Search Results for author: Runzhong Wang

Found 22 papers, 18 papers with code

Batched Bayesian optimization with correlated candidate uncertainties

1 code implementation8 Oct 2024 Jenna Fromer, Runzhong Wang, Mrunali Manjrekar, Austin Tripp, José Miguel Hernández-Lobato, Connor W. Coley

Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library.

Bayesian Optimization Diversity +1

Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks

2 code implementations6 Sep 2024 Runzhong Wang, Yang Li, Junchi Yan, Xiaokang Yang

In this paper, we design a family of non-autoregressive neural networks to solve CO problems under positive linear constraints with the following merits.

Combinatorial Optimization Traveling Salesman Problem

Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search

2 code implementations8 Jul 2024 Kevin Yu, Jihye Roh, Ziang Li, Wenhao Gao, Runzhong Wang, Connor W. Coley

Under this formulation, we propose Double-Ended Synthesis Planning (DESP), a novel CASP algorithm under a bidirectional graph search scheme that interleaves expansions from the target and from the goal starting materials to ensure constraint satisfiability.

Retrosynthesis valid

GMTR: Graph Matching Transformers

1 code implementation14 Nov 2023 Jinpei Guo, Shaofeng Zhang, Runzhong Wang, Chang Liu, Junchi Yan

Meanwhile, on Pascal VOC, QueryTrans improves the accuracy of NGMv2 from $80. 1\%$ to $\mathbf{83. 3\%}$, and BBGM from $79. 0\%$ to $\mathbf{84. 5\%}$.

 Ranked #1 on Graph Matching on PASCAL VOC (matching accuracy metric)

Graph Attention Graph Matching +2

Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime

1 code implementation13 Nov 2023 Haoyu Geng, Hang Ruan, Runzhong Wang, Yang Li, Yang Wang, Lei Chen, Junchi Yan

Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.

Benchmarking Combinatorial Optimization +3

Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions

1 code implementation8 Nov 2023 Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, Junchi Yan

To address these challenges under open-world assumptions, we design an \textbf{A}daptive \textbf{M}ulti-\textbf{I}nterest \textbf{D}ebiasing framework for cross-domain sequential recommendation (\textbf{AMID}), which consists of a multi-interest information module (\textbf{MIM}) and a doubly robust estimator (\textbf{DRE}).

Sequential Recommendation

Deep Learning of Partial Graph Matching via Differentiable Top-K

1 code implementation CVPR 2023 Runzhong Wang, Ziao Guo, Shaofei Jiang, Xiaokang Yang, Junchi Yan

Graph matching (GM) aims at discovering node matching between graphs, by maximizing the node- and edge-wise affinities between the matched elements.

 Ranked #1 on Graph Matching on Willow Object Class (F1 score metric)

Deep Learning Graph Matching +1

MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization

1 code implementation18 Apr 2022 Wujiang Xu, Runzhong Wang, Xiaobo Guo, Shaoshuai Li, Qiongxu Ma, Yunan Zhao, Sheng Guo, Zhenfeng Zhu, Junchi Yan

However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content.

Video Summarization

Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond

1 code implementation CVPR 2022 Qibing Ren, Qingquan Bao, Runzhong Wang, Junchi Yan

We first show that an adversarial attack on keypoint localities and the hidden graphs can cause significant accuracy drop to deep GM models.

Ranked #7 on Graph Matching on PASCAL VOC (matching accuracy metric)

Adversarial Attack Data Augmentation +2

A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs

no code implementations28 Dec 2021 Han Lu, Zenan Li, Runzhong Wang, Qibing Ren, Junchi Yan, Xiaokang Yang

Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research.

Adversarial Attack Combinatorial Optimization

On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK

no code implementations29 Sep 2021 Runzhong Wang, Li Shen, Yiting Chen, Junchi Yan, Xiaokang Yang, DaCheng Tao

Cardinality constrained combinatorial optimization requires selecting an optimal subset of $k$ elements, and it will be appealing to design data-driven algorithms that perform TopK selection over a probability distribution predicted by a neural network.

Combinatorial Optimization One-Shot Learning +1

Learning Latent Topology for Graph Matching

no code implementations1 Jan 2021 Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li

Graph matching (GM) has been traditionally modeled as a deterministic optimization problem characterized by an affinity matrix under pre-defined graph topology.

Graph Generation Graph Matching +1

Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching

2 code implementations16 Dec 2020 Chang Liu, Zetian Jiang, Runzhong Wang, Junchi Yan, Lingxiao Huang, Pinyan Lu

As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.

Combinatorial Optimization Decision Making +4

Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning

1 code implementation NeurIPS 2020 Runzhong Wang, Junchi Yan, Xiaokang Yang

This paper considers the setting of jointly matching and clustering multiple graphs belonging to different groups, which naturally rises in many realistic problems.

Clustering Graph Matching

Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

1 code implementation CVPR 2021 Runzhong Wang, Tianqi Zhang, Tianshu Yu, Junchi Yan, Xiaokang Yang

This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver.

Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach.

1 code implementation TPAMI 2020 Runzhong Wang, Junchi Yan and Xiaokang Yang.

Our approach enjoys flexibility in that the permutation loss is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network can deal with varying numbers of nodes for both training and inference.

Graph Matching

Learning deep graph matching with channel-independent embedding and Hungarian attention

no code implementations ICLR 2020 Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li

Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete.

Ranked #16 on Graph Matching on PASCAL VOC (matching accuracy metric)

Graph Matching Hard Attention

InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting

3 code implementations ICCV 2019 Hao-Shu Fang, Jianhua Sun, Runzhong Wang, Minghao Gou, Yong-Lu Li, Cewu Lu

With the guidance of such map, we boost the performance of R101-Mask R-CNN on instance segmentation from 35. 7 mAP to 37. 9 mAP without modifying the backbone or network structure.

Data Augmentation Instance Segmentation +3

Learning Combinatorial Embedding Networks for Deep Graph Matching

1 code implementation ICCV 2019 Runzhong Wang, Junchi Yan, Xiaokang Yang

In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises.

Graph Embedding Graph Matching

Cannot find the paper you are looking for? You can Submit a new open access paper.