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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Nystr$\\$\" omformer: A Nystr$\\$\" om-Based Algorithm for Approximating Self-Attention.\n \n \n \n\n\n \n Xiong, Y.; Zeng, Z.; Chakraborty, R.; Tan, M.; Fung, G.; Li, Y.; and Singh, V.\n\n\n \n\n\n\n arXiv preprint arXiv:2102.03902. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{xiong2021nystr,\n  title={Nystr$\\backslash$" omformer: A Nystr$\\backslash$" om-Based Algorithm for Approximating Self-Attention},\n  author={Xiong, Yunyang and Zeng, Zhanpeng and Chakraborty, Rudrasis and Tan, Mingxing and Fung, Glenn and Li, Yin and Singh, Vikas},\n  journal={arXiv preprint arXiv:2102.03902},\n  year={2021}\n}\n
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\n  \n 2020\n \n \n (13)\n \n \n
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\n \n\n \n \n \n \n \n Efficient recursive estimation of the Riemannian barycenter on the hypersphere and the special orthogonal group with applications.\n \n \n \n\n\n \n Chakraborty, R.; and Vemuri, B. C\n\n\n \n\n\n\n In Riemannian Geometric Statistics in Medical Image Analysis, pages 273–297. Academic Press, 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{chakraborty2020efficient,\n  title={Efficient recursive estimation of the Riemannian barycenter on the hypersphere and the special orthogonal group with applications},\n  author={Chakraborty, Rudrasis and Vemuri, Baba C},\n  booktitle={Riemannian Geometric Statistics in Medical Image Analysis},\n  pages={273--297},\n  year={2020},\n  publisher={Academic Press}\n}\n\n
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\n \n\n \n \n \n \n \n A GMM based algorithm to generate point-cloud and its application to neuroimaging.\n \n \n \n\n\n \n Yang, L.; and Chakraborty, R.\n\n\n \n\n\n\n In 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), pages 1–4, 2020. IEEE\n \n\n\n\n
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@inproceedings{yang2020gmm,\n  title={A GMM based algorithm to generate point-cloud and its application to neuroimaging},\n  author={Yang, Liu and Chakraborty, Rudrasis},\n  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)},\n  pages={1--4},\n  year={2020},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n An “Augmentation-Free” Rotation Invariant Classification Scheme on Point-Cloud and Its Application to Neuroimaging.\n \n \n \n\n\n \n Yang, L.; and Chakraborty, R.\n\n\n \n\n\n\n In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pages 713–716, 2020. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{yang2020augmentation,\n  title={An “Augmentation-Free” Rotation Invariant Classification Scheme on Point-Cloud and Its Application to Neuroimaging},\n  author={Yang, Liu and Chakraborty, Rudrasis},\n  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},\n  pages={713--716},\n  year={2020},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n Orthogonal Convolutional Neural Networks.\n \n \n \n\n\n \n Wang, J.; Chen, Y.; Chakraborty, R.; and Yu, S. X\n\n\n \n\n\n\n In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11505–11515, 2020. \n \n\n\n\n
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@inproceedings{wang2020orthogonal,\n  title={Orthogonal Convolutional Neural Networks},\n  author={Wang, Jiayun and Chen, Yubei and Chakraborty, Rudrasis and Yu, Stella X},\n  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n  pages={11505--11515},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning.\n \n \n \n\n\n \n Chakraborty, R.; Yang, L.; Hauberg, S.; and Vemuri, B.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020.\n \n\n\n\n
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@article{chakraborty2020intrinsic,\n  title={Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning},\n  author={Chakraborty, Rudrasis and Yang, Liu and Hauberg, Soren and Vemuri, Baba},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n  year={2020},\n  publisher={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n ManifoldNorm: Extending normalizations on Riemannian Manifolds.\n \n \n \n\n\n \n Chakraborty, R.\n\n\n \n\n\n\n arXiv preprint arXiv:2003.13869. 2020.\n \n\n\n\n
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@article{chakraborty2020manifoldnorm,\n  title={ManifoldNorm: Extending normalizations on Riemannian Manifolds},\n  author={Chakraborty, Rudrasis},\n  journal={arXiv preprint arXiv:2003.13869},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning.\n \n \n \n\n\n \n Chakraborty, R.; Xing, Y.; Duan, M.; and Yu, S. X\n\n\n \n\n\n\n In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 80–81, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{chakraborty2020c,\n  title={C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning},\n  author={Chakraborty, Rudrasis and Xing, Yifei and Duan, Minxuan and Yu, Stella X},\n  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},\n  pages={80--81},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n Meibomian Gland Morphology and Ghost Prediction with Deep Learning.\n \n \n \n\n\n \n Wang, J.; Li, S.; Yeh, T. N; Chakraborty, R.; Yu, S.; and Lin, M. C\n\n\n \n\n\n\n Investigative Ophthalmology & Visual Science, 61(7): 2634–2634. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wang2020meibomian,\n  title={Meibomian Gland Morphology and Ghost Prediction with Deep Learning},\n  author={Wang, Jiayun and Li, Shixuan and Yeh, Thao N and Chakraborty, Rudrasis and Yu, Stella and Lin, Meng C},\n  journal={Investigative Ophthalmology \\& Visual Science},\n  volume={61},\n  number={7},\n  pages={2634--2634},\n  year={2020},\n  publisher={The Association for Research in Vision and Ophthalmology}\n}\n\n
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\n \n\n \n \n \n \n \n C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning.\n \n \n \n\n\n \n Xing, Y.; Chakraborty, R.; Duan, M.; and Yu, S.\n\n\n \n\n\n\n arXiv preprint arXiv:2006.12590. 2020.\n \n\n\n\n
