Search Results for author: Wentai Zhang

Found 7 papers, 3 papers with code

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

1 code implementation13 Oct 2023 Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin

Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing.

Knowledge Base Question Answering Knowledge Graphs +2

Attention Routing: track-assignment detailed routing using attention-based reinforcement learning

no code implementations20 Apr 2020 Haiguang Liao, Qingyi Dong, Xuliang Dong, Wentai Zhang, Wangyang Zhang, Weiyi Qi, Elias Fallon, Levent Burak Kara

We also discover a similarity between the attention router and the baseline genetic router in terms of positive correlations in cost and routing patterns, which demonstrate the attention router's ability to be utilized not only as a detailed router but also as a predictor for routability and congestion.

reinforcement-learning Reinforcement Learning (RL)

A Deep Reinforcement Learning Approach for Global Routing

1 code implementation20 Jun 2019 Haiguang Liao, Wentai Zhang, Xuliang Dong, Barnabas Poczos, Kenji Shimada, Levent Burak Kara

At the heart of the proposed method is deep reinforcement learning that enables an agent to produce an optimal policy for routing based on the variety of problems it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders

no code implementations16 Apr 2019 Wentai Zhang, Zhangsihao Yang, Haoliang Jiang, Suyash Nigam, Soji Yamakawa, Tomotake Furuhata, Kenji Shimada, Levent Burak Kara

We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.

3D Shape Representation

Data-driven Upsampling of Point Clouds

no code implementations8 Jul 2018 Wentai Zhang, Haoliang Jiang, Zhangsihao Yang, Soji Yamakawa, Kenji Shimada, Levent Burak Kara

High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.

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