Ph.D. THESIS
“Batch Mode Active Learning for Multimedia Pattern Recognition”, School of Computing, Informatics and Decision Systems Engineering, Arizona State University, April 2013
JOURNALS / BOOK CHAPTERS / MAGAZINE ARTICLES
Z. He, M. Dieciuc, D. Carr, S. Chakraborty, A. Singh, I. Fowe, S. Zhang, M. Lustria, A. Terracciano, N. Charness, W. Boot, “New Opportunities for the Early Detection and Treatment of Cognitive Decline: Adherence Challenges and the Promise of Smart and Person-Centered Technologies”, BMC Digital Health 2023
A. Singh, S. Chakraborty, Z. He, S. Tian, S. Zhang, M. Lustria, N. Charness, N. Roque, E. Harrell, W. Boot, “Deep Learning-based Predictions of Older Adults’ Adherence to Cognitive Training to Support Training Efficacy”, Frontiers in Psychology 2022 (Impact Factor: 4.23)
Z. He, S. Tian, A. Singh, S. Chakraborty, S. Zhang, M. Lustria, N. Charness, N. Roque, E. Harrell, W. Boot, “A Machine-Learning Based Approach for Predicting Older Adults’ Adherence to Technology-Based Cognitive Training”, Information Processing and Management 2022 (Impact Factor: 7.47)
D. Carr, S. Tian, Z. He, S. Chakraborty, M. Dieciuc, N. Gray, M. Agharazidermani, M. Lustria, A. Dilanchian, S. Zhang, N. Charness, A. Terracciano, W. Boot, “Motivation to Engage in Aging Research: Are There Typologies and Predictors?”, The Gerontologist 2022 (Impact Factor: 5.27)
M. Ghaffari, A. Srinivasan, X. Liu, S. Chakraborty, “High-resolution Home Location Prediction from Twitter Activities using Consensus Deep Learning”, Social Network Analysis and Mining (SNAM) Journal 2021
S. Chakraborty, T. Wu, E. Forzani, C. Whisner, D. Jackemeyer, “Long-Term Resting Metabolic Rate Analysis -Towards Prediction Models for Resting Metabolic Rate Changes”, International Journal of Prognostics and Health Management (IJPHM) 2020
R. Ramakrishnan, B. Nagabandi, J. Eusebio, S. Chakraborty, S. Panchanathan, H. Venkateswara, “Deep Hashing Network for Unsupervised Domain Adaptation”, Domain Adaptation in Computer Vision With Deep Learning, Springer 2020
H. Ranganathan, H. Venkateswara, S. Chakraborty, S. Panchanathan, “Deep Active Learning for Image Regression”, Deep Learning Applications, Springer 2019
A. Singh, S. Chakraborty, “Deep Domain Adaptation for Regression”, Development and Analysis of Deep Learning Architectures, Springer 2019
S. Panchanathan, M. Moore, H. Venkateswara, S. Chakraborty, T. McDaniel, “Computer Vision for Augmentative and Alternative Communication”, Computer Vision for Assistive Healthcare, Elsevier 2018
H. Venkateswara, S. Chakraborty, S. Panchanathan, “Deep Learning Systems for Domain Adaptation in Computer Vision”, IEEE Signal Processing Magazine (SPM), Special Issue on Deep Learning for Visual Understanding 2017
S. Panchanathan, S. Chakraborty, T. McDaniel et al., “Enriching the Fan Experience in a Smart Stadium Using Internet of Things Technologies”, International Journal of Semantic Computing (IJSC) 2017 (Special Issue on Best of IEEE ISM 2016)
S. Panchanathan, S. Chakraborty, T. McDaniel, R. Tadayon, “Person-Centered Multimedia Computing: A New Paradigm Inspired by Assistive and Rehabilitative Applications”, IEEE Multimedia Magazine (MM) 2016 (2017 IEEE Multimedia Best Department Article Award)
S. Panchanathan, S. Chakraborty, T. McDaniel, “Social Interaction Assistant: A Person-Centered Approach to Enrich Social Interactions for Individuals with Visual Impairments”, IEEE Journal on Selected Topics in Signal Processing (J-STSP) 2016
S. Chakraborty, V. Balasubramanian, Q. Sun, S. Panchanathan, J. Ye, “Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Adaptive Batch Mode Active Learning”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2015
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Conformal Predictions for Information Fusion: A Comparative Study of P-Value Combination Methods”, Annals of Mathematics and Artificial Intelligence (AMAI) 2014
V. Balasubramanian, S. Chakraborty, S.S. Ho, H. Wechsler, S. Panchanathan, “Active Learning using Conformal Predictions”, Morgan Kaufmann Publ. (Elsevier) 2014
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Generalized Batch Mode Active Learning for Face-based Biometric Recognition”, Pattern Recognition Journal 2012
S. Marcel, C. McCool, S. Chakraborty, V. Balasubramanian, S. Panchanathan et al. “On the Results of the First Mobile Biometry (MOBIO) Face and Speaker Verification Evaluation”, Lecture Notes on Computer Science (LNCS) 2010
CONFERENCES AND WORKSHOPS
L. Zhang, L. Xu, S. Motamed, S. Chakraborty, F. de la Torre, “D3GU: Multi-target Active Domain Adaptation via Enhancing Domain Alignment”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2024
