Publication

Conference

    1. Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection [pdf]
      S. Gupta, S. Lee, M. De-Arteaga, M. Lease. Web Conference (WWW), arXiv:2302.07372 (2023).
    2. Learning a neural Pareto manifold extractor with constraints. [pdf]
      S. Gupta, G. Singh, R. Bollapragada and M. Lease. Uncertainty in Artificial Intelligence (UAI) (2022).
    3. Pareto Solutions vs Dataset Optima: Concepts and Methods for Optimizing Competing Objectives
      with Constraints in Retrieval. [pdf]
      S. Gupta, G. Singh, A. Das and M. Lease. International Conference on Theory of Information Retrieval (ICTIR) (2022).
    4. A Streaming model for Generalized Rayleigh with extensions to Minimum Noise Fraction. [pdf]
      S. Gupta and C. Bajaj. IEEE International Conference on Big Data (2019).
    5. Correlation, Prediction and Ranking of Evaluation Metrics (Best Student Paper Award) in Information Retrieval. [pdf]
      S. Gupta, M. Kutlu, V. Khetan and M. Lease. European Conference on Information Retrieval (ECIR) (2019).
    6. Efficient Clustering-based Noise Covariance Estimation for Maximum Noise Fraction. [pdf]
      S. Gupta and C. Bajaj. NCVPRIPG, Springer (2017).
    7. A GPU based real-time CUDA implementation for obtaining Visual Saliency. [pdf]
      R. Agarwal, S. Gupta, J. Mukhopadhyay and R. Layek. ICVGIP, ACM 2014.
    8. Psychovisual saliency in color images. [pdf]
      S. Gupta, R. Agarwal, R. Layek and J. Mukhopadhyay. NCVPRIPG, IEEE 2013.

Journal

    1. HOFS: Higher order mutual information approximation for feature selection in R. [pdf]
      K. Gajowniczek, J. Wu, S. Gupta and C. Bajaj. SoftwareX, Elsevier (2022). arXiv:1612.00554.
    2. A Fully Automated, Faster Noise Rejection Approach to Increasing the Analytical Capability
      of Chemical Imaging for Digital Histopathology. [pdf]
      S. Gupta, S. Mittal, A. Balla, R. Bhargava and C. Bajaj. PloS One 14.4 (2019): e0205219.

Preprints

    1. Tail-Net: Extracting Lowest Singular Triplets for Big Data Applications [pdf]
      G. Singh and S. Gupta. arXiv: preprint, arXiv:2104.13968 (2021).
    2. SCA-Net: A Self-Correcting Two-Layer Auto-encoder for Hyperspectral Unmixing. [pdf] (under Review)
      G. Singh, S. Gupta, M. Lease and C. Dawson. arXiv: preprint, arXiv:2102.05713 (2021).
    3. Hybrid Neural Pareto Front (HNPF): A Two-Stage Neural-Filter approach for Pareto Front Extraction. [pdf]
      S. Gupta, G. Singh, M. Lease and C. Dawson. arXiv: preprint, arXiv:2101.11684 (2021).
    4. Streaming Singular Value Decomposition for Big Data Applications. [pdf] (under Review)
      S. Gupta, G. Singh, M. Lease and C. Dawson. arXiv: preprint, arXiv:2010.14226 (2020).
    5. Extracting Optimal Solution Manifolds using Constrained Neural Optimization. [pdf]
      S. Gupta, G. Singh and M. Lease. arXiv preprint, arXiv:2009.06024 (2020).
    6. Prevention is Better than Cure: Handling Basis Collapse and Transparency in Dense Networks. [pdf]
      S. Gupta, G. Singh and C. Dawson. arXiv preprint, arXiv:2008.09878 (2020).
    7. TIME: A Fully Convolutional Neural Network Architecture with Interpretable Kernels for Dynamic Physical Processes. [pdf]
      G. Singh, S. Gupta, M. Lease and C. Dawson. arXiv: preprint, arXiv:2003.02426 (2020).