December 2024
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4 Reads
Neural Networks
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December 2024
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4 Reads
Neural Networks
November 2024
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6 Reads
Information Sciences
October 2024
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19 Reads
Neural Networks
April 2024
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24 Reads
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4 Citations
Information Sciences
March 2024
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18 Reads
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3 Citations
Neurocomputing
February 2024
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11 Reads
Science China Mathematics
Combinatorial optimization (CO) on graphs is a classic topic that has been extensively studied across many scientific and industrial fields. Recently, solving CO problems on graphs through learning methods has attracted great attention. Advanced deep learning methods, e.g., graph neural networks (GNNs), have been used to effectively assist the process of solving COs. However, current frameworks based on GNNs are mainly designed for certain CO problems, thereby failing to consider their transferable and generalizable abilities among different COs on graphs. Moreover, simply using original graphs to model COs only captures the direct correlations among objects, which does not consider the mathematical logicality and properties of COs. In this paper, we propose a unified pre-training and adaptation framework for COs on graphs with the help of the maximum satisfiability (Max-SAT) problem. We first use Max-SAT to bridge different COs on graphs since they can be converted to Max-SAT problems represented by standard formulas and clauses with logical information. Then we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from them. In the pre-training stage, Max-SAT instances are generated to initialize the parameters of the model. In the fine-tuning stage, instances from CO and Max-SAT problems are used for adaptation so that the transferable ability can be further improved. Numerical experiments on several datasets show that features extracted by our framework exhibit superior transferability and Max-SAT can boost the ability to solve COs on graphs.
January 2024
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3 Reads
Procedia Computer Science
January 2024
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3 Reads
Procedia Computer Science
November 2023
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15 Reads
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3 Citations
Information Sciences
May 2023
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21 Reads
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10 Citations
Pattern Recognition Letters
... In many instances, less than 1 percent of the inputs exhibit non-zero values. AI tools consider such raw material as sparse [26], emphasizing the scattered nature of the entire dataset [27]. ...
March 2024
Neurocomputing
... Many existing works [48], [49] use t-SNE to visualize the embeddings in order to explain the effectiveness of the attack methods. Therefore, inspired by this, we use t-SNE for visualization. ...
November 2023
Information Sciences
... The VRP is classified as a non-polynomial hard problem. After the first mention, the problem was investigated for diverse applications, changing through the years with formulations more robust and realistic than previously [15]. The main constraints to solve the problem are related to the vehicle's capacity and time windows to perform the service. ...
January 2023
Procedia Computer Science
... These analysis tools uncover hidden information and provide a better understanding of network dynamics. The graph partitioning problem is a classic combinatorial optimization problem that has been the subject of much research [78]. It aims to divide a graph in two subgraphs by minimizing the total cost of the cut edges. ...
January 2023
Procedia Computer Science
... The y i is the true label and y i is the predicted probability distribution. The equation 3 is L1 regularization loss, it represents the sum of the absolute values of the model parameters and is used to prevent overfitting [40]. By incorporating the L1 regularization term into the loss function, the model is encouraged to produce sparse parameters, meaning that more parameters are driven toward zero. ...
May 2023
Pattern Recognition Letters
... However, the latent weights oscillating around the center of quantization bin causes implicit stochasticity during the binary training. As shown in Figure 1, we observe that the learned latent weights tend to cluster around their mean centers, and the distribution exhibits a pronounced peak near 0, which coincides with previous observations [20,44]. We argue that the closer the latent weights are to the point 0 (i.e., the center of the quantized bin), the more easily their gradients are reversed, leading to the weights oscillation 1 Thus, it requires a regularization scheme to induce a bimodal distribution of weights that can solve the oscillation problem. ...
March 2023
Pattern Recognition
... Addressing client drift issues involves employing model aggregation and model training methods [10]. FedAvgM [11] introduces momentum on top of Stochastic Gradient Descent (SGD) for model aggregation, a concept similarly utilized in FedMIM [12]. ...
December 2022
Procedia Computer Science
... In this context, many deep learning approaches have been proposed since the emergence of the disease. These strategies show promise for automating COVID-19 diagnosis and reducing the strain on medical systems [10]. Several researchers analyzed both chest X-ray and CT scan images using computer vision approaches that presented good results. ...
November 2022
Methods
... To understand the gravity of the issue, contrast OT with another divergence, the Maximum Mean Discrepancy [34], whose sample complexity is dimension independent! Finally, there are limitations inherent to the vanilla OT formulation itself such as sensitivity to outliers [35], inability to incorporate context/structure in the optimal plan [36], strong reliance on the chosen ground cost [37], assuming a common ground cost [38] and the inability to handle measures (generalized, un-normalized distributions) of non-equivalent masses (normalization constants) [39]. Unsatisfied with these limitations, researchers have produced a diverse spectrum of OT formulations over the years with the potential to meet a broader range of applications and requirements. ...
February 2022
Procedia Computer Science
... We can better understand the correlation between nodes and their labels when considering the semantic connections among the nodes through their neighborhoods. In Figure 2, nodes p1 and p2 have multi-label neighbors [36], so a path from p1 to p2 is determined as p1.2. This technique strengthens the communication between the nodes, and rich information is obtained from the paths. ...
July 2022
Neural Networks