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@article{xing2020c,\n  title={C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning},\n  author={Xing, Yifei and Chakraborty, Rudrasis and Duan, Minxuan and Yu, Stella},\n  journal={arXiv preprint arXiv:2006.12590},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n Manifoldnet: A deep neural network for manifold-valued data with applications.\n \n \n \n\n\n \n Chakraborty, R.; Bouza, J.; Manton, J.; and Vemuri, B. C\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020.\n \n\n\n\n
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@article{chakraborty2020manifoldnet,\n  title={Manifoldnet: A deep neural network for manifold-valued data with applications},\n  author={Chakraborty, Rudrasis and Bouza, Jose and Manton, Jonathan and Vemuri, Baba C},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n  year={2020},\n  publisher={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold.\n \n \n \n\n\n \n Chakraborty, R.; Xing, Y.; and Stella, X Y.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems. 2020.\n \n\n\n\n
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@article{chakraborty2020surreal,\n  title={SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold},\n  author={Chakraborty, Rudrasis and Xing, Yifei and Stella, X Yu},\n  journal={IEEE Transactions on Neural Networks and Learning Systems},\n  year={2020},\n  publisher={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n VolterraNet: A higher order convolutionalnetwork with group equivariance forhomogeneous manifolds.\n \n \n \n\n\n \n Banerjee, M.; Chakraborty, R.; Bouza, J.; and Vemuri, B. C\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, (01): 1–1. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{banerjee2020volterranet,\n  title={VolterraNet: A higher order convolutionalnetwork with group equivariance forhomogeneous manifolds},\n  author={Banerjee, Monami and Chakraborty, Rudrasis and Bouza, Jose and Vemuri, Baba C},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n  number={01},\n  pages={1--1},\n  year={2020},\n  publisher={IEEE Computer Society}\n}\n\n
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\n \n\n \n \n \n \n \n Flow-based Generative Models for Learning Manifold to Manifold Mappings.\n \n \n \n\n\n \n Zhen, X.; Chakraborty, R.; Yang, L.; and Singh, V.\n\n\n \n\n\n\n arXiv preprint arXiv:2012.10013. 2020.\n \n\n\n\n
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@article{zhen2020flow,\n  title={Flow-based Generative Models for Learning Manifold to Manifold Mappings},\n  author={Zhen, Xingjian and Chakraborty, Rudrasis and Yang, Liu and Singh, Vikas},\n  journal={arXiv preprint arXiv:2012.10013},\n  year={2020}\n}\n\n
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\n  \n 2019\n \n \n (14)\n \n \n
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\n \n\n \n \n \n \n \n Statistics on the Stiefel manifold: theory and applications.\n \n \n \n\n\n \n Chakraborty, R.; Vemuri, B. C; and others\n\n\n \n\n\n\n The Annals of Statistics, 47(1): 415–438. 2019.\n \n\n\n\n
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@article{chakraborty2019statistics,\n  title={Statistics on the Stiefel manifold: theory and applications},\n  author={Chakraborty, Rudrasis and Vemuri, Baba C and others},\n  journal={The Annals of Statistics},\n  volume={47},\n  number={1},\n  pages={415--438},\n  year={2019},\n  publisher={Institute of Mathematical Statistics}\n}\n\n
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\n \n\n \n \n \n \n \n A deep neural network for manifold-valued data with applications to neuroimaging.\n \n \n \n\n\n \n Chakraborty, R.; Bouza, J.; Manton, J.; and Vemuri, B. C\n\n\n \n\n\n\n In International Conference on Information Processing in Medical Imaging, pages 112–124, 2019. Springer, Cham\n \n\n\n\n
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@inproceedings{chakraborty2019deep,\n  title={A deep neural network for manifold-valued data with applications to neuroimaging},\n  author={Chakraborty, Rudrasis and Bouza, Jose and Manton, Jonathan and Vemuri, Baba C},\n  booktitle={International Conference on Information Processing in Medical Imaging},\n  pages={112--124},\n  year={2019},\n  organization={Springer, Cham}\n}\n\n
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\n \n\n \n \n \n \n \n Visual Similarity from Optimizing Feature and Memory On A Hypersphere.\n \n \n \n\n\n \n Pan, X.; Chakraborty, R.; and Yu, S.\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 13–16, 2019. \n \n\n\n\n
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@inproceedings{pan2019visual,\n  title={Visual Similarity from Optimizing Feature and Memory On A Hypersphere},\n  author={Pan, Xinlei and Chakraborty, Rudrasis and Yu, Stella},\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},\n  pages={13--16},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n Sur-real: Frechet mean and distance transform for complex-valued deep learning.\n \n \n \n\n\n \n Chakraborty, R.; Wang, J.; and Yu, S. X\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. \n \n\n\n\n
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@inproceedings{chakraborty2019real,\n  title={Sur-real: Frechet mean and distance transform for complex-valued deep learning},\n  author={Chakraborty, Rudrasis and Wang, Jiayun and Yu, Stella X},\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},\n  pages={0--0},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n Dmr-cnn: A cnn tailored for dmr scans with applications to pd classification.\n \n \n \n\n\n \n Banerjee, M.; Chakraborty, R.; Archer, D.; Vaillancourt, D.; and Vemuri, B. C\n\n\n \n\n\n\n In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pages 388–391, 2019. IEEE\n \n\n\n\n
