Suryabhan Singh Hada Ph.D. student Electrical Engineering and Computer Science School of Engineering University of California, Merced 5200 N. Lake Road Merced, CA 95343 Email: shada [at] ucmerced.edu WWW: http://graduatestudents.ucmerced.edu/shada Social: [LinkedIn]

Research interests

My research focuses on Machine Learning and Optimization and their applications in computer vision and natural language processing. Particularly, my recent work focuses on interpretability of deep neural networks and their visualization. In the past, I worked on mathematical modelling and fractal image compression.

About

I am a fifth year Ph.D. student at UC Merced supervised by Miguel Á. Carreira-Perpiñán. I received my Integrated Masters of Technology (Undergraduation and Masters), in Mathematics and Computing from IIT(BHU). Before starting my Ph.D. I worked as a Software Engineer at Cvent. You can find my CV [here] .

Publications

• Hada, S. S. and Carreira-Perpiñán, M. Á. (2022):
  "Sparse oblique decision trees: a tool to interpret natural language processing datasets".
  International Joint Conf. on Neural Networks (IJCNN 2022), to appear.
  [external link] [paper preprint] [© IEEE]

• Hada, S. S. and Carreira-Perpiñán, M. Á. (2022):
  "Interpretable image classification using sparse oblique decision trees".
  IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2022), pp. 2759-2763.
  [external link] [paper preprint] [slides] [© IEEE]

• Hada, S. S. and Carreira-Perpiñán, M. Á. (2021):
  "Exploring counterfactual explanations for classification and regression trees".
  Int. Workshop and Tutorial on eXplainable Knowledge Discovery in Data Mining (ECML 2021).
  [external link] [paper preprint]

• Hada, S. S. and Carreira-Perpiñán, M. Á. (2021):
  "Understanding and Manipulating Neural Net Features Using Sparse Oblique Classification Trees".
  28th IEEE International Conference on Image Processing (IEEE - ICIP 2021), to appear.
  [external link] [paper preprint] [© IEEE]

• Hada, S. S. and Carreira-Perpiñán, M. Á. and Zharmagambetov, A. (2021):
  "Sparse Oblique Decision Trees:A Tool to Understand and Manipulate Neural Net Features".
  Unpublished manuscript, April 6, 2021, arXiv:2104.02922
  [external link] [paper preprint]

• Carreira-Perpiñán, M. Á. and Hada, S. S. (2021):
  "Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms".
  35th AAAI Conference on Artificial Intelligence (AAAI 2021).
  [external link] [paper preprint] [Python implementation (coming soon)]

  • Longer version:
    Carreira-Perpiñán, M. Á. and Hada, S. S.
    "Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms".
    Unpublished manuscript, March 1, 2021, arXiv:2103.01096.
    [external link] [paper preprint]

• Hada, S. S. and Carreira-Perpiñán, M. Á. (2021):
  "Style Transfer by Rigid Alignment in Neural Net Feature Space".
  IEEE Conf. Winter Conference of Applications on Computer Vision (WACV 2021)
  [external link] [paper preprint] [supplementary material] [slides] [animations] [Python implementation (coming soon)] [© IEEE]

  • Longer version:
    Hada, S. S. and Carreira-Perpiñán, M. Á.
    "Style Transfer by Rigid Alignment in Neural Net Feature Space".
    Unpublished manuscript, Sept 27, 2019, arXiv:1909.13690.
    [external link] [paper preprint]

• Zharmagambetov, A.* and Hada, S. S.* and Carreira-Perpiñán, M. Á. and Gabidolla, M. (2021) (* means equal contribution):
  "Non-Greedy Algorithms for Decision Tree Optimization: An Experimental Comparison".
  International Joint Conference on Neural Networks (IJCNN 2021), to appear.
  [external link] [paper preprint] [© IEEE]

  • Longer version:
    Zharmagambetov, A.* and Hada, S. S.* and Carreira-Perpiñán, M. Á. and Gabidolla, M. (2019):
    "An Experimental Comparison of Old and New Decision Tree Algorithms".
    Unpublished manuscript, Nov. 8, 2019, arXiv:1911.03054
    [external link] [paper preprint]

• Carreira-Perpiñán, M. Á. and Hada, S. S. (2020):
  "Inverse classification with logistic and softmax classifiers: efficient optimization".
  Unpublished manuscript, 2020, arXiv:.
  [external link] [paper preprint] [Matlab implementation]

  • Short version at the Workshop on Beyond first order methods in machine learning systems (ICML 2020)
    [external link] [paper preprint]

• Hada, S. S. and Carreira-Perpiñán, M. Á. (2021):
  "Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets".
  28th IEEE International Conference on Image Processing (IEEE - ICIP 2021), to appear.
  [external link] [paper preprint] [slides] [poster] [© IEEE]

  • Longer version:
    Hada, S. S. and Carreira-Perpiñán, M. Á.
    "Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets".
    Unpublished manuscript, Sept 27, 2019, arXiv:1910.04857.
    [external link] [paper preprint] [animations]

  • Extended abstract at the Bay Area Machine Learning Symposium, Oct. 19, 2017 (BayLearn 2017)
    [external link] [paper preprint] [poster]
    

• Suryabhan Singh Hada (2014): "A Fast Encoding Method for Fractal Image Compression".
  International Journal of Computer Applications (IJCA), August 2014.,
  [external link]

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