Song Classification: Classical and Non-classical Discrimination Using MFCC Co-occurrence Based Features. - Arijit Ghosal; Rudrasis Chakraborty; Bibhas Chandra Dhara; Sanjoy Kumar Saha 001 (FGIT-SIP, 2011)

Segmenting web-domains and hashtags using length specific models. - Sriram Srinivasan; Sourangshu Bhattacharya; Rudrasis Chakraborty (CIKM, 2012)

Song/instrumental classification using spectrogram based contextual features. - Arijit Ghosal; Rudrasis Chakraborty; Bibhas Chandra Dhara; Sanjoy Kumar Saha 001 (CUBE, 2012)

Genre-Based Classification of Song Using Perceptual Features. - Arijit Ghosal; Rudrasis Chakraborty; Bibhas Chandra Dhara; Sanjoy Kumar Saha 001 (ICACNI, 2013)

Incorporating ϵ-dominance in AMOSA: Application to multiobjective 0/1 knapsack problem and clustering gene expression data. - Sanghamitra Bandyopadhyay; Ujjwal Maulik; Rudrasis Chakraborty (Appl. Soft Comput., 2013)

Sensor (Group Feature) Selection with Controlled Redundancy in a Connectionist Framework. - Rudrasis Chakraborty; Chin-Teng Lin; Nikhil R. Pal (Int. J. Neural Syst., 2014)

Recursive Fréchet Mean Computation on the Grassmannian and Its Applications to Computer Vision. - Rudrasis Chakraborty; Baba C. Vemuri (ICCV, 2015)

Nonlinear Regression on Riemannian Manifolds and Its Applications to Neuro-Image Analysis. - Monami Banerjee; Rudrasis Chakraborty; Edward Ofori; David E. Vaillancourt; Baba C. Vemuri (MICCAI (1), 2015)

Perceptual feature-based song genre classification using RANSAC. - Arijit Ghosal; Rudrasis Chakraborty; Bibhas Chandra Dhara; Sanjoy Kumar Saha 001 (Int. J. Comput. Intell. Stud., 2015)

Priority based ∈ dominance: A new measure in multiobjective optimization. - Sanghamitra Bandyopadhyay; Rudrasis Chakraborty; Ujjwal Maulik (Inf. Sci., 2015)

Feature Selection Using a Neural Framework With Controlled Redundancy. - Rudrasis Chakraborty; Nikhil R. Pal (IEEE Trans. Neural Networks Learn. Syst., 2015)

A Nonlinear Regression Technique for Manifold Valued Data with Applications to Medical Image Analysis. - Monami Banerjee; Rudrasis Chakraborty; Edward Ofori; Michael S. Okun; David E. Vaillancourt; Baba C. Vemuri (CVPR, 2016)

An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds. - Rudrasis Chakraborty; Dohyung Seo; Baba C. Vemuri (CVPR, 2016)

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. - Rudrasis Chakraborty; Søren Hauberg; Baba C. Vemuri (CVPR, 2017)

Sparse Exact PGA on Riemannian Manifolds. - Monami Banerjee; Rudrasis Chakraborty; Baba C. Vemuri (ICCV, 2017)

A Geometric Framework for Statistical Analysis of Trajectories with Distinct Temporal Spans. - Rudrasis Chakraborty; Vikas Singh; Nagesh Adluru; Baba C. Vemuri (ICCV, 2017)

Statistical analysis of longitudinal data and applications to neuro-imaging. - Rudrasis Chakraborty; Baba C. Vemuri (ICIP, 2017)

Statistics on the space of trajectories for longitudinal data analysis. - Rudrasis Chakraborty; Monami Banerjee; Baba C. Vemuri (ISBI, 2017)

Statistics on the (compact) Stiefel manifold: Theory and Applications. - Rudrasis Chakraborty; Baba C. Vemuri (CoRR, 2017)

A Mixture Model for Aggregation of Multiple Pre-Trained Weak Classifiers. - Rudrasis Chakraborty; Chun-Hao Yang; Baba C. Vemuri (CVPR Workshops, 2018)

A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices. - Rudrasis Chakraborty; Chun-Hao Yang; Xingjian Zhen; Monami Banerjee; Derek B. Archer; David E. Vaillancourt; Vikas Singh; Baba C. Vemuri (NeurIPS, 2018)

Generative Adversarial Network based Autoencoder: Application to Fault Detection Problem for Closed Loop Dynamical Systems. - Indrasis Chakraborty; Rudrasis Chakraborty; Draguna L. Vrabie (DX@Safeprocess, 2018)

Dictionary Learning and Sparse Coding on Statistical Manifolds. - Rudrasis Chakraborty; Monami Banerjee; Baba C. Vemuri (CoRR, 2018)

H-CNNs: Convolutional Neural Networks for Riemannian Homogeneous Spaces. - Rudrasis Chakraborty; Monami Banerjee; Baba C. Vemuri (CoRR, 2018)

Statistical Recurrent Models on Manifold valued Data. - Rudrasis Chakraborty; Chun-Hao Yang; Xingjian Zhen; Monami Banerjee; Derek B. Archer; David E. Vaillancourt; Vikas Singh; Baba C. Vemuri (CoRR, 2018)

ManifoldNet: A Deep Network Framework for Manifold-valued Data. - Rudrasis Chakraborty; Jose Bouza; Jonathan H. Manton; Baba C. Vemuri (CoRR, 2018)

Sur-Real: Frechet Mean and Distance Transform for Complex-Valued Deep Learning. - Rudrasis Chakraborty; Jiayun Wang; Stella X. Yu (CVPR Workshops, 2019)

