Rudrasis Chakraborty

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 68

Top Topics

Nonlinear Regression

Convolutional Neural Networks

Top Venues

IEEE Trans. Pattern Anal. Mach. Intell.

Publications

Jurijs Nazarovs, Zhichun Huang, Xingjian Zhen, Sourav Pal, Rudrasis Chakraborty, Vikas Singh
Variational Sampling of Temporal Trajectories. CoRR (2024)
Monami Banerjee, Rudrasis Chakraborty, Jose Bouza, Baba C. Vemuri
VolterraNet: A Higher Order Convolutional Network With Group Equivariance for Homogeneous Manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2) (2022)
Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh
Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data. CoRR (2022)
Zhichun Huang, Rudrasis Chakraborty, Vikas Singh
Forward Operator Estimation in Generative Models with Kernel Transfer Operators. ICML (2022)
Rudrasis Chakraborty, Yifei Xing, Stella X. Yu
SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold. IEEE Trans. Neural Networks Learn. Syst. 33 (3) (2022)
Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh
On the Versatile Uses of Partial Distance Correlation in Deep Learning. ECCV (26) (2022)
Jiayun Wang, Rudrasis Chakraborty, Stella X. Yu
Transformer for 3D Point Clouds. IEEE Trans. Pattern Anal. Mach. Intell. 44 (8) (2022)
Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets. CoRR (2022)
Rudrasis Chakraborty, Jose Bouza, Jonathan H. Manton, Baba C. Vemuri
ManifoldNet: A Deep Neural Network for Manifold-Valued Data With Applications. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2) (2022)
Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets. CVPR (2022)
Jurijs Nazarovs, Zhichun Huang, Songwong Tasneeyapant, Rudrasis Chakraborty, Vikas Singh
Understanding Uncertainty Maps in Vision with Statistical Testing. CVPR (2022)
Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh
On the Versatile Uses of Partial Distance Correlation in Deep Learning. CoRR (2022)
Rudrasis Chakraborty, Liu Yang, Søren Hauberg, Baba C. Vemuri
Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43 (11) (2021)
Zhichun Huang, Rudrasis Chakraborty, Vikas Singh
Forward Operator Estimation in Generative Models with Kernel Transfer Operators. CoRR (2021)
Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh
A variational approximation for analyzing the dynamics of panel data. UAI (2021)
Zihang Meng, Rudrasis Chakraborty, Vikas Singh
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. NeurIPS (2021)
Monami Banerjee, Rudrasis Chakraborty, Jose Bouza, Baba C. Vemuri
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds. CoRR (2021)
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh
Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention. AAAI (2021)
Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh
Flow-based Generative Models for Learning Manifold to Manifold Mappings. AAAI (2021)
Zihang Meng, Rudrasis Chakraborty, Vikas Singh
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. CoRR (2021)
Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh
Simpler Certified Radius Maximization by Propagating Covariances. CoRR (2021)
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention. CoRR (2021)
Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh
Simpler Certified Radius Maximization by Propagating Covariances. CVPR (2021)
Yifei Xing, Rudrasis Chakraborty, Minxuan Duan, Stella X. Yu
C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning. CoRR (2020)
Rudrasis Chakraborty
ManifoldNorm: Extending normalizations on Riemannian Manifolds. CoRR (2020)
Rudrasis Chakraborty, Yifei Xing, Minxuan Duan, Stella X. Yu
C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning. CVPR Workshops (2020)
Liu Yang, Rudrasis Chakraborty
A GMM Based Algorithm To Generate Point-Cloud And Its Application To Neuroimaging. ISBI Workshops (2020)
Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh
Flow-based Generative Models for Learning Manifold to Manifold Mappings. CoRR (2020)
Liu Yang, Rudrasis Chakraborty
An "Augmentation-Free" Rotation Invariant Classification Scheme on Point-Cloud and Its Application to Neuroimaging. ISBI (2020)
Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu
Orthogonal Convolutional Neural Networks. CVPR (2020)
Rudrasis Chakraborty, Jiayun Wang, Stella X. Yu
SurReal: Fréchet Mean and Distance Transform for Complex-Valued Deep Learning. CoRR (2019)
Xinlei Pan, Rudrasis Chakraborty, Stella X. Yu
Visual Similarity from Optimizing Feature and Memory On A Hypersphere. CVPR Workshops (2019)
Xingjian Zhen, Rudrasis Chakraborty, Nicholas Vogt, Barbara B. Bendlin, Vikas Singh
Dilated Convolutional Neural Networks for Sequential Manifold-valued Data. CoRR (2019)
Liu Yang, Rudrasis Chakraborty
An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging. CoRR (2019)
Liu Yang, Rudrasis Chakraborty
A GMM based algorithm to generate point-cloud and its application to neuroimaging. CoRR (2019)
Ronak Mehta, Rudrasis Chakraborty, Vikas Singh, Yunyang Xiong
Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains. ICCV (2019)
Monami Banerjee, Rudrasis Chakraborty, Derek Archer, David E. Vaillancourt, Baba C. Vemuri
DMR-CNN: A CNN Tailored For DMR Scans With Applications To PD Classification. ISBI (2019)
Rudrasis Chakraborty, Xingjian Zhen, Nicholas Vogt, Barbara B. Bendlin, Vikas Singh
Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data. ICCV (2019)
Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu
Orthogonal Convolutional Neural Networks. CoRR (2019)
Rudrasis Chakraborty, Jose Bouza, Jonathan H. Manton, Baba C. Vemuri
A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging. IPMI (2019)
Rudrasis Chakraborty, Yifei Xing, Stella X. Yu
Surreal: Complex-Valued Deep Learning as Principled Transformations on a Rotational Lie Group. CoRR (2019)
Liu Yang, Rudrasis Chakraborty, Stella X. Yu
POIRot: A rotation invariant omni-directional pointnet. CoRR (2019)
Jiayun Wang, Rudrasis Chakraborty, Stella X. Yu
Spatial Transformer for 3D Points. CoRR (2019)
Rudrasis Chakraborty, Jiayun Wang, Stella X. Yu
Sur-Real: Frechet Mean and Distance Transform for Complex-Valued Deep Learning. CVPR Workshops (2019)
Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
H-CNNs: Convolutional Neural Networks for Riemannian Homogeneous Spaces. CoRR (2018)
Indrasis Chakraborty, Rudrasis Chakraborty, Draguna L. Vrabie
Generative Adversarial Network based Autoencoder: Application to Fault Detection Problem for Closed Loop Dynamical Systems. DX@Safeprocess (2018)
Rudrasis Chakraborty, Chun-Hao Yang, Baba C. Vemuri
A mixture model for aggregation of multiple pre-trained weak classifiers. CoRR (2018)
Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
Dictionary Learning and Sparse Coding on Statistical Manifolds. CoRR (2018)
Indrasis Chakraborty, Rudrasis Chakraborty, Draguna L. Vrabie
Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems. CoRR (2018)
Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices. NeurIPS (2018)
Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri
Statistical Recurrent Models on Manifold valued Data. CoRR (2018)
Rudrasis Chakraborty, Chun-Hao Yang, Baba C. Vemuri
A Mixture Model for Aggregation of Multiple Pre-Trained Weak Classifiers. CVPR Workshops (2018)
Rudrasis Chakraborty, Jose Bouza, Jonathan H. Manton, Baba C. Vemuri
ManifoldNet: A Deep Network Framework for Manifold-valued Data. CoRR (2018)
Rudrasis Chakraborty, Søren Hauberg, Baba C. Vemuri
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. CoRR (2017)
Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
Statistics on the space of trajectories for longitudinal data analysis. ISBI (2017)
Rudrasis Chakraborty, Baba C. Vemuri
Statistical analysis of longitudinal data and applications to neuro-imaging. ICIP (2017)
Monami Banerjee, Rudrasis Chakraborty, Baba C. Vemuri
Sparse Exact PGA on Riemannian Manifolds. ICCV (2017)
Rudrasis Chakraborty, Søren Hauberg, Baba C. Vemuri
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. CVPR (2017)
Rudrasis Chakraborty, Baba C. Vemuri
Statistics on the (compact) Stiefel manifold: Theory and Applications. CoRR (2017)
Rudrasis Chakraborty, Vikas Singh, Nagesh Adluru, Baba C. Vemuri
A Geometric Framework for Statistical Analysis of Trajectories with Distinct Temporal Spans. ICCV (2017)
Rudrasis Chakraborty, Dohyung Seo, Baba C. Vemuri
An efficient Exact-PGA algorithm for constant curvature manifolds. CoRR (2016)
Rudrasis Chakraborty, Dohyung Seo, Baba C. Vemuri
An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds. CVPR (2016)
Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, Michael S. Okun, David E. Vaillancourt, Baba C. Vemuri
A Nonlinear Regression Technique for Manifold Valued Data with Applications to Medical Image Analysis. CVPR (2016)
Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara, Sanjoy Kumar Saha
Perceptual feature-based song genre classification using RANSAC. Int. J. Comput. Intell. Stud. 4 (1) (2015)
Rudrasis Chakraborty, Nikhil R. Pal
Feature Selection Using a Neural Framework With Controlled Redundancy. IEEE Trans. Neural Networks Learn. Syst. 26 (1) (2015)
Sanghamitra Bandyopadhyay, Rudrasis Chakraborty, Ujjwal Maulik
Priority based ∈ dominance: A new measure in multiobjective optimization. Inf. Sci. 305 (2015)
Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, David E. Vaillancourt, Baba C. Vemuri
Nonlinear Regression on Riemannian Manifolds and Its Applications to Neuro-Image Analysis. MICCAI (1) (2015)
Rudrasis Chakraborty, Baba C. Vemuri
Recursive Fréchet Mean Computation on the Grassmannian and Its Applications to Computer Vision. ICCV (2015)
Rudrasis Chakraborty, Chin-Teng Lin, Nikhil R. Pal
Sensor (Group Feature) Selection with Controlled Redundancy in a Connectionist Framework. Int. J. Neural Syst. 24 (6) (2014)
Sanghamitra Bandyopadhyay, Ujjwal Maulik, Rudrasis Chakraborty
Incorporating ϵ-dominance in AMOSA: Application to multiobjective 0/1 knapsack problem and clustering gene expression data. Appl. Soft Comput. 13 (5) (2013)
Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara, Sanjoy Kumar Saha
Genre-Based Classification of Song Using Perceptual Features. ICACNI (2013)
Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara, Sanjoy Kumar Saha
Song/instrumental classification using spectrogram based contextual features. CUBE (2012)
Sriram Srinivasan, Sourangshu Bhattacharya, Rudrasis Chakraborty
Segmenting web-domains and hashtags using length specific models. CIKM (2012)
Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara, Sanjoy Kumar Saha
Song Classification: Classical and Non-classical Discrimination Using MFCC Co-occurrence Based Features. FGIT-SIP (2011)