On modelling the dynamics of speech patterns. - Mahesan Niranjan; Frank Fallside (ECST, 1987)

Book reviews. - Tony Dodd 001; Leonard Bolc; Paul Taylor; Judith Wusterman; Blay Whitby; Carlton McDonald; J. Garwood; Jim Sholicar; Wendy J. Milne; Michael Pengelly; Tony Priest; Antony Galton; et al. (Artif. Intell. Rev., 1988)

Temporal decomposition: a framework for enhanced speech recognition. - Mahesan Niranjan; Frank Fallside (ICASSP, 1989)

Speech Feature Extraction Using Neural Networks. - Mahesan Niranjan; Frank Fallside (EURASIP Workshop, 1990)

CELP coding with adaptive output-error model identification. - Mahesan Niranjan (ICASSP, 1990)

Sequential Adaptation of Radial Basis Function Networks. - Visakan Kadirkamanathan; Mahesan Niranjan; Frank Fallside (NIPS, 1990)

A theoretical investigation into the performance of the Hopfield model. - Sreeram V. B. Aiyer; Mahesan Niranjan; Frank Fallside (IEEE Trans. Neural Networks, 1990)

Nonlinear adaptive filtering in nonstationary environments. - Visakan Kadirkamanathan; Mahesan Niranjan (ICASSP, 1991)

A nonlinear model for time series prediction and signal interpolation. - Mahesan Niranjan; Visakan Kadirkamanathan (ICASSP, 1991)

Models of dynamic complexity for time-series prediction (neural networks). - Visakan Kadirkamanathan; Mahesan Niranjan; Frank Fallside (ICASSP, 1992)

A Function Estimation Approach to Sequential Learning with Neural Networks. - Visakan Kadirkamanathan; Mahesan Niranjan (Neural Comput., 1993)

Recursive tracking of formants in speech signals. - Mahesan Niranjan; Ingemar J. Cox; Sunita L. Hingorani (ICASSP (2), 1994)

On the design of nonlinear speech predictors with recurrent nets. - Lizhong Wu; Mahesan Niranjan (ICASSP (2), 1994)

Fully vector-quantized neural network-based code-excited nonlinear predictive speech coding. - Lizhong Wu; Mahesan Niranjan; Frank Fallside (IEEE Trans. Speech Audio Process., 1994)

Vocal tract modelling with recurrent neural networks. - T. L. Burrows; Mahesan Niranjan (ICASSP, 1995)

The use of maximum a posteriori parameters in linear prediction of speech. - Gaafar M. K. Saleh; Mahesan Niranjan; William J. Fitzgerald 001 (EUROSPEECH, 1995)

On the practical applicability of VC dimension bounds. - Sean B. Holden; Mahesan Niranjan (Neural Comput., 1995)

On the statistical physics of radial basis function networks. - Sean B. Holden; Mahesan Niranjan (Neural Process. Lett., 1995)

Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches. - Mahesan Niranjan (NIPS, 1996)

Pruning with Replacement on Limited Resource Allocating Networks by F-Projections. - Christophe Molina; Mahesan Niranjan (Neural Comput., 1996)

Regularisation in Sequential Learning Algorithms. - João F. G. de Freitas; Mahesan Niranjan; Andrew H. Gee (NIPS, 1997)

Average-Case Learning Curves for Radial Basis Function Networks. - Sean B. Holden; Mahesan Niranjan (Neural Comput., 1997)

The past, present, and future of neural networks for signal processing. - Jenq-Neng Hwang; Sun-Yuan Kung; Mahesan Niranjan; José C. Príncipe (IEEE Signal Process. Mag., 1997)

Examples in Medical Applications. - Mahesan Niranjan (IEEE Signal Process. Mag., 1997)

Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. - Martin J. J. Scott; Mahesan Niranjan; Richard W. Prager (BMVC, 1998)

Parametric subspace modelling of speech transitions. - Klaus Reinhard; Mahesan Niranjan (ICASSP, 1998)