Visual Similarity from Optimizing Feature and Memory On A Hypersphere. - Xinlei Pan; Rudrasis Chakraborty; Stella X. Yu (CVPR Workshops, 2019)

Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data. - Rudrasis Chakraborty; Xingjian Zhen; Nicholas Vogt; Barbara B. Bendlin; Vikas Singh (ICCV, 2019)

Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains. - Ronak Mehta; Rudrasis Chakraborty; Vikas Singh; Yunyang Xiong (ICCV, 2019)

A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging. - Rudrasis Chakraborty; Jose Bouza; Jonathan H. Manton; Baba C. Vemuri (IPMI, 2019)

DMR-CNN: A CNN Tailored For DMR Scans With Applications To PD Classification. - Monami Banerjee; Rudrasis Chakraborty; Derek B. Archer; David E. Vaillancourt; Baba C. Vemuri (ISBI, 2019)

SurReal: Fréchet Mean and Distance Transform for Complex-Valued Deep Learning. - Rudrasis Chakraborty; Jiayun Wang; Stella X. Yu (CoRR, 2019)

Spatial Transformer for 3D Points. - Jiayun Wang; Rudrasis Chakraborty; Stella X. Yu (CoRR, 2019)

Surreal: Complex-Valued Deep Learning as Principled Transformations on a Rotational Lie Group. - Rudrasis Chakraborty; Yifei Xing 001; Stella X. Yu (CoRR, 2019)

POIRot: A rotation invariant omni-directional pointnet. - Liu Yang; Rudrasis Chakraborty; Stella X. Yu (CoRR, 2019)

C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning. - Rudrasis Chakraborty; Yifei Xing 001; Minxuan Duan; Stella X. Yu (CVPR Workshops, 2020)

Orthogonal Convolutional Neural Networks. - Jiayun Wang; Yubei Chen; Rudrasis Chakraborty; Stella X. Yu (CVPR, 2020)

An "Augmentation-Free" Rotation Invariant Classification Scheme on Point-Cloud and Its Application to Neuroimaging. - Liu Yang; Rudrasis Chakraborty (ISBI, 2020)

A GMM Based Algorithm To Generate Point-Cloud And Its Application To Neuroimaging. - Liu Yang; Rudrasis Chakraborty (ISBI Workshops, 2020)

ManifoldNorm: Extending normalizations on Riemannian Manifolds. - Rudrasis Chakraborty (CoRR, 2020)

Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention. - Yunyang Xiong; Zhanpeng Zeng; Rudrasis Chakraborty; Mingxing Tan; Glenn Fung; Yin Li 003; Vikas Singh (AAAI, 2021)

Flow-based Generative Models for Learning Manifold to Manifold Mappings. - Xingjian Zhen; Rudrasis Chakraborty; Liu Yang; Vikas Singh (AAAI, 2021)

Simpler Certified Radius Maximization by Propagating Covariances. - Xingjian Zhen; Rudrasis Chakraborty; Vikas Singh (CVPR, 2021)

An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. - Zihang Meng; Rudrasis Chakraborty; Vikas Singh (NeurIPS, 2021)

A variational approximation for analyzing the dynamics of panel data. - Jurijs Nazarovs; Rudrasis Chakraborty; Songwong Tasneeyapant; Sathya N. Ravi; Vikas Singh (UAI, 2021)

Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning. - Rudrasis Chakraborty; Liu Yang; Søren Hauberg; Baba C. Vemuri (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets. - Vishnu Suresh Lokhande; Rudrasis Chakraborty; Sathya N. Ravi; Vikas Singh (CVPR, 2022)

Understanding Uncertainty Maps in Vision with Statistical Testing. - Jurijs Nazarovs; Zhichun Huang; Songwong Tasneeyapant; Rudrasis Chakraborty; Vikas Singh (CVPR, 2022)

On the Versatile Uses of Partial Distance Correlation in Deep Learning. - Xingjian Zhen; Zihang Meng; Rudrasis Chakraborty; Vikas Singh (ECCV (26), 2022)

Forward Operator Estimation in Generative Models with Kernel Transfer Operators. - Zhichun Huang; Rudrasis Chakraborty; Vikas Singh (ICML, 2022)

Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data. - Jurijs Nazarovs; Rudrasis Chakraborty; Songwong Tasneeyapant; Sathya N. Ravi; Vikas Singh (CoRR, 2022)

VolterraNet: A Higher Order Convolutional Network With Group Equivariance for Homogeneous Manifolds. - Monami Banerjee; Rudrasis Chakraborty; Jose Bouza; Baba C. Vemuri (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

ManifoldNet: A Deep Neural Network for Manifold-Valued Data With Applications. - Rudrasis Chakraborty; Jose Bouza; Jonathan H. Manton; Baba C. Vemuri (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Transformer for 3D Point Clouds. - Jiayun Wang; Rudrasis Chakraborty; Stella X. Yu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold. - Rudrasis Chakraborty; Yifei Xing 001; Stella X. Yu (IEEE Trans. Neural Networks Learn. Syst., 2022)

Variational Sampling of Temporal Trajectories. - Jurijs Nazarovs; Zhichun Huang; Xingjian Zhen; Sourav Pal; Rudrasis Chakraborty; Vikas Singh (CoRR, 2024)

FoGE: Fock Space inspired encoding for graph prompting. - Sotirios Panagiotis Chytas; Rudrasis Chakraborty; Vikas Singh (CoRR, 2025)