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@inproceedings{banerjee2019dmr,\n  title={Dmr-cnn: A cnn tailored for dmr scans with applications to pd classification},\n  author={Banerjee, Monami and Chakraborty, Rudrasis and Archer, Derek and Vaillancourt, David and Vemuri, Baba C},\n  booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},\n  pages={388--391},\n  year={2019},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n Dilated convolutional neural networks for sequential manifold-valued data.\n \n \n \n\n\n \n Zhen, X.; Chakraborty, R.; Vogt, N.; Bendlin, B. B; and Singh, V.\n\n\n \n\n\n\n In Proceedings of the IEEE International Conference on Computer Vision, pages 10621–10631, 2019. \n \n\n\n\n
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@inproceedings{zhen2019dilated,\n  title={Dilated convolutional neural networks for sequential manifold-valued data},\n  author={Zhen, Xingjian and Chakraborty, Rudrasis and Vogt, Nicholas and Bendlin, Barbara B and Singh, Vikas},\n  booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n  pages={10621--10631},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n Surreal: Complex-Valued Deep Learning as Principled Transformations on a Rotational Lie Group.\n \n \n \n\n\n \n Chakraborty, R.; Xing, Y.; and Yu, S.\n\n\n \n\n\n\n arXiv preprint arXiv:1910.11334. 2019.\n \n\n\n\n
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@article{chakraborty2019surreal,\n  title={Surreal: Complex-Valued Deep Learning as Principled Transformations on a Rotational Lie Group},\n  author={Chakraborty, Rudrasis and Xing, Yifei and Yu, Stella},\n  journal={arXiv preprint arXiv:1910.11334},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n POIRot: A rotation invariant omni-directional pointnet.\n \n \n \n\n\n \n Yang, L.; Chakraborty, R.; and Yu, S. X\n\n\n \n\n\n\n arXiv preprint arXiv:1910.13050. 2019.\n \n\n\n\n
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@article{yang2019poirot,\n  title={POIRot: A rotation invariant omni-directional pointnet},\n  author={Yang, Liu and Chakraborty, Rudrasis and Yu, Stella X},\n  journal={arXiv preprint arXiv:1910.13050},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains.\n \n \n \n\n\n \n Mehta, R.; Chakraborty, R.; Xiong, Y.; and Singh, V.\n\n\n \n\n\n\n In Proceedings of the IEEE International Conference on Computer Vision, pages 10571–10579, 2019. \n \n\n\n\n
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@inproceedings{mehta2019scaling,\n  title={Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains},\n  author={Mehta, Ronak and Chakraborty, Rudrasis and Xiong, Yunyang and Singh, Vikas},\n  booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n  pages={10571--10579},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n Spatial Transformer for 3D Point Clouds.\n \n \n \n\n\n \n Jiayun, W.; Rudrasis, C.; and Stella X., Y.\n\n\n \n\n\n\n In 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{jiayun2019spatial,\n  title={Spatial Transformer for 3D Point Clouds},\n  author={Jiayun, Wang and Rudrasis, Chakraborty and Stella X., Yu},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images.\n \n \n \n\n\n \n Wang, J.; Yeh, T. N; Chakraborty, R.; Stella, X Y.; and Lin, M. C\n\n\n \n\n\n\n Translational vision science & technology, 8(6): 37–37. 2019.\n \n\n\n\n
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@article{wang2019deep,\n  title={A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images},\n  author={Wang, Jiayun and Yeh, Thao N and Chakraborty, Rudrasis and Stella, X Yu and Lin, Meng C},\n  journal={Translational vision science \\& technology},\n  volume={8},\n  number={6},\n  pages={37--37},\n  year={2019},\n  publisher={The Association for Research in Vision and Ophthalmology}\n}\n\n
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\n \n\n \n \n \n \n \n Spatial Transformer for 3D Points.\n \n \n \n\n\n \n Wang, J.; Chakraborty, R.; and Yu, S. X\n\n\n \n\n\n\n arXiv preprint arXiv:1906.10887. 2019.\n \n\n\n\n
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@article{wang2019spatial,\n  title={Spatial Transformer for 3D Points},\n  author={Wang, Jiayun and Chakraborty, Rudrasis and Yu, Stella X},\n  journal={arXiv preprint arXiv:1906.10887},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n SurReal: Fr$\\$'echet Mean and Distance Transform for Complex-Valued Deep Learning.\n \n \n \n\n\n \n Chakraborty, R.; Wang, J.; and Yu, S. X\n\n\n \n\n\n\n arXiv preprint arXiv:1906.10048. 2019.\n \n\n\n\n
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@article{chakraborty2019surreal,\n  title={SurReal: Fr$\\backslash$'echet Mean and Distance Transform for Complex-Valued Deep Learning},\n  author={Chakraborty, Rudrasis and Wang, Jiayun and Yu, Stella X},\n  journal={arXiv preprint arXiv:1906.10048},\n  year={2019}\n}\n\n
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\n \n\n \n \n \n \n \n Spatial Transformer for 3D Point Clouds.\n \n \n \n\n\n \n Wang, J.; Chakraborty, R.; and Yu, S. X\n\n\n \n\n\n\n arXiv,arXiv–1906. 2019.\n \n\n\n\n
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@article{wang2019spatial,\n  title={Spatial Transformer for 3D Point Clouds},\n  author={Wang, Jiayun and Chakraborty, Rudrasis and Yu, Stella X},\n  journal={arXiv},\n  pages={arXiv--1906},\n  year={2019}\n}\n\n
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\n  \n 2018\n \n \n (10)\n \n \n
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\n \n\n \n \n \n \n \n H-cnns: Convolutional neural networks for riemannian homogeneous spaces.\n \n \n \n\n\n \n Chakraborty, R.; Banerjee, M.; and Vemuri, B. C\n\n\n \n\n\n\n arXiv preprint arXiv:1805.05487, 1. 2018.\n \n\n\n\n
