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Books and Theses
- 2012
[b1]Archana Venkataraman:
Generative models of brain connectivity for population studies. Massachusetts Institute of Technology, Cambridge, MA, USA, 2012
Journal Articles
- 2024
- [j13]Shadi Albarqouni, Christian F. Baumgartner, Qi Dou, Ender Konukoglu, Bjoern H. Menze, Archana Venkataraman:
Editorial for the Special Issue on the 2022 Medical Imaging with Deep Learning Conference. Medical Image Anal. 98: 103308 (2024) - 2023
- [j12]Ravi Shankar
, Hsi-Wei Hsieh, Nicolas Charon, Archana Venkataraman:
A Diffeomorphic Flow-Based Variational Framework for Multi-Speaker Emotion Conversion. IEEE ACM Trans. Audio Speech Lang. Process. 31: 39-53 (2023) - [j11]Naresh Nandakumar, David Hsu, Raheel Ahmed, Archana Venkataraman:
DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity. IEEE Trans. Biomed. Eng. 70(1): 216-227 (2023) - 2021
- [j10]Jeff Craley, Emily Johnson, Christophe Jouny, Archana Venkataraman:
Automated inter-patient seizure detection using multichannel Convolutional and Recurrent Neural Networks. Biomed. Signal Process. Control. 64: 102360 (2021) - [j9]Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Stewart H. Mostofsky, Mary Beth Nebel, Keri Rosch, Karen Seymour, Deana Crocetti, Hassna Irzan, Michael Hütel, Sébastien Ourselin, Neil Marlow, Andrew Melbourne, Egor Levchenko, Shuo Zhou, Mwiza Kunda, Haiping Lu, Nicha C. Dvornek, Juntang Zhuang, Gideon Pinto, Sandip Samal, Jennings Zhang, Jorge L. Bernal-Rusiel, Rudolph Pienaar, Ai Wern Chung:
Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge. Medical Image Anal. 70: 101972 (2021) - [j8]Naresh Nandakumar, Komal Manzoor, Shruti Agarwal, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman:
Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks. Medical Image Anal. 74: 102203 (2021) - [j7]Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L. Goldman, William Ulrich, Karen Faith Berman, Giuseppe Blasi, Leonardo Fazio, Antonio Rampino, Alessandro Bertolino, Daniel R. Weinberger, Venkata S. Mattay, Archana Venkataraman:
A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space. NeuroImage 238: 118200 (2021) - [j6]Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Joshua Robinson, Nicholas F. Wymbs, Stewart H. Mostofsky, Archana Venkataraman:
Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations. NeuroImage 241: 118388 (2021) - 2020
- [j5]Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart H. Mostofsky, Archana Venkataraman:
A joint network optimization framework to predict clinical severity from resting state functional MRI data. NeuroImage 206 (2020) - [j4]Jeff Craley, Emily Johnson, Archana Venkataraman:
A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy. IEEE Trans. Medical Imaging 39(5): 1404-1418 (2020) - 2016
- [j3]Archana Venkataraman, Daniel Y.-J. Yang, Kevin A. Pelphrey, James S. Duncan:
Bayesian Community Detection in the Space of Group-Level Functional Differences. IEEE Trans. Medical Imaging 35(8): 1866-1882 (2016) - 2013
- [j2]Archana Venkataraman, Marek Kubicki, Polina Golland:
From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder. IEEE Trans. Medical Imaging 32(11): 2078-2098 (2013) - 2012
- [j1]Archana Venkataraman, Yogesh Rathi, Marek Kubicki, Carl-Fredrik Westin, Polina Golland:
Joint Modeling of Anatomical and Functional Connectivity for Population Studies. IEEE Trans. Medical Imaging 31(2): 164-182 (2012)
Conference and Workshop Papers
- 2024
- [c44]Naresh Nandakumar, David Hsu, Raheel Ahmed, Archana Venkataraman:
A Deep Learning Framework To Characterize Noisy Labels In Epileptogenic Zone Localization Using Functional Connectivity. ISBI 2024: 1-5 - [c43]Deeksha M. Shama, Archana Venkataraman:
Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection. UNSURE@MICCAI 2024: 3-13 - 2023
- [c42]Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. Hussain Shuler, Noah J. Cowan, Michael L. Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M. Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal C. Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy D. Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein:
