10 citations · 13 across the 8 of their papers we have counts for
10 papers
From Sight to Foresight: Predictive Spatial Reasoning in Vision-Language Models
Feiran Wang, Xiaoqi Wang, Ziwei Li +3
Predicting future spatial states supports collision avoidance and timely decision-making in dynamic environments. However, existing vision-language models (VLMs) and benchmarks for…
From Fragments to Global Maps: Learning Vectorized Map Aggregation with Large Language Models
Ziwei Li, Yi-Tang Chen, Xiaoqi Wang +3
Large-scale vectorized HD maps provide structured road information that is essential for perception, localization, and planning in autonomous driving. Constructing such maps requir…
GS-Surrogate: Deformable Gaussian Splatting for Parameter Space Exploration of Ensemble Simulations
Ziwei Li, Rumali Perera, Angus Forbes +5
Exploring ensemble simulations is increasingly important across many scientific domains. However, supporting flexible post-hoc exploration remains challenging due to the trade-off…
SaviorRec: Semantic-Behavior Alignment for Cold-Start Recommendation
Yining Yao, Ziwei Li, Shuwen Xiao +5
In recommendation systems, predicting Click-Through Rate (CTR) is crucial for accurately matching users with items. To improve recommendation performance for cold-start and long-ta…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
FA-INR: Adaptive Implicit Neural Representations for Interpretable Exploration of Simulation Ensembles
Ziwei Li, Yuhan Duan, Tianyu Xiong +3
Surrogate models are essential for efficient exploration of large-scale ensemble simulations. Implicit neural representations (INRs) provide a compact and continuous framework for…
Neural Approaches to SAT Solving: Design Choices and Interpretability
David Mojžíšek, Jan Hůla, Ziwei Li +2
In this contribution, we provide a comprehensive evaluation of graph neural networks applied to Boolean satisfiability problems, accompanied by an intuitive explanation of the mech…
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Ziwei Li, Xiaoqi Wang, Hong-You Chen +2
Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. D…
On the Importance and Applicability of Pre-Training for Federated Learning
Hong-You Chen, Cheng-Hao Tu, Ziwei Li +2
Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly in…
Learn Like The Pro: Norms from Theory to Size Neural Computation
Margaret Trautner, Ziwei Li, Sai Ravela
The optimal design of neural networks is a critical problem in many applications. Here, we investigate how dynamical systems with polynomial nonlinearities can inform the design of…