D. Goswami, S. Chakraborty, “Active Batch Sampling for Multi-label Classification with Binary User Feedback”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2024
Md S. Seraj, A. Singh, S. Chakraborty, “Semi-supervised Deep Domain Adaptation for Deepfake Detection”, Workshop on Manipulation, Adversarial and Presentation Attacks In Biometrics (MAP-A) at IEEE Winter Conference on Applications of Computer Vision (WACV) 2024
D. Goswami, S. Chakraborty, “Active Learning for Video Classification with Frame Level Queries”, IEEE International Joint Conference on Neural Networks (IJCNN) 2023
S. Chakraborty, A. Singh, “Active Sampling for Text Classification with Subinstance Level Queries”, AAAI Conference on Artificial Intelligence 2022 (accepted for oral presentation)
S. Banerjee, S. Chakraborty, “Deterministic Mini-batch Sequencing for Training Deep Neural Networks”, AAAI Conference on Artificial Intelligence 2021
A. Singh, S. Chakraborty, “Deep Active Learning with Relative Label Feedback: An Application to Facial Age Estimation”, IEEE International Joint Conference on Neural Networks (IJCNN) 2021
A. Yuan, S. Chakraborty, “A Study of Deep Learning for Predicting Freeze of Gait in Patients with Parkinson’s Disease”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2020
A. Singh, S. Chakraborty, “Deep Active Transfer Learning for Image Recognition”, IEEE International Joint Conference on Neural Networks (IJCNN) 2020
S. Banerjee, S. Chakraborty, “Budgeted Subset Selection for Fine-tuning Deep Learning Architectures in Resource-Constrained Applications”, IEEE International Joint Conference on Neural Networks (IJCNN) 2020
S. Chakraborty, “Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles”, AAAI Conference on Artificial Intelligence 2020
M. Woodham, J. Hawkins, A. Singh, S. Chakraborty, “When to Pull Starting Pitchers in Major League Baseball? A Data Mining Approach”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2019
A. Bhattacharya, J. Liu, S. Chakraborty, “A Generic Active Learning Framework for Class Imbalance Applications”, British Machine Vision Conference (BMVC) 2019
C. Mills, J. Escobar-Avila, A. Bhattacharya, G. Kondyukov, S. Chakraborty, S. Haiduc, “Tracing with Less Data: Active Learning for Classification-Based Traceability Link Recovery”, IEEE International Conference on Software Maintenance and Evolution (ICSME) 2019
S. Banerjee, S. Chakraborty, “DeepSub: A Novel Subset Selection Framework for Training Deep Learning Architectures”, IEEE International Conference on Image Processing (ICIP) 2019
A. Bhattacharya, S. Chakraborty, “Active Learning with n-ary Queries for Image Recognition”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2019
V. Torvi, A. Bhattacharya, S. Chakraborty, “Deep Domain Adaptation to Predict Freezing of Gait in Patients with Parkinson’s Disease”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2018
H. Ranganathan, H. Venkateswara, S. Chakraborty, S. Panchanathan, “Multi-Label Deep Active Learning with Label Correlation”, IEEE International Conference on Image Processing (ICIP) 2018
S. Chakraborty, “Distributed Active Learning for Image Recognition”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2018
S. Chakraborty, J. Stokes, L. Xiao, D. Zhou, M. Marinescu, A. Thomas, “Hierarchical Learning for Automated Malware Classification”, IEEE Military Communications Conference (MILCOM) 2017
H. Ranganathan, H. Venkateswara, S. Chakraborty, S. Panchanathan, “Deep Active Learning for Image Classification”, IEEE International Conference on Image Processing (ICIP) 2017
H. Venkateswara, J. Eusebio, S. Chakraborty, S. Panchanathan, “Deep Hashing Network for Unsupervised Domain Adaptation”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017
H. Venkateswara, S. Chakraborty, T. McDaniel, S. Panchanathan, “Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation”, Workshop on Knowledge-based Techniques for Problem Solving and Reasoning (KnowPros) at the Association for the Advancement of Artificial Intelligence (AAAI) 2017
S. Panchanathan, S. Chakraborty, T. McDaniel et al. “Smart Stadium for Smart Living: Enriching the Fan Experience”, IEEE International Symposium on Multimedia (ISM) 2016 (Invited Paper)
H. Venkateswara, S. Chakraborty, S. Panchanathan, “Nonlinear Embedding Transform for Unsupervised Domain Adaptation”, Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV) at the European Conference on Computer Vision (ECCV) 2016