Speech enhancement in a Bayesian framework. - Gaafar M. K. Saleh; Mahesan Niranjan (ICASSP, 1998)

Markov chain Monte Carlo methods for speech enhancement. - Jaco Vermaak; Mahesan Niranjan (ICASSP, 1998)

Global optimisation of neural network models via sequential sampling-importance resampling. - João F. G. de Freitas; Sue E. Johnson; Mahesan Niranjan; Andrew H. Gee (ICSLP, 1998)

Global Optimisation of Neural Network Models via Sequential Sampling. - João F. G. de Freitas; Mahesan Niranjan; Arnaud Doucet; Andrew H. Gee (NIPS, 1998)

Feature selection using expected attainable discrimination. - David R. Lovell; Christopher R. Dance; Mahesan Niranjan; Richard W. Prager; Kevin J. Dalton; R. Derom (Pattern Recognit. Lett., 1998)

Hybrid sequential Monte Carlo/Kalman methods to train neural networks in non-stationary environments. - João F. G. de Freitas; Mahesan Niranjan; Andrew H. Gee (ICASSP, 1999)

Sequential Bayesian computation of logistic regression models. - Mahesan Niranjan (ICASSP, 1999)

Diphone multi-trajectory subspace models. - Klaus Reinhard; Mahesan Niranjan (ICASSP, 1999)

Diphone subspace models for phone-based HMM complementation. - Klaus Reinhard; Mahesan Niranjan (EUROSPEECH, 1999)

Speech Modelling Using Subspace and EM Techniques. - Gavin Smith; João F. G. de Freitas; Tony Robinson; Mahesan Niranjan (NIPS, 1999)

Parametric subspace modeling of speech transitions. - Klaus Reinhard; Mahesan Niranjan (Speech Commun., 1999)

Matched filter design for diphone subspace models. - Klaus Reinhard; Mahesan Niranjan (ICASSP, 2000)

Data-dependent kernels in svm classification of speech patterns. - Nathan Smith; Mahesan Niranjan (INTERSPEECH, 2000)

Hierarchical Bayesian Models for Regularization in Sequential Learning. - João F. G. de Freitas; Mahesan Niranjan; Andrew H. Gee (Neural Comput., 2000)

Sequential Monte Carlo Methods to Train Neural Network Models. - João F. G. de Freitas; Mahesan Niranjan; Andrew H. Gee; Arnaud Doucet (Neural Comput., 2000)

Neuro-computing versus linear statistical techniques applied to liver transplant monitoring: a comparative study. - David G. Melvin; Mahesan Niranjan; Richard W. Prager; Andrew K. Trull; Vikki F. Hughes (IEEE Trans. Biomed. Eng., 2000)

Dynamic Learning with the EM Algorithm for Neural Networks. - João F. G. de Freitas; Mahesan Niranjan; Andrew H. Gee (J. VLSI Signal Process., 2000)

Extractive summarization of voicemail using lexical and prosodic feature subset selection. - Konstantinos Koumpis; Steve Renals; Mahesan Niranjan (INTERSPEECH, 2001)

Speech enhancement using a Bayesian evidence approach. - Gaafar M. K. Saleh; Mahesan Niranjan (Comput. Speech Lang., 2001)

Diphone subspace mixture trajectory models for HMM complementation. - Klaus Reinhard; Mahesan Niranjan (Speech Commun., 2002)

Bayesian processing of microarray images. - Neil D. Lawrence; Marta Milo; Mahesan Niranjan; Penny Rashbass; Stephan Soullier (NNSP, 2003)

Subspaces of text discrimination with application to biological literature. - Mahesan Suwannaroj; Mahesan Niranjan (NNSP, 2003)

Sequential Bayesian Decoding with a Population of Neurons. - Si Wu 001; Danmei Chen; Mahesan Niranjan; Shun-ichi Amari (Neural Comput., 2003)

Reducing the variability in cDNA microarray image processing by Bayesian inference. - Neil D. Lawrence; Marta Milo; Mahesan Niranjan; Penny Rashbass; Stephan Soullier (Bioinform., 2004)

