Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Hassani, Kaveh"'
Autor:
Wen, Wei, Liu, Kuang-Hung, Fedorov, Igor, Zhang, Xin, Yin, Hang, Chu, Weiwei, Hassani, Kaveh, Sun, Mengying, Liu, Jiang, Wang, Xu, Jiang, Lin, Chen, Yuxin, Zhang, Buyun, Liu, Xi, Cheng, Dehua, Chen, Zhengxing, Zhao, Guang, Han, Fangqiu, Yang, Jiyan, Hao, Yuchen, Xiong, Liang, Chen, Wen-Yen
Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In indust
Externí odkaz:
http://arxiv.org/abs/2311.08430
Autor:
Samanta, Ankur, Karlov, Mark, Ravikumar, Meghna, Clarke, Christian McIntosh, Rajadas, Jayakumar, Hassani, Kaveh
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. For cardiovascular disease, one such condit
Externí odkaz:
http://arxiv.org/abs/2302.11021
Autor:
Bian, Shijie, Grandi, Daniele, Hassani, Kaveh, Sadler, Elliot, Borijin, Bodia, Fernandes, Axel, Wang, Andrew, Lu, Thomas, Otis, Richard, Ho, Nhut, Li, Bingbing
Successful material selection is critical in designing and manufacturing products for design automation. Designers leverage their knowledge and experience to create high-quality designs by selecting the most appropriate materials through performance,
Externí odkaz:
http://arxiv.org/abs/2209.12793
A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality. So far, most techniques use either traditional metrics based on subgraph counting, or the representations of randomly in
Externí odkaz:
http://arxiv.org/abs/2206.06234
Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets. We introduce LG2AR, Learning Graph Augmentations to Learn Graph Repr
Externí odkaz:
http://arxiv.org/abs/2201.09830
Autor:
Hassani, Kaveh
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. We also propose an attention-based graph encoder tha
Externí odkaz:
http://arxiv.org/abs/2201.08265
Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are ye
Externí odkaz:
http://arxiv.org/abs/2107.07042
Autor:
Taghanaki, Saeid Asgari, Hassani, Kaveh, Jayaraman, Pradeep Kumar, Khasahmadi, Amir Hosein, Custis, Tonya
Deep classifiers tend to associate a few discriminative input variables with their objective function, which in turn, may hurt their generalization capabilities. To address this, one can design systematic experiments and/or inspect the models via int
Externí odkaz:
http://arxiv.org/abs/2007.04525
We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to more than two or contrasting mu
Externí odkaz:
http://arxiv.org/abs/2006.05582
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph. We also int
Externí odkaz:
http://arxiv.org/abs/2002.09518