Hanmo Liu
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View article: RobustFlow: Towards Robust Agentic Workflow Generation
RobustFlow: Towards Robust Agentic Workflow Generation Open
The automated generation of agentic workflows is a promising frontier for enabling large language models (LLMs) to solve complex tasks. However, our investigation reveals that the robustness of agentic workflow remains a critical, unaddres…
Beyond Model Base Selection: Weaving Knowledge to Master Fine-grained Neural Network Design Open
Database systems have recently advocated for embedding machine learning (ML) capabilities, offering declarative model queries over large, managed model repositories, thereby circumventing the huge computational overhead of traditional ML-b…
View article: When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction Open
Temporal link prediction in dynamic graphs is a critical task with applications in diverse domains such as social networks, recommendation systems, and e-commerce platforms. While existing Temporal Graph Neural Networks (T-GNNs) have achie…
View article: Real-time Two-tape Control System in Vine robots
Real-time Two-tape Control System in Vine robots Open
This paper focuses on how to make a growing Vine robot steer in different directions with a novel approach to real-time steering control by autonomously applying adhesive tape to induce a surface wrinkles. This enabling real-time direction…
A Selective Learning Method for Temporal Graph Continual Learning Open
Node classification is a key task in temporal graph learning (TGL). Real-life temporal graphs often introduce new node classes over time, but existing TGL methods assume a fixed set of classes. This assumption brings limitations, as updati…
View article: Computation-friendly Graph Neural Network Design by Accumulating Knowledge on Large Language Models
Computation-friendly Graph Neural Network Design by Accumulating Knowledge on Large Language Models Open
Graph Neural Networks (GNNs), like other neural networks, have shown remarkable success but are hampered by the complexity of their architecture designs, which heavily depend on specific data and tasks. Traditionally, designing proper arch…
Identifying and updating local optimization methods in extended Kalman filter SLAM Open
he essence of simultaneous location and mapping (SLAM) is an estimation problem. It requires mobile robots to use the sensor information to estimate the structure of the external environment in real-time and their position in the backgroun…