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@article{chakraborty2018h,\n  title={H-cnns: Convolutional neural networks for riemannian homogeneous spaces},\n  author={Chakraborty, Rudrasis and Banerjee, Monami and Vemuri, Baba C},\n  journal={arXiv preprint arXiv:1805.05487},\n  volume={1},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulation.\n \n \n \n\n\n \n Chakraborty, S.; Gilyén, A.; and Jeffery, S.\n\n\n \n\n\n\n arXiv preprint arXiv:1804.01973. 2018.\n \n\n\n\n
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@article{chakraborty2018power,\n  title={The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulation},\n  author={Chakraborty, Shantanav and Gily{\\'e}n, Andr{\\'a}s and Jeffery, Stacey},\n  journal={arXiv preprint arXiv:1804.01973},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n Dictionary Learning and Sparse Coding on Statistical Manifolds.\n \n \n \n\n\n \n Chakraborty, R.; Banerjee, M.; and Vemuri, B. C\n\n\n \n\n\n\n arXiv preprint arXiv:1805.02505. 2018.\n \n\n\n\n
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@article{chakraborty2018dictionary,\n  title={Dictionary Learning and Sparse Coding on Statistical Manifolds},\n  author={Chakraborty, Rudrasis and Banerjee, Monami and Vemuri, Baba C},\n  journal={arXiv preprint arXiv:1805.02505},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n Statistical recurrent models on manifold valued data.\n \n \n \n\n\n \n Chakraborty, R.; Yang, C.; Zhen, X.; Banerjee, M.; Archer, D.; Vaillancourt, D.; Singh, V.; and Vemuri, B. C\n\n\n \n\n\n\n ArXiv e-prints. 2018.\n \n\n\n\n
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@article{chakraborty2018statistical,\n  title={Statistical recurrent models on manifold valued data},\n  author={Chakraborty, Rudrasis and Yang, Chun-Hao and Zhen, Xingjian and Banerjee, Monami and Archer, Derek and Vaillancourt, David and Singh, Vikas and Vemuri, Baba C},\n  journal={ArXiv e-prints},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n A mixture model for aggregation of multiple pre-trained weak classifiers.\n \n \n \n\n\n \n Chakraborty, R.; Yang, C.; and Vemuri, B. C\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 341–348, 2018. \n \n\n\n\n
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@inproceedings{chakraborty2018mixture,\n  title={A mixture model for aggregation of multiple pre-trained weak classifiers},\n  author={Chakraborty, Rudrasis and Yang, Chun-Hao and Vemuri, Baba C},\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},\n  pages={341--348},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n Manifoldnet: A deep network framework for manifold-valued data.\n \n \n \n\n\n \n Chakraborty, R.; Bouza, J.; Manton, J.; and Vemuri, B. C\n\n\n \n\n\n\n arXiv preprint arXiv:1809.06211. 2018.\n \n\n\n\n
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@article{chakraborty2018manifoldnet,\n  title={Manifoldnet: A deep network framework for manifold-valued data},\n  author={Chakraborty, Rudrasis and Bouza, Jose and Manton, Jonathan and Vemuri, Baba C},\n  journal={arXiv preprint arXiv:1809.06211},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n A statistical recurrent model on the manifold of symmetric positive definite matrices.\n \n \n \n\n\n \n Chakraborty, R.; Yang, C.; Zhen, X.; Banerjee, M.; Archer, D.; Vaillancourt, D.; Singh, V.; and Vemuri, B.\n\n\n \n\n\n\n Advances in Neural Information Processing Systems, 31: 8883–8894. 2018.\n \n\n\n\n
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@article{chakraborty2018statistical,\n  title={A statistical recurrent model on the manifold of symmetric positive definite matrices},\n  author={Chakraborty, Rudrasis and Yang, Chun-Hao and Zhen, Xingjian and Banerjee, Monami and Archer, Derek and Vaillancourt, David and Singh, Vikas and Vemuri, Baba},\n  journal={Advances in Neural Information Processing Systems},\n  volume={31},\n  pages={8883--8894},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA.\n \n \n \n\n\n \n Chakraborty, R.; Bouza, J.; Manton, J.; and Vemuri, B. C\n\n\n \n\n\n\n . 2018.\n \n\n\n\n
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@article{chakraborty2018manifoldnet,\n  title={MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA},\n  author={Chakraborty, Rudrasis and Bouza, Jose and Manton, Jonathan and Vemuri, Baba C},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n A CNN for homogneous Riemannian manifolds with applications to Neuroimaging.\n \n \n \n\n\n \n Chakraborty, R.; Banerjee, M.; and Vemuri, B. C\n\n\n \n\n\n\n arXiv preprint arXiv:1805.05487. 2018.\n \n\n\n\n
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@article{chakraborty2018cnn,\n  title={A CNN for homogneous Riemannian manifolds with applications to Neuroimaging},\n  author={Chakraborty, Rudrasis and Banerjee, Monami and Vemuri, Baba C},\n  journal={arXiv preprint arXiv:1805.05487},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n Generative adversarial network based autoencoder: Application to fault detection problem for closed loop dynamical systems.\n \n \n \n\n\n \n Chakraborty, I.; Chakraborty, R.; and Vrabie, D.\n\n\n \n\n\n\n arXiv preprint arXiv:1804.05320. 2018.\n \n\n\n\n
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@article{chakraborty2018generative,\n  title={Generative adversarial network based autoencoder: Application to fault detection problem for closed loop dynamical systems},\n  author={Chakraborty, Indrasis and Chakraborty, Rudrasis and Vrabie, Draguna},\n  journal={arXiv preprint arXiv:1804.05320},\n  year={2018}\n}\n\n
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\n \n\n \n \n \n \n \n Intrinsic Grassmann averages for online linear and robust subspace learning.\n \n \n \n\n\n \n Chakraborty, R.; Hauberg, S.; and Vemuri, B. C\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6196–6204, 2017. \n \n\n\n\n
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@inproceedings{chakraborty2017intrinsic,\n  title={Intrinsic Grassmann averages for online linear and robust subspace learning},\n  author={Chakraborty, Rudrasis and Hauberg, Soren and Vemuri, Baba C},\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n  pages={6196--6204},\n  year={2017}\n}\n\n
