35 citations · 38 across the 10 of their papers we have counts for
11 papers
Tracing Morphology, Semantics, and Divergence in Language Acquisition
Forough Shirin Abkenar, Haoshuo Fu, Liang Feng +2
Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve into structured linguistic capab…
Embodied Task Planning via Graph-Informed Action Generation with Large Language Models
Xiang Li, Ning Yan, Masood Mortazavi
While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon…
EvoMem: Improving Multi-Agent Planning with Dual-Evolving Memory
Wenzhe Fan, Ning Yan, Masood Mortazavi
Planning has been a cornerstone of artificial intelligence for solving complex problems, and recent progress in LLM-based multi-agent frameworks have begun to extend this capabilit…
PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning
Zihan Zhao, Kaushik Pendiyala, Masood Mortazavi +1
Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor-asymmetry problem: the richest modali…
LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs
Amit Roy, Ning Yan, Masood Mortazavi
Dynamic Text-Attributed Graphs (DyTAGs) have numerous real-world applications, e.g. social, collaboration, citation, communication, and review networks. In these networks, nodes an…
Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning
Tao Zhang, Ning Yan, Masood Mortazavi +3
Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning capability on many NLP tasks. Recasting an NLP task into a text-to-text g…
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism
Yimin Tang, Yurong Xu, Ning Yan +1
Transformers have a quadratic scaling of computational complexity with input size, which limits the input context window size of large language models (LLMs) in both training and i…
Fusion of regional and sparse attention in Vision Transformers
Nabil Ibtehaz, Ning Yan, Masood Mortazavi +1
Modern vision transformers leverage visually inspired local interaction between pixels through attention computed within window or grid regions, in contrast to the global attention…
ACC-ViT : Atrous Convolution's Comeback in Vision Transformers
Nabil Ibtehaz, Ning Yan, Masood Mortazavi +1
Transformers have elevated to the state-of-the-art vision architectures through innovations in attention mechanism inspired from visual perception. At present two classes of attent…
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems
Mi Zhou, Jiazhi Li, Masood Mortazavi +2
In this article, a \underline{S}tate-dependent \underline{M}ulti-\underline{A}gent \underline{D}eep \underline{D}eterministic \underline{P}olicy \underline{G}radient (\textbf{SMADD…
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
Yihong Ma, Ning Yan, Jiayu Li +2
Graphs have emerged as a natural choice to represent and analyze the intricate patterns and rich information of the Web, enabling applications such as online page classification an…