Prospective Learning: Principled Extrapolation to the Future. CoLLAs 2023: 347-357 - [c41]Sarah Wu, Archana Venkataraman, Sayan Ghosal:
GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity. EMBC 2023: 1-4 - [c40]Saurav Chennuri, Sha Lai, Anne Billot, Maria Varkanitsa, Emily J. Braun, Swathi Kiran, Archana Venkataraman, Janusz Konrad, Prakash Ishwar, Margrit Betke:
Fusion Approaches to Predict Post-stroke Aphasia Severity from Multimodal Neuroimaging Data. ICCV (Workshops) 2023: 2636-2645 - [c39]Niharika Shimona D'Souza, Archana Venkataraman:
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds. IPMI 2023: 53-65 - [c38]Naresh Nandakumar, David Hsu, Raheel Ahmed, Archana Venkataraman:
A Deep Learning Framework to Localize the Epileptogenic Zone from Dynamic Functional Connectivity Using a Combined Graph Convolutional and Transformer Network. ISBI 2023: 1-5 - [c37]Deeksha M. Shama, Jiasen Jing, Archana Venkataraman:
DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data. MICCAI (8) 2023: 184-194 - [c36]Ravi Shankar, Archana Venkataraman:
Adaptive Duration Modification of Speech using Masked Convolutional Networks and Open-Loop Time Warping. SSW 2023: 177-183 - 2022
- [c35]Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L. Goldman, William Ulrich, Daniel R. Weinberger, Archana Venkataraman:
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease. ICLR 2022 - [c34]Naresh Nandakumar, Komal Manzoor, Shruti Agarwal, Haris I. Sair, Archana Venkataraman:
RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis. MICCAI (1) 2022: 315-325 - [c33]Jeff Craley, Emily Johnson, Christophe Jouny, David Hsu, Raheel Ahmed, Archana Venkataraman:
SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG. MIDL 2022: 261-281 - 2021
- [c32]Danielle Currey, David Hsu, Raheel Ahmed, Archana Venkataraman, Jeff Craley:
Cross-site Epileptic Seizure Detection Using Convolutional Neural Networks. CISS 2021: 1-6 - [c31]Yu-Chung Peng, Niharika Shimona D'Souza, Brian Bush, Charles Brown, Archana Venkataraman:
Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convoutional-Recurrent Neural Networks. CISS 2021: 1-6 - [c30]Naresh Nandakumar, Komal Manzoor, Shruti Agarwal, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman:
A Multi-scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize the Eloquent Cortex in Brain Tumor Patients. IPMI 2021: 241-252 - [c29]Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman:
A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes. MICCAI (7) 2021: 625-636 - [c28]Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman:
M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations. MIDL 2021: 119-130 - [c27]Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L. Goldman, William Ulrich, Karen Faith Berman, Giuseppe Blasi, Leonardo Fazio, Antonio Rampino, Alessandro Bertolino, Daniel R. Weinberger, Venkata S. Mattay, Archana Venkataraman:
G-MIND: an end-to-end multimodal imaging-genetics framework for biomarker identification and disease classification. Medical Imaging: Image Processing 2021 - 2020
- [c26]Ravi Shankar, Hsi-Wei Hsieh, Nicolas Charon, Archana Venkataraman:
Multi-Speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network. INTERSPEECH 2020: 3391-3395 - [c25]Ravi Shankar, Jacob Sager, Archana Venkataraman:
Non-Parallel Emotion Conversion Using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator. INTERSPEECH 2020: 3396-3400 - [c24]Naresh Nandakumar, Niharika Shimona D'Souza, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman:
A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity. MLCN/RNO-AI@MICCAI 2020: 34-44 - [c23]Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman:
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism. MICCAI (7) 2020: 437-447 - 2019
- [c22]Jacob Sager, Ravi Shankar, Jacob Reinhold, Archana Venkataraman:
VESUS: A Crowd-Annotated Database to Study Emotion Production and Perception in Spoken English. INTERSPEECH 2019: 316-320 - [c21]Ravi Shankar, Archana Venkataraman:
Weakly Supervised Syllable Segmentation by Vowel-Consonant Peak Classification. INTERSPEECH 2019: 644-648 - [c20]Ravi Shankar, Jacob Sager, Archana Venkataraman:
A Multi-Speaker Emotion Morphing Model Using Highway Networks and Maximum Likelihood Objective. INTERSPEECH 2019: 2848-2852 - [c19]Ravi Shankar, Hsi-Wei Hsieh, Nicolas Charon, Archana Venkataraman:
Automated Emotion Morphing in Speech Based on Diffeomorphic Curve Registration and Highway Networks. INTERSPEECH 2019: 4499-4503 - [c18]Jeff Craley, Emily Johnson, Archana Venkataraman:
Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG. IPMI 2019: 291-303 - [c17]Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman:
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces. IPMI 2019: 605-616 - [c16]Naresh Nandakumar, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman:
A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity. CNI@MICCAI 2019: 10-20 - [c15]Jeff Craley, Emily Johnson, Christophe Jouny, Archana Venkataraman:
Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks. MICCAI (4) 2019: 253-261 - [c14]Sayan Ghosal, Qiang Chen, Aaron L. Goldman, William Ulrich, Karen Faith Berman, Daniel R. Weinberger, Venkata S. Mattay, Archana Venkataraman:
Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework. MICCAI (4) 2019: 647-655 - [c13]Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman:
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. MICCAI (3) 2019: 709-717 - [c12]Sayan Ghosal, Qiang Chen, Aaron L. Goldman, William Ulrich, Karen Faith Berman, Daniel R. Weinberger, Venkata S. Mattay, Archana Venkataraman:
A generative-predictive framework to capture altered brain activity in fMRI and its association with genetic risk: application to Schizophrenia. Medical Imaging: Image Processing 2019: 1094927 - 2018
- [c11]Naresh Nandakumar, Niharika Shimona D'Souza, Jeff Craley, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman:
Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields. CNI@MICCAI 2018: 88-98 - [c10]Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman:
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data. MICCAI (3) 2018: 163-171 - [c9]Jeff Craley, Emily Johnson, Archana Venkataraman:
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models. MICCAI (3) 2018: 482-489 - 2017
- [c8]Archana Venkataraman, Nicholas F. Wymbs, Mary Beth Nebel, Stewart Mostofsky:
A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort. CNI@MICCAI 2017: 60-69 - 2013
- [c7]Andrew Sweet, Archana Venkataraman, Steven M. Stufflebeam, Hesheng Liu, Naoro Tanaka, Joseph R. Madsen, Polina Golland:
Detecting Epileptic Regions Based on Global Brain Connectivity Patterns. MICCAI (1) 2013: 98-105 - 2012
- [c6]Archana Venkataraman, Marek Kubicki, Polina Golland:
From Brain Connectivity Models to Identifying Foci of a Neurological Disorder. MICCAI (1) 2012: 715-722 - 2010
- [c5]Archana Venkataraman, Marek Kubicki, Carl-Fredrik Westin, Polina Golland:
Robust feature selection in resting-state fMRI connectivity based on population studies. CVPR Workshops 2010: 63-70 - [c4]Archana Venkataraman, Yogesh Rathi, Marek Kubicki, Carl-Fredrik Westin, Polina Golland:
Joint Generative Model for fMRI/DWI and Its Application to Population Studies. MICCAI (1) 2010: 191-199 - 2009
- [c3]Archana Venkataraman, Koene R. A. Van Dijk, Randy L. Buckner, Polina Golland:
Exploring functional connectivity in fMRI via clustering. ICASSP 2009: 441-444 - 2008
- [c2]Polina Golland, Danial Lashkari, Archana Venkataraman:
Spatial patterns and functional profiles for discovering structure in fMRI data. ACSCC 2008: 1402-1409 - [c1]Archana Venkataraman, Alan V. Oppenheim:
Signal approximation using the bilinear transform. ICASSP 2008: 3729-3732
Editorship
- 2022
- [e3]Ender Konukoglu, Bjoern H. Menze, Archana Venkataraman, Christian F. Baumgartner, Qi Dou, Shadi Albarqouni:
International Conference on Medical Imaging with Deep Learning, MIDL 2022, 6-8 July 2022, Zurich, Switzerland. Proceedings of Machine Learning Research 172, PMLR 2022 [contents] - 2019
- [e2]Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Ai Wern Chung:
Connectomics in NeuroImaging - Third International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. Lecture Notes in Computer Science 11848, Springer 2019, ISBN 978-3-030-32390-5 [contents] - 2014
- [e1]Thomas Schultz, Gemma L. Nedjati-Gilani, Archana Venkataraman, Lauren O'Donnell, Eleftheria Panagiotaki:
Computational Diffusion MRI and Brain Connectivity, MICCAI Workshops CDMRI/MMBC, Nagoya, Japan, September 22nd, 2013. Springer 2014, ISBN 978-3-319-02474-5 [contents]
Informal and Other Publications
- 2024
- [i20]Ravi Shankar, Archana Venkataraman:
Re-ENACT: Reinforcement Learning for Emotional Speech Generation using Actor-Critic Strategy. CoRR abs/2408.01892 (2024) - [i19]Zijian Chen, Maria Varkanitsa, Prakash Ishwar, Janusz Konrad, Margrit Betke, Swathi Kiran, Archana Venkataraman:
A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia. CoRR abs/2409.02303 (2024) - [i18]Zijian Chen, Jueqi Wang, Archana Venkataraman:
QID2: An Image-Conditioned Diffusion Model for Q-space Up-sampling of DWI Data. CoRR abs/2409.02309 (2024) - 2023
- [i17]Niharika Shimona D'Souza, Archana Venkataraman:
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds. CoRR abs/2303.14986 (2023) - 2022
- [i16]Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael L. Platt, Lyle H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang:
Prospective Learning: Back to the Future. CoRR abs/2201.07372 (2022) - [i15]Ravi Shankar, Abdouh Harouna Kenfack, Arjun Somayazulu, Archana Venkataraman:
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition. CoRR abs/2211.05047 (2022) - [i14]Ravi Shankar, Hsi-Wei Hsieh, Nicolas Charon, Archana Venkataraman:
A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion. CoRR abs/2211.05071 (2022) - 2021
- [i13]Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L. Goldman, William Ulrich, Karen Faith Berman, Giuseppe Blasi, Leonardo Fazio, Antonio Rampino, Alessandro Bertolino, Daniel R. Weinberger, Venkata S. Mattay, Archana Venkataraman:
G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification. CoRR abs/2101.11656 (2021) - [i12]Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman:
A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes. CoRR abs/2105.14409 (2021) - [i11]Ravi Shankar, Archana Venkataraman:
A Deep-Bayesian Framework for Adaptive Speech Duration Modification. CoRR abs/2107.04973 (2021) - 2020
- [i10]Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Stewart Mostofsky, Mary Beth Nebel, Keri Rosch, Karen Seymour, Deana Crocetti, Hassna Irzan, Michael Hütel, Sébastien Ourselin, Neil Marlow, Andrew Melbourne, Egor Levchenko, Shuo Zhou, Mwiza Kunda, Haiping Lu, Nicha C. Dvornek, Juntang Zhuang, Gideon Pinto, Sandip Samal, Jorge L. Bernal-Rusiel, Rudolph Pienaar, Ai Wern Chung:
Neuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning Challenge. CoRR abs/2006.03611 (2020) - [i9]Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman:
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces. CoRR abs/2007.01929 (2020) - [i8]Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman:
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. CoRR abs/2007.01930 (2020) - [i7]Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman:
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism. CoRR abs/2007.01931 (2020) - [i6]Ravi Shankar, Jacob Sager, Archana Venkataraman:
Non-parallel Emotion Conversion using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator. CoRR abs/2007.12932 (2020) - [i5]Ravi Shankar, Hsi-Wei Hsieh, Nicolas Charon, Archana Venkataraman:
Multi-speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network. CoRR abs/2007.12937 (2020) - [i4]Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas F. Wymbs, Joshua Robinson, Stewart H. Mostofsky, Archana Venkataraman:
Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations. CoRR abs/2008.12410 (2020) - [i3]Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart H. Mostofsky, Archana Venkataraman:
A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data. CoRR abs/2009.03238 (2020) - [i2]Naresh Nandakumar, Niharika Shimona D'Souza, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman:
A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity. CoRR abs/2011.08813 (2020) - 2018
- [i1]Nicha C. Dvornek, Daniel Y.-J. Yang, Archana Venkataraman, Pamela Ventola, Lawrence H. Staib, Kevin A. Pelphrey, James S. Duncan:
Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging. CoRR abs/1805.09799 (2018)
Coauthor Index
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