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\n \n\n \n \n \n \n \n Statistics on the space of trajectories for longitudinal data analysis.\n \n \n \n\n\n \n Chakraborty, R.; Banerjee, M.; and Vemuri, B.\n\n\n \n\n\n\n In Biomedical Imaging (ISBI), 2017 IEEE 14th International Symposium on, pages 999–1002, 2017. IEEE\n \n\n\n\n
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@inproceedings{chakraborty2017statistics,\n  title={Statistics on the space of trajectories for longitudinal data analysis},\n  author={Chakraborty, Rudrasis and Banerjee, Monami and Vemuri, Baba},\n  booktitle={Biomedical Imaging (ISBI), 2017 IEEE 14th International Symposium on},\n  pages={999--1002},\n  year={2017},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n Sparse Exact PGA on Riemannian Manifolds.\n \n \n \n\n\n \n Banerjee, M.; Chakraborty, R.; and Vemuri, B. C\n\n\n \n\n\n\n In Proceedings of the IEEE International Conference on Computer Vision, pages 5010–5018, 2017. \n \n\n\n\n
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@inproceedings{banerjee2017sparse,\n  title={Sparse Exact PGA on Riemannian Manifolds},\n  author={Banerjee, Monami and Chakraborty, Rudrasis and Vemuri, Baba C},\n  booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n  pages={5010--5018},\n  year={2017}\n}\n\n
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\n \n\n \n \n \n \n \n A geometric framework for statistical analysis of trajectories with distinct temporal spans.\n \n \n \n\n\n \n Chakraborty, R.; Singh, V.; Adluru, N.; and Vemuri, B. C\n\n\n \n\n\n\n In Proceedings of the IEEE international conference on computer vision, pages 172–181, 2017. \n \n\n\n\n
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@inproceedings{chakraborty2017geometric,\n  title={A geometric framework for statistical analysis of trajectories with distinct temporal spans},\n  author={Chakraborty, Rudrasis and Singh, Vikas and Adluru, Nagesh and Vemuri, Baba C},\n  booktitle={Proceedings of the IEEE international conference on computer vision},\n  pages={172--181},\n  year={2017}\n}\n\n
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\n \n\n \n \n \n \n \n Inactivation of interferon receptor promotes the establishment of immune privileged tumor microenvironment.\n \n \n \n\n\n \n Katlinski, K. V; Gui, J.; Katlinskaya, Y. V; Ortiz, A.; Chakraborty, R.; Bhattacharya, S.; Carbone, C. J; Beiting, D. P; Girondo, M. A; Peck, A. R; and others\n\n\n \n\n\n\n Cancer cell, 31(2): 194–207. 2017.\n \n\n\n\n
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@article{katlinski2017inactivation,\n  title={Inactivation of interferon receptor promotes the establishment of immune privileged tumor microenvironment},\n  author={Katlinski, Kanstantsin V and Gui, Jun and Katlinskaya, Yuliya V and Ortiz, Angel{\\'\\i}ca and Chakraborty, Riddhita and Bhattacharya, Sabyasachi and Carbone, Christopher J and Beiting, Daniel P and Girondo, Melanie A and Peck, Amy R and others},\n  journal={Cancer cell},\n  volume={31},\n  number={2},\n  pages={194--207},\n  year={2017},\n  publisher={Cell Press}\n}\n\n
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\n \n\n \n \n \n \n \n Statistical analysis of longitudinal data and applications to neuro-imaging.\n \n \n \n\n\n \n Chakraborty, R.; and Vemuri, B. C\n\n\n \n\n\n\n In 2017 IEEE International Conference on Image Processing (ICIP), pages 211–214, 2017. IEEE\n \n\n\n\n
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@inproceedings{chakraborty2017statistical,\n  title={Statistical analysis of longitudinal data and applications to neuro-imaging},\n  author={Chakraborty, Rudrasis and Vemuri, Baba C},\n  booktitle={2017 IEEE International Conference on Image Processing (ICIP)},\n  pages={211--214},\n  year={2017},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds.\n \n \n \n\n\n \n Chakraborty, R.; Banerjee, M.; Crawford, V.; and Vemuri, B. C\n\n\n \n\n\n\n arXiv preprint arXiv:1604.06939. 2016.\n \n\n\n\n
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@article{chakraborty2016information,\n  title={An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds},\n  author={Chakraborty, Rudrasis and Banerjee, Monami and Crawford, Victoria and Vemuri, Baba C},\n  journal={arXiv preprint arXiv:1604.06939},\n  year={2016}\n}\n\n
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\n \n\n \n \n \n \n \n A nonlinear regression technique for manifold valued data with applications to medical image analysis.\n \n \n \n\n\n \n Banerjee, M.; Chakraborty, R.; Ofori, E.; Okun, M. S; Viallancourt, D. E; and Vemuri, B. C\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4424–4432, 2016. \n \n\n\n\n
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@inproceedings{banerjee2016nonlinear,\n  title={A nonlinear regression technique for manifold valued data with applications to medical image analysis},\n  author={Banerjee, Monami and Chakraborty, Rudrasis and Ofori, Edward and Okun, Michael S and Viallancourt, David E and Vemuri, Baba C},\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n  pages={4424--4432},\n  year={2016}\n}\n\n
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\n \n\n \n \n \n \n \n An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds.\n \n \n \n\n\n \n Chakraborty, R.; Seo, D.; and Vemuri, B. C.\n\n\n \n\n\n\n In Proceedings of the IEEE Conference of Computer Vision and Pattern Recognition, pages 3976–3984, 2016. \n \n\n\n\n
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@inproceedings{chakraborty2016efficient,\n  title={An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds},\n  author={Chakraborty, Rudrasis and Seo, Dohyung and Vemuri, Baba C.},\n  booktitle={Proceedings of the IEEE Conference of Computer Vision and Pattern Recognition},\n  pages={3976--3984},\n  year={2016}\n}\n\n