Outlier Detection in Benchmark Classification Tasks. - Hongyu Li; Mahesan Niranjan (ICASSP (5), 2006)

Automatic Face Recognition Using Stereo Images. - Anjali Bharatkumar Samani; Joab R. Winkler; Mahesan Niranjan (ICASSP (5), 2006)

Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster. - Anastasia Samsonova; Mahesan Niranjan; Steven Russell; Alvis Brazma (PLoS Comput. Biol., 2007)

Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering. - Yang Zhang 007; Hongyu Li; Mahesan Niranjan; Peter I. Rockett (EuroGP, 2008)

Mining Protein Database using Machine Learning Techniques. - Renata da Silva Camargo; Mahesan Niranjan (J. Integr. Bioinform., 2008)

Enhancing Automatic Construction of Gene Subnetworks by Integrating Multiple Sources of Information. - Sujimarn Suwannaroj; Mahesan Niranjan (J. Signal Process. Syst., 2008)

Handling phrase reorderings for machine translation. - Yizhao Ni; Craig Saunders; Sándor Szedmák; Mahesan Niranjan (ACL/IJCNLP (2), 2009)

Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle. - C. Q. Chang; Y. S. Hung; Mahesan Niranjan (ICASSP, 2009)

Sequential Hierarchical Pattern Clustering. - Bassam Farran; Amirthalingam Ramanan; Mahesan Niranjan (PRIB, 2009)

Cross-Platform Analysis with Binarized Gene Expression Data. - Salih Tuna; Mahesan Niranjan (PRIB, 2009)

Remote Homology Detection Using a Kernel Method that Combines Sequence and Secondary-Structure Similarity Scores. - Daniela Wieser; Mahesan Niranjan (Silico Biol., 2009)

Exploiting Long-Range Dependencies in Protein beta-Sheet Secondary Structure Prediction. - Yizhao Ni; Mahesan Niranjan (PRIB, 2010)

Reducing the algorithmic variability in transcriptome-based inference. - Salih Tuna; Mahesan Niranjan (Bioinform., 2010)

Approximate low-rank factorization with structured factors. - Ivan Markovsky; Mahesan Niranjan (Comput. Stat. Data Anal., 2010)

The application of structured learning in natural language processing. - Yizhao Ni; Craig Saunders; Sándor Szedmák; Mahesan Niranjan (Mach. Transl., 2010)

Estimating a State-Space Model from Point Process Observations: A Note on Convergence. - Ke Yuan; Mahesan Niranjan (Neural Comput., 2010)

Asymptotic mean and variance of Gini correlation for bivariate normal samples. - Weichao Xu; Y. S. Hung; Mahesan Niranjan; Minfen Shen (IEEE Trans. Signal Process., 2010)

Inference from Low Precision Transcriptome Data Representation. - Salih Tuna; Mahesan Niranjan (J. Signal Process. Syst., 2010)

Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. - Yizhao Ni; Craig Saunders; Sándor Szedmák; Mahesan Niranjan (J. Mach. Learn. Res., 2011)

Online Variational Inference for State-Space Models with Point-Process Observations. - Andrew Zammit-Mangion; Ke Yuan; Visakan Kadirkamanathan; Mahesan Niranjan; Guido Sanguinetti (Neural Comput., 2011)

Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters. - Chathurika Dharmagunawardhana; Sasan Mahmoodi; Michael J. Bennett; Mahesan Niranjan (BMVC, 2012)

Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology. - Kaya Kuru; Mahesan Niranjan; Yusuf Tunca (ICMLA (2), 2012)

On the Trade-Off of Accuracy and Computational Complexity for Classifying Normal and Abnormal ECG in Remote CVD Monitoring Systems. - Taihai Chen; Evangelos B. Mazomenos; Koushik Maharatna; Srinandan Dasmahapatra; Mahesan Niranjan (SiPS, 2012)

Gaussian process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile. - Wei Liu; Mahesan Niranjan (Bioinform., 2012)

