no code implementations • CVPR 2023 • Aditya Sanghi, Rao Fu, Vivian Liu, Karl Willis, Hooman Shayani, Amir Hosein Khasahmadi, Srinath Sridhar, Daniel Ritchie
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes.
no code implementations • 29 Jul 2022 • Hang Chu, Amir Hosein Khasahmadi, Karl D. D. Willis, Fraser Anderson, Yaoli Mao, Linh Tran, Justin Matejka, Jo Vermeulen
Our method introduces a user-session network architecture, as well as session dropout as a novel way of data augmentation.
no code implementations • 24 Jan 2022 • Kaveh Hassani, Amir Hosein Khasahmadi
Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets.
no code implementations • 23 Oct 2021 • Linh Tran, Amir Hosein Khasahmadi, Aditya Sanghi, Saeid Asgari
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
no code implementations • 1 Jan 2021 • Saeid Asgari, Kristy Choi, Amir Hosein Khasahmadi, Anirudh Goyal
A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream classification task, without overfitting to spurious input features.
1 code implementation • 9 Jul 2020 • Saeid Asgari Taghanaki, Kaveh Hassani, Pradeep Kumar Jayaraman, Amir Hosein Khasahmadi, Tonya Custis
We show that coupling a PointMask layer with an arbitrary model can discern the points in the input space which contribute the most to the prediction score, thereby leading to interpretability.
4 code implementations • ICML 2020 • Kaveh Hassani, Amir Hosein Khasahmadi
We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol.
2 code implementations • ICLR 2020 • Amir Hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs.