37 citations · 85 across the 9 of their papers we have counts for
9 papers
TransNav: Transformer-Based End-to-End Navigation Method for Static and Dynamic Quadrotor Obstacle Avoidance
Jianghan Zhu, Zhuo Li, Yanjun Zhang +1
Obstacle avoidance for autonomous quadrotors in cluttered environments remains challenging, especially when both dense static obstacles and moving obstacles are present. Existing m…
Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots
Haomin Fu, Yeqin Zhang, Haiyang Yu +5
This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations. This is of particular interest for companies and organizati…
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven
Qingtao Zhao, Jennie Si, Jian Sun
In this paper time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. Among existing approximate…
Dynamic Memory Induction Networks for Few-Shot Text Classification
Ruiying Geng, Binhua Li, Yongbin Li +2
This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification. The model utilizes dynamic routing to provide more flexibility to memory-based few-sh…
A Survey on Dialog Management: Recent Advances and Challenges
Yinpei Dai, Huihua Yu, Yixuan Jiang +3
Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog a…
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness
Qiuling Yang, Alireza Sadeghi, Gang Wang +2
Distributed renewable generation, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today's power systems (PS). In this context,…
A Statistical Learning Approach to Reactive Power Control in Distribution Systems
Qiuling Yang, Alireza Sadeghi, Gang Wang +2
Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, moti…
Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning
Qiuling Yang, Gang Wang, Alireza Sadeghi +2
Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable ge…
Induction Networks for Few-Shot Text Classification
Ruiying Geng, Binhua Li, Yongbin Li +3
Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to s…