← Authors

Rudrasis Chakraborty

19 papers · 2015–2022 · 7 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (13) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (43) 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (7) 🌈 Renaissance Researcher (6) 🀝 Dynamic Duo (13) 🧬 Topic Evolution πŸ“ˆ Trend Setter ⚑ Prolific Year (5) πŸ’Ž Century Club (19) πŸ—ƒοΈ Keyword Collector (90) πŸ”₯ Unstoppable (8)

Conferences

CVPR (7) ICCV (5) AAAI (2) NIPS (2) ECCV (1) ICML (1) UAI (1)

Top co-authors

Vikas Singh (13) Baba C. Vemuri (6) Xingjian Zhen (5) Monami Banerjee (3) Songwong Tasneeyapant (2) Zhichun Huang (2) Yunyang Xiong (2) Zihang Meng (2) Jurijs Nazarovs (2) Michael S. Okun (1)

Keywords

manifold learning (3) riemannian manifold (3) manifold-valued datum (2) grassmann manifold (2) frechet mean (2) convolutional neural network (2) brain imaging (2) kernel methods (2) domain adaptation (1) robust classification (1) image classification (1) image reconstruction (1) adversarial training (1) geometric deep learning (1) medical imaging (1) uncertainty quantification (1) image generation (1) statistical testing (1) hypothesis testing (1) computer vision (1)

Papers

On the Versatile Uses of Partial Distance Correlation in Deep Learning ECCV 2022
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets CVPR 2022
Understanding Uncertainty Maps in Vision With Statistical Testing CVPR 2022
Forward Operator Estimation in Generative Models with Kernel Transfer Operators ICML 2022
Simpler Certified Radius Maximization by Propagating Covariances CVPR 2021
A variational approximation for analyzing the dynamics of panel data UAI 2021
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis NIPS 2021
Flow-based Generative Models for Learning Manifold to Manifold Mappings AAAI 2021
NystrΓΆmformer: A NystrΓΆm-based Algorithm for Approximating Self-Attention AAAI 2021
Orthogonal Convolutional Neural Networks CVPR 2020
Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains ICCV 2019
Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data ICCV 2019
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices NIPS 2018
A Geometric Framework for Statistical Analysis of Trajectories With Distinct Temporal Spans ICCV 2017
Sparse Exact PGA on Riemannian Manifolds ICCV 2017
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning CVPR 2017
A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis CVPR 2016
An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds CVPR 2016
Recursive Frechet Mean Computation on the Grassmannian and its Applications to Computer Vision ICCV 2015