Research
My research interests include Graph Representation Learning and Geometric Deep Learning. More specifically, I am interested in leveraging ideas from differential geometry and/or physics to design better representation learning algorithms for graphs.
Papers
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Spectral Pruning Against Over-Squashing and Over-Smoothing.
Adarsh Jamadandi, Celia Rubio-Madrigal, Rebekka Burkholz. Pre-print, 2024. -
Graph of Thrones: Adversarial Perturbations dismantle Aristocracy in Graphs.
Adarsh Jamadandi, Uma Mudenagudi. AAAI Student Poster, 2021. Extended Version in Differential Geometry meets Deep Learning - DiffGeo4DL (NeurIPS), 2020. -
Probabilistic Word Embeddings in Kinematic Space.
Adarsh Jamadandi, Rishabh Tigadoli, Ramesh Tabib, Uma Mudenagudi. International Conference on Pattern Recognition (ICPR), 2020. -
Exemplar-based Underwater Image Enhancement Augmented by Wavelet Corrected Transforms.
Adarsh Jamadandi, Uma Mudenagudi. Computer Vision and Pattern Recognition (CVPR Workshop, Oral), 2019. -
Learning Hierarchical Representations in Kinematic Space.
Adarsh Jamadandi, Uma Mudenagudi. Graph Representation Learning Workshop, Neural Information Processing Systems (NeurIPS), 2019. -
PredGAN: a deep multi-scale video prediction framework for detecting anomalies in videos.
Adarsh Jamadandi, Sunidhi Kotturshettar, Uma Mudenagudi. 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP ‘18), 2018.