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Found 27 papers, 9 papers with code
Pointer Networks Trained Better via Evolutionary Algorithms
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.
Large Language Models as Evolutionary Optimizers
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.
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset.
Dataset Condensation for Recommendation
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.
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
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.
Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization
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.
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
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.
Large Language Models can be Guided to Evade AI-Generated Text Detection
Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public.
Multi-Domain Learning From Insufficient Annotations
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains.
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
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).
Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles
Attack Ensemble (AE), which combines multiple attacks together, provides a reliable way to evaluate adversarial robustness.
Automatic Construction of Parallel Algorithm Portfolios for Multi-objective Optimization
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).
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem
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.
Disentangled Contrastive Learning for Social Recommendation
In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack
In this paper, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived.
Training Quantized Deep Neural Networks via Cooperative Coevolution
This work considers a challenging Deep Neural Network(DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations.
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization
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.
Imperceptible Black-box Attack via Refining in Salient Region
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.
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack
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.
Multi-Domain Active Learning: Literature Review and Comparative Study
Active learning (AL) can be utilized in MDL to reduce the labeling effort by only using the most informative data.
A New Knowledge Gradient-based Method for Constrained Bayesian Optimization
An unbiased estimator of the gradient of the new acquisition function is derived to implement the $c-\rm{KG}$ approach.
Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows
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.
Few-shots Parallel Algorithm Portfolio Construction via Co-evolution
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.
Towards Feature-free TSP Solver Selection: A Deep Learning Approach
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.
On Performance Estimation in Automatic Algorithm Configuration
Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress.
Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems.
Experience-based Optimization: A Coevolutionary Approach
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.
