Search Results - "Faez, Faezeh"

Deep Graph Generators: A Survey by Faez, Faezeh, Ommi, Yassaman, Baghshah, Mahdieh Soleymani, Rabiee, Hamid R.

“…Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the…”
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SCGG: A deep structure-conditioned graph generative model by Faezeh Faez, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah, Hamid R Rabiee

“…Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems…”
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SCGG: A deep structure-conditioned graph generative model by Faez, Faezeh, Hashemi Dijujin, Negin, Soleymani Baghshah, Mahdieh, Rabiee, Hamid R

“…Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems…”
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Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization by Biparva, Mahdi, Karimi, Raika, Faez, Faezeh, Zhang, Yingxue

“…Temporal Graph Neural Networks have garnered substantial attention for their capacity to model evolving structural and temporal patterns while exhibiting…”
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Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers by Karimi, Raika, Faez, Faezeh, Zhang, Yingxue, Li, Xing, Chen, Lei, Yuan, Mingxuan, Biparva, Mahdi

“…Contemporary hardware design benefits from the abstraction provided by high-level logic gates, streamlining the implementation of logic circuits. Logic…”
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MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization by Faez, Faezeh, Karimi, Raika, Zhang, Yingxue, Li, Xing, Chen, Lei, Yuan, Mingxuan, Biparva, Mahdi

“…Electronic Design Automation (EDA) is essential for IC design and has recently benefited from AI-based techniques to improve efficiency. Logic synthesis, a key…”
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Deep Graph Generators: A Survey by Faez, Faezeh, Ommi, Yassaman, Baghshah, Mahdieh Soleymani, Rabiee, Hamid R

“…Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the…”
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DMNP: A Deep Learning Approach for Missing Node Prediction in Partially Observed Graphs by Faez, Faezeh, Amiri, Ali Akhoondian, Baghshah, Mahdieh Soleymani, Rabiee, Hamid R.

“…Missing data is unavoidable in graphs, which can significantly affect the accuracy of downstream tasks. Many methods have been proposed to mitigate missing…”
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SCGG: A Deep Structure-Conditioned Graph Generative Model by Faez, Faezeh, Dijujin, Negin Hashemi, Baghshah, Mahdieh Soleymani, Rabiee, Hamid R

“…Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems…”
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CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation by Ommi, Yassaman, Yousefabadi, Matin, Faez, Faezeh, Sabour, Amirmojtaba, Baghshah, Mahdieh Soleymani, Rabiee, Hamid R

“…Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the…”
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Deep Graph Generators: A Survey by Faez, Faezeh, Ommi, Yassaman, Baghshah, Mahdieh Soleymani, Rabiee, Hamid R

“…Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the…”
Get full text