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\n \n\n \n \n \n \n \n Priority based∊ dominance: A new measure in multiobjective optimization.\n \n \n \n\n\n \n Bandyopadhyay, S.; Chakraborty, R.; and Maulik, U.\n\n\n \n\n\n\n Information Sciences, 305: 97–109. 2015.\n \n\n\n\n
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@article{bandyopadhyay2015priority,\n  title={Priority based∊ dominance: A new measure in multiobjective optimization},\n  author={Bandyopadhyay, Sanghamitra and Chakraborty, Rudrasis and Maulik, Ujjwal},\n  journal={Information Sciences},\n  volume={305},\n  pages={97--109},\n  year={2015},\n  publisher={Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Perceptual feature-based song genre classification using RANSAC.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n International Journal of Computational Intelligence Studies, 4(1): 31–49. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ghosal2015perceptual,\n  title={Perceptual feature-based song genre classification using RANSAC},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  journal={International Journal of Computational Intelligence Studies},\n  volume={4},\n  number={1},\n  pages={31--49},\n  year={2015},\n  publisher={Inderscience Publishers (IEL)}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n An efficient recursive estimator of the fréchet mean on a hypersphere with applications to medical image analysis.\n \n \n \n\n\n \n Salehian, H.; Chakraborty, R.; Ofori, E.; Vaillancourt, D.; and Vemuri, B. C\n\n\n \n\n\n\n Mathematical Foundations of Computational Anatomy, 3. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{salehian2015efficient,\n  title={An efficient recursive estimator of the fr{\\'e}chet mean on a hypersphere with applications to medical image analysis},\n  author={Salehian, Hesamoddin and Chakraborty, Rudrasis and Ofori, Edward and Vaillancourt, David and Vemuri, Baba C},\n  journal={Mathematical Foundations of Computational Anatomy},\n  volume={3},\n  year={2015}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n An Efficient Recursive Algorithm for Atlas Construction.\n \n \n \n\n\n \n Chakraborty, R.; Banerjee, M.; Seo, D.; Turner, S.; Fuller, D.; Forder, J.; and Vemuri, B. C\n\n\n \n\n\n\n In Mathematical Foundations of Computational Anatomy, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chakraborty2015efficient,\n  title={An Efficient Recursive Algorithm for Atlas Construction},\n  author={Chakraborty, Rudrasis and Banerjee, Monami and Seo, Dohyung and Turner, Sara and Fuller, David and Forder, John and Vemuri, Baba C},\n  booktitle={Mathematical Foundations of Computational Anatomy},\n  year={2015}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Nonlinear regression on Riemannian manifolds and its applications to Neuro-image analysis.\n \n \n \n\n\n \n Banerjee, M.; Chakraborty, R.; Ofori, E.; Vaillancourt, D.; and Vemuri, B. C\n\n\n \n\n\n\n In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 719–727, 2015. Springer, Cham\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{banerjee2015nonlinear,\n  title={Nonlinear regression on Riemannian manifolds and its applications to Neuro-image analysis},\n  author={Banerjee, Monami and Chakraborty, Rudrasis and Ofori, Edward and Vaillancourt, David and Vemuri, Baba C},\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\n  pages={719--727},\n  year={2015},\n  organization={Springer, Cham}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Recursive frechet mean computation on the grassmannian and its applications to computer vision.\n \n \n \n\n\n \n Chakraborty, R.; and Vemuri, B. C\n\n\n \n\n\n\n In Proceedings of the IEEE International Conference on Computer Vision, pages 4229–4237, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chakraborty2015recursive,\n  title={Recursive frechet mean computation on the grassmannian and its applications to computer vision},\n  author={Chakraborty, Rudrasis and Vemuri, Baba C},\n  booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n  pages={4229--4237},\n  year={2015}\n}\n\n
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\n
\n  \n 2014\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Genre-Based Classification of Song Using Perceptual Features.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n In Intelligent Computing, Networking, and Informatics, pages 267–276. Springer, New Delhi, 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@incollection{ghosal2014genre,\n  title={Genre-Based Classification of Song Using Perceptual Features},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  booktitle={Intelligent Computing, Networking, and Informatics},\n  pages={267--276},\n  year={2014},\n  publisher={Springer, New Delhi}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Sensor (group feature) selection with controlled redundancy in a connectionist framework.\n \n \n \n\n\n \n Chakraborty, R.; Lin, C.; and Pal, N. R\n\n\n \n\n\n\n International journal of neural systems, 24(06): 1450021. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{chakraborty2014sensor,\n  title={Sensor (group feature) selection with controlled redundancy in a connectionist framework},\n  author={Chakraborty, Rudrasis and Lin, Chin-Teng and Pal, Nikhil R},\n  journal={International journal of neural systems},\n  volume={24},\n  number={06},\n  pages={1450021},\n  year={2014},\n  publisher={World Scientific Publishing Company}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Feature selection using a neural framework with controlled redundancy.\n \n \n \n\n\n \n Chakraborty, R.; and Pal, N. R\n\n\n \n\n\n\n IEEE transactions on neural networks and learning systems, 26(1): 35–50. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{chakraborty2014feature,\n  title={Feature selection using a neural framework with controlled redundancy},\n  author={Chakraborty, Rudrasis and Pal, Nikhil R},\n  journal={IEEE transactions on neural networks and learning systems},\n  volume={26},\n  number={1},\n  pages={35--50},\n  year={2014},\n  publisher={IEEE}\n}\n\n
