Wu, 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes

M. Niranjan, Y. Gunawardana, 2013, Bioinform..

Predicting Relative Protein Abundance via Sequence-Based Information

M. Niranjan, R. Ewing, G. M. Parkes, 2021 .

Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-’omics analysis

Mahesan Niranjan, Gregory M. Parkes, M. Niranjan, 2019, BMC Bioinformatics.

Outlier Detection in Benchmark Classification Tasks

Hongyu Li, Mahesan Niranjan, M. Niranjan, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Feature selection using expected attainable discrimination

Kevin J. Dalton, Mahesan Niranjan, Richard W. Prager, 1998, Pattern Recognit. Lett..

Tile optimization for area in FPGA based hardware acceleration of peptide identification

S. D. Dewasurendra, Roshan G. Ragel, M. Niranjan, 2011, 2011 6th International Conference on Industrial and Information Systems.

Average-Case Learning Curves for Radial Basis Function Networks

Mahesan Niranjan, Sean B. Holden, M. Niranjan, 1997, Neural Computation.

D 3 . 1 . 2 Regression models of trends Tools for Mining Non-stationary Data : functional prototype

Nicholas Gibbins, Trevor Cohn, Mahesan Niranjan, 2013 .

Hierarchical Bayesian Models for Regularization in Sequential Learning

Andrew H. Gee, Mahesan Niranjan, João F. G. de Freitas, 2000, Neural Computation.

OPBI: An open pipeline for biomarker identification

Mahesan Niranjan, Sugandima Vidanagamachchi, M. Niranjan, 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).