Spatiotemporal-bandwidth product of m-dimensional signals. - Nilesh A. Ahuja; Nirmal K. Bose (IEEE Signal Process. Lett., 2005)

Multidimensional Generalized Sampling Theorem for wavelet Based Image Superresolution. - Nilesh A. Ahuja; Nirmal K. Bose (ICIP, 2006)

Superresolution and noise filtering using moving least squares. - Nirmal K. Bose; Nilesh A. Ahuja (IEEE Trans. Image Process., 2006)

Design of Large Field-of-View High-Resolution Miniaturized Imaging System. - Nilesh A. Ahuja; Nirmal K. Bose (EURASIP J. Adv. Signal Process., 2007)

A Factorization Approach for Enabling Structure-from-Motion/SLAM Using Integer Arithmetic. - Nilesh A. Ahuja; Mahesh Subedar; Omesh Tickoo; Yeongseon Lee (ICCV Workshops, 2017)

Real-time Approximate Bayesian Computation for Scene Understanding. - Javier Felip; Nilesh A. Ahuja; David Gómez-Gutiérrez; Omesh Tickoo; Vikash Mansinghka 001 (CoRR, 2019)

Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection. - Nilesh A. Ahuja; Ibrahima J. Ndiour; Trushant Kalyanpur; Omesh Tickoo (CoRR, 2019)

Deep Probabilistic Models to Detect Data Poisoning Attacks. - Mahesh Subedar; Nilesh A. Ahuja; Ranganath Krishnan; Ibrahima J. Ndiour; Omesh Tickoo (CoRR, 2019)

Tree pyramidal adaptive importance sampling. - Javier Felip; Nilesh A. Ahuja; Omesh Tickoo (CoRR, 2019)

Semantic-Preserving Image Compression. - Neel Patwa; Nilesh A. Ahuja; V. Srinivasa Somayazulu; Omesh Tickoo; Srenivas Varadarajan; Shashidhar G. Koolagudi (ICIP, 2020)

Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features. - Ibrahima J. Ndiour; Nilesh A. Ahuja; Omesh Tickoo (CoRR, 2020)

E2E Visual Analytics: Achieving >10X Edge/Cloud Optimizations. - Chaunte Lacewell; Nilesh A. Ahuja; J. Pablo Munoz; Parual Datta; Ragaad AlTarawneh; Vui Seng Chua; Nilesh Jain; Omesh Tickoo; Ravi Iyer 001 (NAS, 2021)

Energy-Based Anomaly Detection and Localization. - Ergin Utku Genc; Nilesh A. Ahuja; Ibrahima J. Ndiour; Omesh Tickoo (CoRR, 2021)

Mitigating Sampling Bias and Improving Robustness in Active Learning. - Ranganath Krishnan; Alok Sinha; Nilesh A. Ahuja; Mahesh Subedar; Omesh Tickoo; Ravi Iyer 001 (CoRR, 2021)

Improving Robustness and Efficiency in Active Learning with Contrastive Loss. - Ranganath Krishnan; Nilesh A. Ahuja; Alok Sinha; Mahesh Subedar; Omesh Tickoo; Ravi Iyer 001 (CoRR, 2021)

Continual Active Adaptation to Evolving Distributional Shifts. - Amrutha Machireddy; Ranganath Krishnan; Nilesh A. Ahuja; Omesh Tickoo (CVPR Workshops, 2022)

incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection. - Amanda Rios; Nilesh A. Ahuja; Ibrahima J. Ndiour; Ergin Utku Genc; Laurent Itti; Omesh Tickoo (ECCV (25), 2022)

Anomalib: A Deep Learning Library for Anomaly Detection. - Samet Akcay; Dick Ameln; Ashwin Vaidya; Barath Lakshmanan; Nilesh A. Ahuja; Ergin Utku Genc (ICIP, 2022)

Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection. - Ibrahima J. Ndiour; Nilesh A. Ahuja; Omesh Tickoo (ICIP, 2022)

A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing. - Parual Datta; Nilesh A. Ahuja; V. Srinivasa Somayazulu; Omesh Tickoo (ICPR, 2022)

End-to-End Industrial IoT: Software Optimization and Acceleration. - Meena Arunachalam; Vrushabh Sanghavi; Samudyatha Kaira; Nilesh A. Ahuja (IEEE Internet Things Mag., 2022)

FRE: A Fast Method For Anomaly Detection And Segmentation. - Ibrahima J. Ndiour; Nilesh A. Ahuja; Ergin Utku Genc; Omesh Tickoo (BMVC, 2023)

Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN models. - Nilesh A. Ahuja; Parual Datta; Bhavya Kanzariya; V. Srinivasa Somayazulu; Omesh Tickoo (CVPR, 2023)

Rate-Distortion Theory in Coding for Machines and its Application. - Alon Harell; Yalda Foroutan; Nilesh A. Ahuja; Parual Datta; Bhavya Kanzariya; V. Srinivasa Somayazulu; Omesh Tickoo; Anderson de Andrade; Ivan V. Bajic (CoRR, 2023)

Compression without Compromise: Optimizing Point Cloud Object Detection with Bottleneck Architectures For Split Computing. - Vinay Kashyap; Nilesh A. Ahuja; Omesh Tickoo (ICME Workshops, 2024)

Split-DNN Computing for Video Analytics. - Nagabhushan Eswara; Jaroslaw Sydir; V. Srinivasa Somayazulu; Parual Datta; Nilesh A. Ahuja; Omesh Tickoo (ICPR (3), 2024)

A Robust Framework for Evaluation of Unsupervised Time-Series Anomaly Detection. - Onat Güngör; Amanda Rios; Priyanka Mudgal; Nilesh A. Ahuja; Tajana Rosing (ICPR (26), 2024)

CUAL: Continual Uncertainty-aware Active Learner. - Amanda Rios; Ibrahima J. Ndiour; Parual Datta; Jerry Sydir; Omesh Tickoo; Nilesh A. Ahuja (CoRR, 2024)

CONCLAD: COntinuous Novel CLAss Detector. - Amanda Rios; Ibrahima J. Ndiour; Parual Datta; Omesh Tickoo; Nilesh A. Ahuja (CoRR, 2024)

Uncertainty Quantification in Continual Open-World Learning. - Amanda Sofie Rios; Ibrahima J. Ndiour; Parual Datta; Jaroslaw Sydir; Omesh Tickoo; Nilesh A. Ahuja (CoRR, 2024)

Retri3D: 3D Neural Graphics Representation Retrieval. - Yushi Guan; Daniel Kwan; Jean Sebastien Dandurand; Xi Yan; Ruofan Liang; Yuxuan Zhang; Nilesh Jain; Nilesh A. Ahuja; Selvakumar Panneer; Nandita Vijaykumar (ICLR, 2025)

INRet: A General Framework for Accurate Retrieval of INRs for Shapes. - Yushi Guan; Daniel Kwan; Ruofan Liang; Selvakumar Panneer; Nilesh Jain; Nilesh A. Ahuja; Nandita Vijaykumar (CoRR, 2025)

TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress. - Onat Güngör; Amanda Sofie Rios; Nilesh A. Ahuja; Tajana Rosing (CoRR, 2025)

Rate-Distortion Theory in Coding for Machines and Its Applications. - Alon Harell; Yalda Foroutan; Nilesh A. Ahuja; Parual Datta; Bhavya Kanzariya; V. Srinivasa Somayazulu; Omesh Tickoo; Anderson de Andrade; Ivan V. Bajic (IEEE Trans. Pattern Anal. Mach. Intell., 2025)