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\n
\n  \n 2013\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Incorporating ϵ-dominance in AMOSA: Application to multiobjective 0/1 knapsack problem and clustering gene expression data.\n \n \n \n\n\n \n Bandyopadhyay, S.; Maulik, U.; and Chakraborty, R.\n\n\n \n\n\n\n Applied Soft Computing, 13(5): 2405–2411. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{bandyopadhyay2013incorporating,\n  title={Incorporating ϵ-dominance in AMOSA: Application to multiobjective 0/1 knapsack problem and clustering gene expression data},\n  author={Bandyopadhyay, Sanghamitra and Maulik, Ujjwal and Chakraborty, Rudrasis},\n  journal={Applied Soft Computing},\n  volume={13},\n  number={5},\n  pages={2405--2411},\n  year={2013},\n  publisher={Elsevier}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n A hierarchical approach for speech-instrumental-song classification.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n SpringerPlus, 2(1): 526. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ghosal2013hierarchical,\n  title={A hierarchical approach for speech-instrumental-song classification},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  journal={SpringerPlus},\n  volume={2},\n  number={1},\n  pages={526},\n  year={2013},\n  publisher={Springer International Publishing}\n}\n\n
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\n  \n 2012\n \n \n (4)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Music classification based on MFCC variants and amplitude variation pattern: a hierarchical approach.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n International Journal of Signal Processing, Image Processing and Pattern Recognition, 5(1): 131–150. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ghosal2012music,\n  title={Music classification based on MFCC variants and amplitude variation pattern: a hierarchical approach},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  journal={International Journal of Signal Processing, Image Processing and Pattern Recognition},\n  volume={5},\n  number={1},\n  pages={131--150},\n  year={2012}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Segmenting web-domains and hashtags using length specific models.\n \n \n \n\n\n \n Srinivasan, S.; Bhattacharya, S.; and Chakraborty, R.\n\n\n \n\n\n\n In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 1113–1122, 2012. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{srinivasan2012segmenting,\n  title={Segmenting web-domains and hashtags using length specific models},\n  author={Srinivasan, Sriram and Bhattacharya, Sourangshu and Chakraborty, Rudrasis},\n  booktitle={Proceedings of the 21st ACM international conference on Information and knowledge management},\n  pages={1113--1122},\n  year={2012}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Song/instrumental classification using spectrogram based contextual features.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n In Proceedings of the CUBE International Information Technology Conference, pages 21–25, 2012. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ghosal2012song,\n  title={Song/instrumental classification using spectrogram based contextual features},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  booktitle={Proceedings of the CUBE International Information Technology Conference},\n  pages={21--25},\n  year={2012}\n}\n\n
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\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Incorporating epsilon-dominance in MultiObjective Optimization: A Study in AMOSA.\n \n \n \n\n\n \n Chakraborty, R.\n\n\n \n\n\n\n Ph.D. Thesis, Indian Statistical Institution, 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{chakraborty2012incorporating,\n  title={Incorporating epsilon-dominance in MultiObjective Optimization: A Study in AMOSA},\n  author={Chakraborty, Rudrasis},\n  year={2012},\n  school={Indian Statistical Institution}\n}\n\n
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\n
\n  \n 2011\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Instrumental/song classification of music signal using ransac.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n In 2011 3rd International Conference on Electronics Computer Technology, volume 1, pages 269–272, 2011. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ghosal2011instrumental,\n  title={Instrumental/song classification of music signal using ransac},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  booktitle={2011 3rd International Conference on Electronics Computer Technology},\n  volume={1},\n  pages={269--272},\n  year={2011},\n  organization={IEEE}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Song classification: Classical and non-classical discrimination using mfcc co-occurrence based features.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n In International Conference on Signal Processing, Image Processing, and Pattern Recognition, pages 179–185, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ghosal2011song,\n  title={Song classification: Classical and non-classical discrimination using mfcc co-occurrence based features},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  booktitle={International Conference on Signal Processing, Image Processing, and Pattern Recognition},\n  pages={179--185},\n  year={2011},\n  organization={Springer, Berlin, Heidelberg}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Automatic identification of instrument type in music signal using wavelet and mfcc.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n In International Conference on Information Processing, pages 560–565, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ghosal2011automatic,\n  title={Automatic identification of instrument type in music signal using wavelet and mfcc},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  booktitle={International Conference on Information Processing},\n  pages={560--565},\n  year={2011},\n  organization={Springer, Berlin, Heidelberg}\n}\n\n
