About Me

About Me

M.S. student in Computer Science at the University of Minnesota. Researching brain-inspired theoretical AI and robust knowledge structures.

Publications

SAMCNet for Spatial-configuration-based Classification: A Summary of Results

Published in Special Interest Group on Knowledge Discovery and Data Mining, 2021

Point set classification aims to build a representation learning model that distinguishes between spatial and categorical configurations of point set data. This problem is societally important since in many applications domains such as immunology, and microbial ecology. This problem is challenging since the interactions between different categories of points are not always equal; as a result, the representation learning model must selectively learn the most relevant multi-categorical relationships. The related works are limited (1) in learning the importance of different multi-categorical relationships, especially for high-order interactions, and (2) do not fully exploit the spatial distribution of points beyond simply measuring relative distance or applying a feed-forward neural network to coordinates. To overcome these limitations, we leverage the dynamic graph convolutional neural network (DGCNN) architecture to design a novel multi-category DGCNN (MC-DGCNN), contributing location representation and point pair attention layers for multi-categorical point set classification. MC-DGCNN has the ability to identify the categorical importance of each point pair and extends this to N-way spatial relationships, while still preserving all the properties and benefits of DGCNN (e.g., differentiability). Experimental results show that the proposed architecture is computationally efficient and significantly outperforms current deep learning architectures on real-world datasets.

Recommended citation: Farhadloo, M., Molnar, C., Luo, G., Li, Y., Shekhar, S., Maus, R. L., ... & Leontovich, A. (2021). MC-DGCNN: A Novel DNN Architecture for Multi-Category Point Set Classification. arXiv preprint arXiv:2112.12219. https://arxiv.org/pdf/2112.12219.pdf

Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

Published in ACM Transactions on Intelligent Systems and Technology, 2021

Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANN-E approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show that SVANN-I outperforms OSFA and SVANN-E performs the best of all.

Recommended citation: Gupta, J., Molnar, C., Xie, Y., Knight, J., & Shekhar, S. (2021). Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach. ACM Transactions on Intelligent Systems and Technology (TIST), 12(6), 1-21. https://dl.acm.org/doi/pdf/10.1145/3466688

Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results

Published in ACM SIGSPATIAL, 2021

Given Spatial Variability Aware Neural Networks (SVANNs), the goal is to investigate mathematical (or computational) models for comparative physical interpretation towards their transparency (e.g., simulatibility, decomposability and algorithmic transparency). This problem is important due to important use-cases such as reusability, debugging, and explainability to a jury in a court of law. Challenges include a large number of model parameters, vacuous bounds on generalization performance of neural networks, risk of overfitting, sensitivity to noise, etc., which all detract from the ability to interpret the models. Related work on either model-specific or model-agnostic post-hoc interpretation is limited due to a lack of consideration of physical constraints (e.g., mass balance) and properties (e.g., second law of geography). This work investigates physical interpretation of SVANNs using novel comparative approaches based on geographically heterogeneous features. The proposed approach on feature-based physical interpretation is evaluated using a case-study on wetland mapping. The proposed physical interpretation improves the transparency of SVANN models and the analytical results highlight the trade-off between model transparency and model performance (e.g., F1-score). We also describe an interpretation based on geographically heterogeneous processes modeled as partial differential equations (PDEs).

Recommended citation: Gupta, J., Molnar, C., Luo, G., Knight, J., & Shekhar, S. (2021). Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results. arXiv preprint arXiv:2110.15866. https://arxiv.org/pdf/2110.15866.pdf