Search Results for author: Jiajun Li

Found 8 papers, 4 papers with code

RecDiffusion: Rectangling for Image Stitching with Diffusion Models

1 code implementation28 Mar 2024 Tianhao Zhou, Haipeng Li, Ziyi Wang, Ao Luo, Chen-Lin Zhang, Jiajun Li, Bing Zeng, Shuaicheng Liu

Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.

Image Stitching

POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems

1 code implementation31 Mar 2023 YiXuan Wang, Weichao Zhou, Jiameng Fan, Zhilu Wang, Jiajun Li, Xin Chen, Chao Huang, Wenchao Li, Qi Zhu

We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.

PathSAGE: Spatial Graph Attention Neural Networks With Random Path Sampling

no code implementations11 Mar 2022 Junhua Ma, Jiajun Li, Xueming Li, Xu Li

To address these problems, we propose a model called PathSAGE, which can learn high-order topological information and improve the model's performance by expanding the receptive field.

Graph Attention

POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems

2 code implementations25 Jun 2021 Chao Huang, Jiameng Fan, Zhilu Wang, YiXuan Wang, Weichao Zhou, Jiajun Li, Xin Chen, Wenchao Li, Qi Zhu

We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

1 code implementation11 Aug 2018 Kan Ren, Yuchen Fang, Wei-Nan Zhang, Shuhao Liu, Jiajun Li, Ya zhang, Yong Yu, Jun Wang

To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.

Personalizing a Dialogue System with Transfer Reinforcement Learning

no code implementations10 Oct 2016 Kaixiang Mo, Shuangyin Li, Yu Zhang, Jiajun Li, Qiang Yang

One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.

reinforcement-learning Reinforcement Learning (RL) +1

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