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\n  \n 2010\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Audio Classification.\n \n \n \n\n\n \n Chakraborty, R.\n\n\n \n\n\n\n Ph.D. Thesis, Jadavpur University, 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{chakraborty2010audio,\n  title={Audio Classification},\n  author={Chakraborty, Rudrasis},\n  year={2010},\n  school={Jadavpur University}\n}\n\n
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\n  \n 2009\n \n \n (1)\n \n \n
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\n \n \n
\n \n\n \n \n \n \n \n Speech/music classification using occurrence pattern of zcr and ste.\n \n \n \n\n\n \n Ghosal, A.; Chakraborty, R.; Chakraborty, R.; Haty, S.; Dhara, B. C.; and Saha, S. K.\n\n\n \n\n\n\n In 2009 Third International Symposium on Intelligent Information Technology Application, volume 3, pages 435–438, 2009. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ghosal2009speech,\n  title={Speech/music classification using occurrence pattern of zcr and ste},\n  author={Ghosal, Arijit and Chakraborty, Rudrasis and Chakraborty, Ractim and Haty, Swagata and Dhara, Bibhas Chandra and Saha, Sanjoy Kumar},\n  booktitle={2009 Third International Symposium on Intelligent Information Technology Application},\n  volume={3},\n  pages={435--438},\n  year={2009},\n  organization={IEEE}\n}\n\n
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\n  \n 1996\n \n \n (1)\n \n \n
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\n \n \n
\n \n\n \n \n \n \n \n Notes on genetic analysis of yield component characters in rice.\n \n \n \n\n\n \n Singh, V.; and Chakraborty, R.\n\n\n \n\n\n\n Indian J. of Agri. Sci, 52: 311–316. 1996.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{singh1996notes,\n  title={Notes on genetic analysis of yield component characters in rice},\n  author={Singh, VB and Chakraborty, RC},\n  journal={Indian J. of Agri. Sci},\n  volume={52},\n  pages={311--316},\n  year={1996}\n}\n\n
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\n\n
\n
\n  \n 1986\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n A note on some geophysical parameters of Bihar mica belt, Bihar.\n \n \n \n\n\n \n GHOSAL, A.; DAS, L.; CHAKRABORTY, R.; and BOSE, R.\n\n\n \n\n\n\n . 1986.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ghosal1986note,\n  title={A note on some geophysical parameters of Bihar mica belt, Bihar},\n  author={GHOSAL, AK and DAS, LM and CHAKRABORTY, RN and BOSE, RN},\n  year={1986}\n}\n\n
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\n\n
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\n  \n 1982\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Hierarchical gene diversity analysis and its application to brown trout population data.\n \n \n \n\n\n \n CHAKRABORTY, R.; HAAG, M.; RYMAN, N.; and STAHL, G.\n\n\n \n\n\n\n Hereditas, 97(1): 17–21. 1982.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{chakraborty1982hierarchical,\n  title={Hierarchical gene diversity analysis and its application to brown trout population data},\n  author={CHAKRABORTY, RANAJIT and HAAG, MARIE and RYMAN, NILS and STAHL, GUNNAR},\n  journal={Hereditas},\n  volume={97},\n  number={1},\n  pages={17--21},\n  year={1982},\n  publisher={Blackwell Publishing Ltd Oxford, UK}\n}\n\n
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\n  \n undefined\n \n \n (3)\n \n \n
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\n \n \n
\n \n\n \n \n \n \n \n Our thanks to all those who have helped with this issue of Future Microbiology. Listed below are authors, referees and others who have kindly given their time, effort and expertise; their generosity has helped establish this publication.\n \n \n \n\n\n \n Aggarwal, R; Anaya-Velazquez, F; Appleman, M; Armstrong, W; Bhattacharya, S; Bhowmick, T; Bonifaz, A; Bruun, N; Canton, R; Chai, L; and others\n\n\n \n\n\n\n . .\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{aggarwalour,\n  title={Our thanks to all those who have helped with this issue of Future Microbiology. Listed below are authors, referees and others who have kindly given their time, effort and expertise; their generosity has helped establish this publication.},\n  author={Aggarwal, R and Anaya-Velazquez, F and Appleman, M and Armstrong, W and Bhattacharya, S and Bhowmick, T and Bonifaz, A and Bruun, N and Canton, R and Chai, L and others}\n}\n\n
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\n \n\n \n \n \n \n \n A unified geometric framework for the statistical analysis of trajectories with a distinct number of samples.\n \n \n \n\n\n \n Chakraborty, R.; Adluru, N.; Singh, V.; and Vemuri, B. C\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{chakrabortyunified,\n  title={A unified geometric framework for the statistical analysis of trajectories with a distinct number of samples},\n  author={Chakraborty, Rudrasis and Adluru, Nagesh and Singh, Vikas and Vemuri, Baba C}\n}\n\n
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\n \n\n \n \n \n \n \n Turbulence Modeling and Simulation on Cutting Fluid Flow through a Sudden Contraction Nozzle.\n \n \n \n\n\n \n Chakraborty, R; and Saha, S\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{chakrabortyturbulence,\n  title={Turbulence Modeling and Simulation on Cutting Fluid Flow through a Sudden Contraction Nozzle},\n  author={Chakraborty, R and Saha, S}\n}\n\n
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