6 citations · 13 across the 6 of their papers we have counts for
7 papers
InvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generation
Faezeh Faez, Marzieh S. Tahaei, Yaochen Hu +4
Large Language Models (LLMs) have revolutionized the ability to understand and generate text, enabling significant progress in automatic knowledge graph construction from text (Tex…
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization
Faezeh Faez, Raika Karimi, Yingxue Zhang +4
Electronic Design Automation (EDA) is essential for IC design and has recently benefited from AI-based techniques to improve efficiency. Logic synthesis, a key EDA stage, transform…
Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers
Raika Karimi, Faezeh Faez, Yingxue Zhang +4
Contemporary hardware design benefits from the abstraction provided by high-level logic gates, streamlining the implementation of logic circuits. Logic Synthesis Optimization (LSO)…
Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization
Mahdi Biparva, Raika Karimi, Faezeh Faez +1
Temporal Graph Neural Networks have garnered substantial attention for their capacity to model evolving structural and temporal patterns while exhibiting impressive performance. Ho…
SCGG: A Deep Structure-Conditioned Graph Generative Model
Faezeh Faez, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah +1
Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods…
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
Yassaman Ommi, Matin Yousefabadi, Faezeh Faez +3
Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph gene…
Deep Graph Generators: A Survey
Faezeh Faez, Yassaman Ommi, Mahdieh Soleymani Baghshah +1
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 advances in graph-based deep…