State and parameter estimation of the heat shock response system using Kalman and particle filters. - Xin Liu; Mahesan Niranjan (Bioinform., 2012)

Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations. - Ke Yuan; Mark A. Girolami; Mahesan Niranjan (Neural Comput., 2012)

A Review of Codebook Models in Patch-Based Visual Object Recognition. - Amirthalingam Ramanan; Mahesan Niranjan (J. Signal Process. Syst., 2012)

Enriching Texture Analysis with Semantic Data. - Tim Matthews; Mark S. Nixon; Mahesan Niranjan (CVPR, 2013)

Inferring Time-Delayed Gene Regulatory Networks Using Cross-Correlation and Sparse Regression. - Piyushkumar A. Mundra; Jie Zheng 002; Mahesan Niranjan; Roy E. Welsch; Jagath C. Rajapakse (ISBRA, 2013)

Social Event Detection Via Sparse Multi-modal Feature Selection and Incremental Density Based Clustering. - Sina Samangooei; Jonathon S. Hare; David Dupplaw; Mahesan Niranjan; Nicholas Gibbins; Paul H. Lewis; Jamie Davies; Neha Jain 005; John Preston (MediaEval, 2013)

Inducing discrimination in biologically inspired models of visual scene recognition. - Tayyaba Azim; Mahesan Niranjan (MLSP, 2013)

Compact codebook design for visual scene recognition by Sequential Input Space Carving. - B. Mayurathan; U. A. J. Pinidiyaarachchi; Mahesan Niranjan (MLSP, 2013)

Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes. - Yawwani Gunawardana; Mahesan Niranjan (Bioinform., 2013)

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios. - Akiko Takeda; Mahesan Niranjan; Jun-ya Gotoh; Yoshinobu Kawahara (Comput. Manag. Sci., 2013)

Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems. - Taihai Chen; Evangelos B. Mazomenos; Koushik Maharatna; Srinandan Dasmahapatra; Mahesan Niranjan (IEEE J. Emerg. Sel. Topics Circuits Syst., 2013)

On Acoustic Emotion Recognition: Compensating for Covariate Shift. - Ali Hassan 001; Robert I. Damper; Mahesan Niranjan (IEEE Trans. Speech Audio Process., 2013)

Sinhala-Tamil Machine Translation: Towards better Translation Quality. - Randil Pushpananda; Ruvan Weerasinghe; Mahesan Niranjan (ALTA, 2014)

Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression. - Abdullah Alrajeh; Mahesan Niranjan (EMNLP, 2014)

Memory-efficient large-scale linear support vector machine. - Abdullah Alrajeh; Akiko Takeda; Mahesan Niranjan (ICMV, 2014)

An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation. - Chathurika Dharmagunawardhana; Sasan Mahmoodi; Michael J. Bennett; Mahesan Niranjan (ICPRAM, 2014)

Computational Models of Object Recognition - Goal, Role and Success. - Tayyaba Azim; Mahesan Niranjan (VISAPP (1), 2014)

Texture Classification with Fisher Kernel Extracted from the Continuous Models of RBM. - Tayyaba Azim; Mahesan Niranjan (VISAPP (2), 2014)

Quantitative Analysis of Pulmonary Emphysema using Isotropic Gaussian Markov Random Fields. - Chathurika Dharmagunawardhana; Sasan Mahmoodi; Michael J. Bennett; Mahesan Niranjan (VISAPP (3), 2014)

Bayesian Reordering Model with Feature Selection. - Abdullah Alrajeh; Mahesan Niranjan (WMT@ACL, 2014)

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. - Kaya Kuru; Mahesan Niranjan; Yusuf Tunca; Erhan Osvank; Tayyaba Azim (Artif. Intell. Medicine, 2014)

Gaussian Markov random field based improved texture descriptor for image segmentation. - Chathurika Dharmagunawardhana; Sasan Mahmoodi; Michael J. Bennett; Mahesan Niranjan (Image Vis. Comput., 2014)

Statistical Machine Translation from and into Morphologically Rich and Low Resourced Languages. - Randil Pushpananda; Ruvan Weerasinghe; Mahesan Niranjan (CICLing (1), 2015)