H. Ranganathan, S. Chakraborty, S. Panchanathan, “Transfer of Multi-modal Emotion Features ion Deep Belief Networks”, Asilomar Conference on Signals, Systems and Computers 2016
H. Ranganathan, S. Chakraborty, S. Panchanathan, “Multi-modal Emotion Recognition using Deep Learning Architectures”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2016
S. Chakraborty, O. Tickoo, R. Iyer, “Towards Distributed Video Summarization”, ACM Multimedia Conference (ACM-MM) 2015
S. Chakraborty, V. Balasubramanian, A. Ravi Sankar, S. Panchanathan, J. Ye, “BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification”, ACM Conference on Knowledge Discovery and Data Mining (KDD) 2015
S. Chakraborty, O. Tickoo, R. Iyer, “Adaptive Keyframe Selection for Video Summarization”, IEEE Winter Conference on Applications of Computer Vision (WACV) 2015
S. Chakraborty, J. Zhou, V. Balasubramanian, S. Panchanathan, I. Davidson, J. Ye, “Active Matrix Completion”, IEEE International Conference on Data Mining (ICDM) 2013
S. Chakraborty, H. Venkateswara, V. Balasubramanian, S. Panchanathan, “Active Batch Selection for Fuzzy Classification in Facial Expression Recognition”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2011
R. Chattopadhyay, S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Optimization-based Domain Adaptation towards Person-Adaptive Classification Models”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2011
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Optimal Batch Selection for Active Learning in Multi-label Classification”, ACM Multimedia Conference (ACM-MM) 2011
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Dynamic Batch Mode Active Learning”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “An Optimization Based Framework for Dynamic Batch Mode Active Learning”, Workshop on Optimization for Machine Learning at Neural Information Processing Systems (NIPS) 2010
V. Balasubramanian, S. Chakraborty, S. Krishna, S. Panchanathan, “Enhancing Social Interactions of Individuals with Visual Impairments: A Case Study for Assistive Machine Learning”, Workshop on Machine Learning for Assistive Technologies at Neural Information Processing Systems (NIPS) 2010
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Multiple Kernel Learning for Efficient Conformal Predictions”, Workshop on New Directions in Multiple Kernel Learning at Neural Information Processing Systems (NIPS) 2010
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2010
V. Balasubramanian, J. Ye, S. Chakraborty, S. Panchanathan, “Kernel Learning for Efficiency Maximization in the Conformal Predictions Framework”, IEEE International Conference on Machine Learning and Applications (ICMLA) 2010
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Learning from Summaries of Videos: Applying Batch Mode Active Learning to Face-based Biometrics”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Biometrics 2010
S. Chakraborty, V. Balasubramanian, S. Panchanathan, “Batch Mode Active Learning for Biometric Recognition”, SPIE International Conference on Biometric Technology for Human Identification, SPIE Defense, Security & Sensing 2010
V. Balasubramanian, S. Chakraborty, S. Krishna, S. Panchanathan, “Human-Centered Machine Learning in a Social Interaction Assistant for Individuals with Visual Impairments”, Symposium on Assistive Machine Learning for People with Disabilities at Neural Information Processing Systems (NIPS) 2009
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Online Active Learning using Conformal Predictions”, Workshop on Analysis and Design of Algorithms for Interactive Machine Learning at Neural Information Processing Systems (NIPS) 2009
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Generalized Query by Transduction for Online Active Learning”, IEEE International Conference on Computer Vision (ICCV), Workshop on Online Learning for Computer Vision 2009
V. Balasubramanian, S. Chakraborty, S Panchanathan, “Multiple Cue Integration in Transductive Confidence Machines for Head Pose Classification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Online Learning for Classification 2008
G. Gupta, S.K. Saha, S. Chakraborty, C. Mazumdar, “Document Frauds: Identification and Linking Fake Documents to Scanners and Printers”, IEEE International Conference on Computing, Theory and Applications (ICCTA) 2007
TECHNICAL REPORTS
V. Balasubramanian, S. Chakraborty, S. Panchanathan, “Confidence Estimation in Pattern Classification: An Analysis with Head Pose Estimation”, Technical Report TR-09-12, School of Computing, Informatics and Decision Systems Engineering, Arizona State University 2009