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ORCIDRudrasis Chakraborty
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2020 – today
- 2024
[i29]Jurijs Nazarovs, Zhichun Huang, Xingjian Zhen, Sourav Pal, Rudrasis Chakraborty, Vikas Singh:
Variational Sampling of Temporal Trajectories. CoRR abs/2403.11418 (2024)- 2022
- [j10]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): 799-810 (2022) - [j9]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): 823-833 (2022) - [j8]Jiayun Wang, Rudrasis Chakraborty, Stella X. Yu:
Transformer for 3D Point Clouds. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 4419-4431 (2022) - [j7]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): 940-951 (2022) - [c35]Jurijs Nazarovs, Zhichun Huang, Songwong Tasneeyapant, Rudrasis Chakraborty, Vikas Singh:
Understanding Uncertainty Maps in Vision with Statistical Testing. CVPR 2022: 406-416 - [c34]Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh:
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets. CVPR 2022: 10422-10431 - [c33]Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh:
On the Versatile Uses of Partial Distance Correlation in Deep Learning. ECCV (26) 2022: 327-346 - [c32]Zhichun Huang, Rudrasis Chakraborty, Vikas Singh:
Forward Operator Estimation in Generative Models with Kernel Transfer Operators. ICML 2022: 9148-9172 - [i28]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 abs/2202.09463 (2022) - [i27]Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh:
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets. CoRR abs/2203.15234 (2022) - [i26]Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh:
On the Versatile Uses of Partial Distance Correlation in Deep Learning. CoRR abs/2207.09684 (2022) - 2021
- [j6]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): 3904-3917 (2021) - [c31]Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh:
Flow-based Generative Models for Learning Manifold to Manifold Mappings. AAAI 2021: 11042-11052 - [c30]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: 14138-14148 - [c29]Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh:
Simpler Certified Radius Maximization by Propagating Covariances. CVPR 2021: 7292-7301 - [c28]Zihang Meng, Rudrasis Chakraborty, Vikas Singh:
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. NeurIPS 2021: 14056-14068 - [c27]Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh:
A variational approximation for analyzing the dynamics of panel data. UAI 2021: 107-117 - [i25]Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh:
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention. CoRR abs/2102.03902 (2021) - [i24]Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh:
Simpler Certified Radius Maximization by Propagating Covariances. CoRR abs/2104.05888 (2021) - [i23]Zihang Meng, Rudrasis Chakraborty, Vikas Singh:
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis. CoRR abs/2106.07479 (2021) - [i22]Monami Banerjee, Rudrasis Chakraborty, Jose Bouza, Baba C. Vemuri:
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds. CoRR abs/2106.15301 (2021) - [i21]Zhichun Huang, Rudrasis Chakraborty, Vikas Singh:
Forward Operator Estimation in Generative Models with Kernel Transfer Operators. CoRR abs/2112.00305 (2021) - 2020
- [c26]Rudrasis Chakraborty, Yifei Xing, Minxuan Duan, Stella X. Yu:
C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning. CVPR Workshops 2020: 360-367 - [c25]Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu:
Orthogonal Convolutional Neural Networks. CVPR 2020: 11502-11512 - [c24]Liu Yang, Rudrasis Chakraborty:
A GMM Based Algorithm To Generate Point-Cloud And Its Application To Neuroimaging. ISBI Workshops 2020: 1-4 - [c23]Liu Yang, Rudrasis Chakraborty:
An "Augmentation-Free" Rotation Invariant Classification Scheme on Point-Cloud and Its Application to Neuroimaging. ISBI 2020: 713-716 - [i20]Rudrasis Chakraborty:
ManifoldNorm: Extending normalizations on Riemannian Manifolds. CoRR abs/2003.13869 (2020) - [i19]Yifei Xing, Rudrasis Chakraborty, Minxuan Duan, Stella X. Yu:
C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning. CoRR abs/2006.12590 (2020) - [i18]Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh:
Flow-based Generative Models for Learning Manifold to Manifold Mappings. CoRR abs/2012.10013 (2020)
2010 – 2019
- 2019
- [c22]Xinlei Pan, Rudrasis Chakraborty, Stella X. Yu:
Visual Similarity from Optimizing Feature and Memory On A Hypersphere. CVPR Workshops 2019: 13-16 - [c21]Rudrasis Chakraborty, Jiayun Wang, Stella X. Yu:
Sur-Real: Frechet Mean and Distance Transform for Complex-Valued Deep Learning. CVPR Workshops 2019: 889-897 - [c20]Ronak Mehta, Rudrasis Chakraborty, Vikas Singh, Yunyang Xiong:
Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains. ICCV 2019: 10570-10578 - [c19]Rudrasis Chakraborty, Xingjian Zhen, Nicholas Vogt, Barbara B. Bendlin, Vikas Singh:
Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data. ICCV 2019: 10620-10630 - [c18]Rudrasis Chakraborty, Jose Bouza, Jonathan H. Manton, Baba C. Vemuri:
A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging. IPMI 2019: 112-124 - [c17]Monami Banerjee, Rudrasis Chakraborty, Derek B. Archer, David E. Vaillancourt, Baba C. Vemuri:
DMR-CNN: A CNN Tailored For DMR Scans With Applications To PD Classification. ISBI 2019: 388-391 - [i17]Rudrasis Chakraborty, Jiayun Wang, Stella X. Yu:
SurReal: Fréchet Mean and Distance Transform for Complex-Valued Deep Learning. CoRR abs/1906.10048 (2019) - [i16]Jiayun Wang, Rudrasis Chakraborty, Stella X. Yu:
Spatial Transformer for 3D Points. CoRR abs/1906.10887 (2019) - [i15]Xingjian Zhen, Rudrasis Chakraborty, Nicholas Vogt, Barbara B. Bendlin, Vikas Singh:
Dilated Convolutional Neural Networks for Sequential Manifold-valued Data. CoRR abs/1910.02206 (2019) - [i14]Rudrasis Chakraborty, Yifei Xing, Stella X. Yu:
Surreal: Complex-Valued Deep Learning as Principled Transformations on a Rotational Lie Group. CoRR abs/1910.11334 (2019) - [i13]Liu Yang, Rudrasis Chakraborty, Stella X. Yu:
POIRot: A rotation invariant omni-directional pointnet. CoRR abs/1910.13050 (2019) - [i12]Liu Yang, Rudrasis Chakraborty:
A GMM based algorithm to generate point-cloud and its application to neuroimaging. CoRR abs/1911.01705 (2019) - [i11]Liu Yang, Rudrasis Chakraborty:
An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging. CoRR abs/1911.03443 (2019) - [i10]Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu:
Orthogonal Convolutional Neural Networks. CoRR abs/1911.12207 (2019) - 2018
- [c16]Rudrasis Chakraborty, Chun-Hao Yang, Baba C. Vemuri:
A Mixture Model for Aggregation of Multiple Pre-Trained Weak Classifiers. CVPR Workshops 2018: 341-348 - [c15]Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek B. Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri:
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices. NeurIPS 2018: 8897-8908 - [c14]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 - [i9]Indrasis Chakraborty, Rudrasis Chakraborty, Draguna L. Vrabie:
Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems. CoRR abs/1804.05320 (2018) - [i8]Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri:
Dictionary Learning and Sparse Coding on Statistical Manifolds. CoRR abs/1805.02505 (2018) - [i7]Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri:
H-CNNs: Convolutional Neural Networks for Riemannian Homogeneous Spaces. CoRR abs/1805.05487 (2018) - [i6]Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek B. Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri:
Statistical Recurrent Models on Manifold valued Data. CoRR abs/1805.11204 (2018) - [i5]Rudrasis Chakraborty, Chun-Hao Yang, Baba C. Vemuri:
A mixture model for aggregation of multiple pre-trained weak classifiers. CoRR abs/1806.00003 (2018) - [i4]Rudrasis Chakraborty, Jose Bouza, Jonathan H. Manton, Baba C. Vemuri:
ManifoldNet: A Deep Network Framework for Manifold-valued Data. CoRR abs/1809.06211 (2018) - 2017
- [c13]Rudrasis Chakraborty, Søren Hauberg, Baba C. Vemuri:
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. CVPR 2017: 801-809 - [c12]Rudrasis Chakraborty, Vikas Singh, Nagesh Adluru, Baba C. Vemuri:
A Geometric Framework for Statistical Analysis of Trajectories with Distinct Temporal Spans. ICCV 2017: 172-181 - [c11]Monami Banerjee, Rudrasis Chakraborty, Baba C. Vemuri:
Sparse Exact PGA on Riemannian Manifolds. ICCV 2017: 5020-5028 - [c10]Rudrasis Chakraborty, Baba C. Vemuri:
Statistical analysis of longitudinal data and applications to neuro-imaging. ICIP 2017: 211-214 - [c9]Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri:
Statistics on the space of trajectories for longitudinal data analysis. ISBI 2017: 999-1002 - [i3]Rudrasis Chakraborty, Søren Hauberg, Baba C. Vemuri:
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. CoRR abs/1702.01005 (2017) - [i2]Rudrasis Chakraborty, Baba C. Vemuri:
Statistics on the (compact) Stiefel manifold: Theory and Applications. CoRR abs/1708.00045 (2017) - 2016
- [c8]Rudrasis Chakraborty, Dohyung Seo, Baba C. Vemuri:
An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds. CVPR 2016: 3976-3984 - [c7]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: 4424-4432 - [i1]Rudrasis Chakraborty, Dohyung Seo, Baba C. Vemuri:
An efficient Exact-PGA algorithm for constant curvature manifolds. CoRR abs/1603.03984 (2016) - 2015
- [j5]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): 31-49 (2015) - [j4]Sanghamitra Bandyopadhyay, Rudrasis Chakraborty, Ujjwal Maulik:
Priority based ∈ dominance: A new measure in multiobjective optimization. Inf. Sci. 305: 97-109 (2015) - [j3]Rudrasis Chakraborty, Nikhil R. Pal:
Feature Selection Using a Neural Framework With Controlled Redundancy. IEEE Trans. Neural Networks Learn. Syst. 26(1): 35-50 (2015) - [c6]Rudrasis Chakraborty, Baba C. Vemuri:
Recursive Fréchet Mean Computation on the Grassmannian and Its Applications to Computer Vision. ICCV 2015: 4229-4237 - [c5]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: 719-727 - 2014
- [j2]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) - 2013
- [j1]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): 2405-2411 (2013) - [c4]Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara, Sanjoy Kumar Saha:
Genre-Based Classification of Song Using Perceptual Features. ICACNI 2013: 267-276 - 2012
- [c3]Sriram Srinivasan, Sourangshu Bhattacharya, Rudrasis Chakraborty:
Segmenting web-domains and hashtags using length specific models. CIKM 2012: 1113-1122 - [c2]Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara, Sanjoy Kumar Saha:
Song/instrumental classification using spectrogram based contextual features. CUBE 2012: 21-25 - 2011
- [c1]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: 179-185
Coauthor Index
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last updated on 2024-10-07 21:16 CEST by the dblp team
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