1 code implementation • IEEE Western New York Image and Signal Processing Workshop (WNYISPW) 2019 • Miguel Dominguez, Rohan Dhamdhere, Naga Durga Harish Kanamarlapudi, Sunand Raghupathi, Raymond Ptucha
Our evolution mutates a population of neural networks to search the architecture and hyperparameter space.
Ranked #1 on Graph Classification on MUTAG
no code implementations • 27 Sep 2018 • Shagan Sah, Dheeraj Peri, Ameya Shringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Ray Ptucha
Along with MMVR, we propose two improvements to the text conditioned image generation.
1 code implementation • IEEE Winter Conference on Applications of Computer Vision (WACV) 2018 • Miguel Dominguez, Rohan Dhamdhere, Atir Petkar, Saloni Jain, Shagan Sah, Raymond Ptucha
We adopt these graph based methods to 3D point clouds to introduce a generic vector representation of 3D graphs, we call graph 3D (G3D).
Ranked #2 on 3D Object Classification on ModelNet40 (using extra training data)
1 code implementation • 2 Mar 2017 • Felipe Petroski Such, Shagan Sah, Miguel Dominguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan Cahill, Raymond Ptucha
Graph-CNNs can handle both heterogeneous and homogeneous graph data, including graphs having entirely different vertex or edge sets.