3 citations · 5 across the 10 of their papers we have counts for
10 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…
When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation
Matin Aghaei, Lingfeng Zhang, Mohammad Ali Alomrani +2
Recent ObjectNav systems credit large language models (LLMs) for sizable zero-shot gains, yet it remains unclear how much comes from language versus geometry. We revisit this quest…
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)…
EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems
Mohammad Dehghan, Mohammad Ali Alomrani, Sunyam Bagga +12
The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems i…
Scalable Graph Self-Supervised Learning
Ali Saheb Pasand, Reza Moravej, Mahdi Biparva +2
In regularization Self-Supervised Learning (SSL) methods for graphs, computational complexity increases with the number of nodes in graphs and embedding dimensions. To mitigate the…
WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization
Ali Saheb Pasand, Reza Moravej, Mahdi Biparva +1
A common phenomena confining the representation quality in Self-Supervised Learning (SSL) is dimensional collapse (also known as rank degeneration), where the learned representatio…
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…
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning
Mahdi Naseri, Mahdi Biparva
Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep learning models without relying on expensive annotated labels, instead leveraging embedded s…
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang +1
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns. However, previous works often rely…