MIAT: A novel attribute selection approach to better predict upper gastrointestinal cancer. - Avi Rosenfeld; David G. Graham; Rifat A. Hamoudi; Rommel Butawan; Victor Eneh; Saif Khan; Haroon Miah; Mahesan Niranjan; Laurence B. Lovat (DSAA, 2015)

Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development. - Haifen Chen; Jing Guo; Shital K. Mishra; Paul Robson 001; Mahesan Niranjan; Jie Zheng 002 (Bioinform., 2015)

Outlier detection at the transcriptome-proteome interface. - Yawwani Gunawardana; Shuhei Fujiwara; Akiko Takeda; Jeongmin Woo; Christopher H. Woelk; Mahesan Niranjan (Bioinform., 2015)

Speaker Adaptation Applied to Sinhala Speech Recognition. - Thilini Nadungodage; Ruvan Weerasinghe; Mahesan Niranjan (Int. J. Comput. Linguistics Appl., 2015)

Detection of social events in streams of social multimedia. - Jonathon S. Hare; Sina Samangooei; Mahesan Niranjan; Nicholas Gibbins (Int. J. Multim. Inf. Retr., 2015)

Scalable Reordering Models for SMT based on Multiclass SVM. - Abdullah Alrajeh; Mahesan Niranjan (Prague Bull. Math. Linguistics, 2015)

Defining the Gold Standard Definitions for the Morphology of Sinhala Words. - Viraj Welgama; Ruvan Weerasinghe; Mahesan Niranjan (Res. Comput. Sci., 2015)

Extending the feature set of a data-driven artificial neural network model of pricing financial options. - Luis Montesdeoca; Mahesan Niranjan (SSCI, 2016)

Rotation invariant texture descriptors based on Gaussian Markov random fields for classification. - Chathurika Dharmagunawardhana; Sasan Mahmoodi; Michael J. Bennett; Mahesan Niranjan (Pattern Recognit. Lett., 2016)

Robust Portfolio Risk Minimization Using the Graphical Lasso. - Tristan Millington; Mahesan Niranjan (ICONIP (2), 2017)

A Method of Integrating Spatial Proteomics and Protein-Protein Interaction Network Data. - Steven Squires; Rob M. Ewing; Adam Prügel-Bennett; Mahesan Niranjan (ICONIP (5), 2017)

Non-Negative Matrix Factorization with Exogenous Inputs for Modeling Financial Data. - Steven Squires; Luis Montesdeoca; Adam Prügel-Bennett; Mahesan Niranjan (ICONIP (2), 2017)

Rank Selection in Nonnegative Matrix Factorization using Minimum Description Length. - Steven Squires; Adam Prügel-Bennett; Mahesan Niranjan (Neural Comput., 2017)

Improved understanding of aqueous solubility modeling through topological data analysis. - Mariam Pirashvili; Lee Steinberg; Francisco Belchí Guillamón; Mahesan Niranjan; Jeremy G. Frey; Jacek Brodzki (J. Cheminformatics, 2018)

Deep Cascade Learning. - Enrique S. Marquez; Jonathon S. Hare; Mahesan Niranjan (IEEE Trans. Neural Networks Learn. Syst., 2018)

Transfer learning across human activities using a cascade neural network architecture. - Xin Du; Katayoun Farrahi; Mahesan Niranjan (UbiComp, 2019)

Saliency Map on Cnns for Protein Secondary Structure Prediction. - Guillermo Romero Moreno; Mahesan Niranjan; Adam Prügel-Bennett (ICASSP, 2019)

Representation-dimensionality Trade-off in Biological Sequence-based Inference. - Bahman Asadi; Mahesan Niranjan (IJCNN, 2019)

Classification and Regression Analysis of Lung Tumors from Multi-level Gene Expression Data. - Pratheeba Jeyananthan; Mahesan Niranjan (IJCNN, 2019)

