no code implementations • 2 Dec 2023 • Muyao Zhong, Shengcai Liu, Bingdong Li, Haobo Fu, Ke Tang, Peng Yang
With this advantage, this paper is able to at the first time report the results of solving 1000-dimensional TSPs by training a PtrNet on the same dimensionality, which strongly suggests that scaling up the training instances is in need to improve the performance of PtrNet on solving higher-dimensional COPs.
1 code implementation • 29 Oct 2023 • Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, Yew-Soon Ong
Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions.
no code implementations • 15 Oct 2023 • Jiahao Wu, Qijiong Liu, Hengchang Hu, Wenqi Fan, Shengcai Liu, Qing Li, Xiao-Ming Wu, Ke Tang
Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset.
no code implementations • 2 Oct 2023 • Jiahao Wu, Wenqi Fan, Shengcai Liu, Qijiong Liu, Rui He, Qing Li, Ke Tang
However, applying existing approaches to condense recommendation datasets is impractical due to following challenges: (i) sampling-based methods are inadequate in addressing the long-tailed distribution problem; (ii) synthesizing-based methods are not applicable due to discreteness of interactions and large size of recommendation datasets; (iii) neither of them fail to address the specific issue in recommendation of false negative items, where items with potential user interest are incorrectly sampled as negatives owing to insufficient exposure.
no code implementations • 22 Sep 2023 • Jiahao Wu, Wenqi Fan, Shengcai Liu, Qijiong Liu, Qing Li, Ke Tang
To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items.
no code implementations • 27 Aug 2023 • Wenjie Chen, Shengcai Liu, Yew-Soon Ong, Ke Tang
Moreover, given a real-time constraint of one minute, the NIE-based method can solve IBM problems with up to hundreds of thousands of nodes, which is at least one order of magnitude larger than what can be solved by existing methods.
2 code implementations • 26 Jun 2023 • Xuanfeng Li, Shengcai Liu, Jin Wang, Xiao Chen, Yew-Soon Ong, Ke Tang
In particular, we focus on the practical scenario of CCMCKP, where the probability distributions of random weights are unknown but only sample data is available.
1 code implementation • 18 May 2023 • Ning Lu, Shengcai Liu, Rui He, Qi Wang, Yew-Soon Ong, Ke Tang
Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public.
no code implementations • 4 May 2023 • Rui He, Shengcai Liu, Jiahao Wu, Shan He, Ke Tang
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains.
no code implementations • 6 Feb 2023 • Ning Lu, Shengcai Liu, Zhirui Zhang, Qi Wang, Haifeng Liu, Ke Tang
Our comprehensive experiments reveal that in approximately 90\% of cases, word-level attacks lead to the generation of examples where the frequency of $n$-grams decreases, a tendency we term as the $n$-gram Frequency Descend ($n$-FD).
1 code implementation • 23 Nov 2022 • Shengcai Liu, Fu Peng, Ke Tang
Attack Ensemble (AE), which combines multiple attacks together, provides a reliable way to evaluate adversarial robustness.
no code implementations • 17 Nov 2022 • Xiasheng Ma, Shengcai Liu, Wenjing Hong
It has been widely observed that there exists no universal best Multi-objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-objective Optimization Problems (MOPs).
no code implementations • 22 Sep 2022 • Shengcai Liu, Yu Zhang, Ke Tang, Xin Yao
Hopefully, this work would help with a better understanding of the strengths and weaknesses of NCO and provide a comprehensive evaluation protocol for further benchmarking NCO approaches in comparison to other approaches.
1 code implementation • 18 Aug 2022 • Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qing Li, Ke Tang
In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.
1 code implementation • 4 Jun 2022 • Zeyu Dai, Shengcai Liu, Ke Tang, Qing Li
In this paper, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived.
1 code implementation • 23 Dec 2021 • Fu Peng, Shengcai Liu, Ning Lu, Ke Tang
This work considers a challenging Deep Neural Network(DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations.
1 code implementation • 2 Nov 2021 • Shengcai Liu, Ning Lu, Wenjing Hong, Chao Qian, Ke Tang
The field of adversarial textual attack has significantly grown over the last few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model.
no code implementations • 29 Sep 2021 • Zeyu Dai, Shengcai Liu, Ke Tang, Qing Li
To address this issue, in this paper we propose to use segmentation priors for black-box attacks such that the perturbations are limited in the salient region.
no code implementations • 6 Sep 2021 • Shengcai Liu, Ning Lu, Cheng Chen, Ke Tang
Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing.
1 code implementation • 25 Jun 2021 • Rui He, Shengcai Liu, Shan He, Ke Tang
Active learning (AL) can be utilized in MDL to reduce the labeling effort by only using the most informative data.
no code implementations • 20 Jan 2021 • Wenjie Chen, Shengcai Liu, Ke Tang
An unbiased estimator of the gradient of the new acquisition function is derived to implement the $c-\rm{KG}$ approach.
1 code implementation • 12 Nov 2020 • Shengcai Liu, Ke Tang, Xin Yao
The Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW) has attracted much research interest in the last decade, due to its wide application in modern logistics.
no code implementations • 1 Jul 2020 • Ke Tang, Shengcai Liu, Peng Yang, Xin Yao
In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of training problem instances, which is often referred to as PAP construction.
no code implementations • 1 Jun 2020 • Kangfei Zhao, Shengcai Liu, Yu Rong, Jeffrey Xu Yu
To solve TSP efficiently, in addition to developing new TSP solvers, it needs to find a per-instance solver for each TSP instance, which is known as the TSP solver selection problem.
no code implementations • 19 Nov 2019 • Shengcai Liu, Ke Tang, Yunwen Lei, Xin Yao
Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress.
no code implementations • 17 Apr 2018 • Shengcai Liu, Ke Tang, Xin Yao
Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems.
no code implementations • 29 Mar 2017 • Shengcai Liu, Ke Tang, Xin Yao
The idea behind LiangYi is to promote the population-based solver by training it (with the training module) to improve its performance on those instances (discovered by the sampling module) on which it performs badly, while keeping the good performances obtained by it on previous instances.