no code implementations • 14 Nov 2023 • Wei Wen, Kuang-Hung Liu, Igor Fedorov, Xin Zhang, Hang Yin, Weiwei Chu, Kaveh Hassani, Mengying Sun, Jiang Liu, Xu Wang, Lin Jiang, Yuxin Chen, Buyun Zhang, Xi Liu, Dehua Cheng, Zhengxing Chen, Guang Zhao, Fangqiu Han, Jiyan Yang, Yuchen Hao, Liang Xiong, Wen-Yen Chen
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
no code implementations • 21 Feb 2023 • Ankur Samanta, Mark Karlov, Meghna Ravikumar, Christian McIntosh Clarke, Jayakumar Rajadas, Kaveh Hassani
Deep learning provides an excellent avenue for optimizing diagnosis and patient monitoring for clinical-based applications, which can critically enhance the response time to the onset of various conditions.
1 code implementation • 26 Sep 2022 • Shijie Bian, Daniele Grandi, Kaveh Hassani, Elliot Sadler, Bodia Borijin, Axel Fernandes, Andrew Wang, Thomas Lu, Richard Otis, Nhut Ho, Bingbing Li
Successful material selection is critical in designing and manufacturing products for design automation.
1 code implementation • 13 Jun 2022 • Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland
A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality.
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.
1 code implementation • 20 Jan 2022 • Kaveh Hassani
We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets.
no code implementations • 8 Jul 2021 • Vincenzo Ferrero, Kaveh Hassani, Daniele Grandi, Bryony DuPont
Function is defined as the ensemble of tasks that enable the product to complete the designed purpose.
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.
no code implementations • ICCV 2019 • Kaveh Hassani, Mike Haley
We introduce an unsupervised multi-task model to jointly learn point and shape features on point clouds.
no code implementations • 18 Oct 2019 • Salvatore Vivona, Kaveh Hassani
We introduce a relational graph neural network with bi-directional attention mechanism and hierarchical representation learning for open-domain question answering task.
Graph Representation Learning Open-Domain Question Answering
no code implementations • 4 Jul 2016 • Kaveh Hassani, Won-Sook Lee
In this paper, a novel and generic multi-objective design paradigm is proposed which utilizes quantum-behaved PSO(QPSO) for deciding the optimal configuration of the LQR controller for a given problem considering a set of competing objectives.
no code implementations • 3 Jul 2016 • Kaveh Hassani, Won-Sook Lee
A natural language interface exploits the conceptual simplicity and naturalness of the language to create a high-level user-friendly communication channel between humans and machines.