Quantifying Influence in Financial Markets via Partial Correlation Network Inference. - Tristan Millington; Mahesan Niranjan (ISPA, 2019)

Variational Autoencoder for Non-Negative Matrix Factorization with Exogenous Inputs Applied to Financial Data Modelling. - Luis Montesdeoca; Steven Squires; Mahesan Niranjan (ISPA, 2019)

On Comparing the Influences of Exogenous Information on Bitcoin Prices and Stock Index Values. - Luis Montesdeoca; Mahesan Niranjan (MARBLE, 2019)

Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-'omics analysis. - Gregory M. Parkes; Mahesan Niranjan (BMC Bioinform., 2019)

A Biologically Inspired Visual Working Memory for Deep Networks. - Ethan Harris; Mahesan Niranjan; Jonathon S. Hare (CoRR, 2019)

Minimum description length as an objective function for non-negative matrix factorization. - Steven Squires; Adam Prügel-Bennett; Mahesan Niranjan (CoRR, 2019)

A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation. - Steven Squires; Adam Prügel-Bennett; Mahesan Niranjan (CoRR, 2019)

A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting. - Manuel Nunes; Enrico H. Gerding; Frank McGroarty; Mahesan Niranjan (Expert Syst. Appl., 2019)

Partial correlation financial networks. - Tristan Millington; Mahesan Niranjan (Appl. Netw. Sci., 2020)

Understanding and Enhancing Mixed Sample Data Augmentation. - Ethan Harris; Antonia Marcu; Matthew Painter; Mahesan Niranjan; Adam Prügel-Bennett; Jonathon S. Hare (CoRR, 2020)

Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box. - Manuel Nunes; Enrico H. Gerding; Frank McGroarty; Mahesan Niranjan (CoRR, 2020)

Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation. - Tristan Millington; Mahesan Niranjan (CoRR, 2020)

A Numerical Measure of the Instability of Mapper-Type Algorithms. - Francisco Belchí Guillamón; Jacek Brodzki; Matthew Burfitt; Mahesan Niranjan (J. Mach. Learn. Res., 2020)

Estimation of Gaussian mixture models via tensor moments with application to online learning. - Donya Rahmani; Mahesan Niranjan; Damien Fay; Akiko Takeda; Jacek Brodzki (Pattern Recognit. Lett., 2020)

Depth Estimation from a Single Omnidirectional Image using Domain Adaptation. - Yihong Wu 004; Yuwen Heng; Mahesan Niranjan; Hansung Kim 001 (CVMP, 2021)

Automated gastrointestinal abnormalities detection from endoscopic images. - Premananth Gowtham; Mahesan Niranjan; Kaneswaran Anantharajah (ICIIS, 2021)

Information Bottleneck Theory Based Exploration of Cascade Learning. - Xin Du; Katayoun Farrahi; Mahesan Niranjan (Entropy, 2021)

The AI for Scientific Discovery Network+. - Samantha Kanza; Colin Leonard Bird; Mahesan Niranjan; William McNeill; Jeremy Graham Frey (Patterns, 2021)

Room Acoustic Properties Estimation from a Single 360° Photo. - Mona Alawadh; Yihong Wu 004; Yuwen Heng; Luca Remaggi; Mahesan Niranjan; Hansung Kim 001 (EUSIPCO, 2022)

Robust 3D rotation invariant local binary pattern for volumetric texture classification. - Shengyu Lu; Sasan Mahmoodi; Mahesan Niranjan (ICPR, 2022)

Deep Cascade Learning for Optimal Medical Image Feature Representation. - Junwen Wang; Xin Du; Katayoun Farrahi; Mahesan Niranjan (MLHC, 2022)

Flattening the Curve Through Reinforcement Learning Driven Test and Trace Policies. - Andrei C. Rusu; Katayoun Farrahi; Mahesan Niranjan (PervasiveHealth, 2022)

TransformerGO: predicting protein-protein interactions by modelling the attention between sets of gene ontology terms. - Ioan Ieremie; Rob M. Ewing; Mahesan Niranjan (Bioinform., 2022)

Robustness under parameter and problem domain alterations of Bayesian optimization methods for chemical reactions. - Rubaiyat Mohammad Khondaker; Stephen Gow; Samantha Kanza; Jeremy G. Frey; Mahesan Niranjan (J. Cheminformatics, 2022)

Convex Multi-View Clustering Via Robust Low Rank Approximation With Application to Multi-Omic Data. - Omar Shetta; Mahesan Niranjan; Srinandan Dasmahapatra (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

Depth Estimation for a Single Omnidirectional Image with Reversed-Gradient Warming-up Thresholds Discriminator. - Yihong Wu 004; Yuwen Heng; Mahesan Niranjan; Hansung Kim 001 (ICASSP, 2023)

IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer. - Keqiang Fan; Xiaohao Cai; Mahesan Niranjan (ICONIP (6), 2023)

Prompt position really matters in few-shot and zero-shot NLU tasks. - Junyu Mao; Stuart E. Middleton; Mahesan Niranjan (CoRR, 2023)

GO-LDA: Generalised Optimal Linear Discriminant Analysis. - Jiahui Liu; Xiaohao Cai; Mahesan Niranjan (CoRR, 2023)

Few-shot Learning for Inference in Medical Imaging with Subspace Feature Representations. - Jiahui Liu; Keqiang Fan; Xiaohao Cai; Mahesan Niranjan (CoRR, 2023)

Depth Insight - Contribution of Different Features to Indoor Single-image Depth Estimation. - Yihong Wu 004; Yuwen Heng; Mahesan Niranjan; Hansung Kim 001 (CoRR, 2023)

Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes. - Jiahui Liu; Xiaohao Cai; Mahesan Niranjan (CoRR, 2023)

Quantum annealing-based clustering of single cell RNA-seq data. - Michal Kubacki; Mahesan Niranjan (Briefings Bioinform., 2023)

Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning. - Alex Thomas; Mahesan Niranjan; Julian Legg (Sensors, 2023)

SliceFormer: Deep Dense Depth Estimation from a Single Indoor Omnidirectional Image Using a Slice-Based Transformer. - Yihong Wu 004; Yuwen Heng; Mahesan Niranjan; Hansung Kim 001 (ICEIC, 2024)

3D Semantic Scene Completion From A Depth Map With Unsupervised Learning For Semantics Prioritisation. - Mona Alawadh; Mahesan Niranjan; Hansung Kim 001 (ICIP, 2024)

Do Prompt Positions Really Matter? - Junyu Mao; Stuart E. Middleton; Mahesan Niranjan (NAACL-HLT (Findings), 2024)

Discrepancy-based diffusion models for lesion detection in brain MRI. - Keqiang Fan; Xiaohao Cai; Mahesan Niranjan (Comput. Biol. Medicine, 2024)

Semantic Scene Completion with Multi-Feature Data Balancing Network. - Mona Alawadh; Mahesan Niranjan; Hansung Kim 001 (CoRR, 2024)

Non-negative subspace feature representation for few-shot learning in medical imaging. - Keqiang Fan; Xiaohao Cai; Mahesan Niranjan (Image Vis. Comput., 2024)

EpiCURB: Learning to Derive Epidemic Control Policies. - Andrei C. Rusu; Katayoun Farrahi; Mahesan Niranjan (IEEE Pervasive Comput., 2024)

Protein language models meet reduced amino acid alphabets. - Ioan Ieremie; Rob M. Ewing; Mahesan Niranjan (Bioinform., 2024)

How does the Machine Perceive Depth for Indoor Single Images with CNN? - Yihong Wu 004; Yuwen Heng; Mahesan Niranjan; Hansung Kim 001 (CVPR Workshops, 2025)

Arch-LLM: Taming LLMs for Neural Architecture Generation via Unsupervised Discrete Representation Learning. - Deshani Geethika Poddenige; Sachith Seneviratne; Damith A. Senanayake; Mahesan Niranjan; Ponnuthurai N. Suganthan; Saman K. Halgamuge (CoRR, 2025)