作者: 不要嚴(yán)酷 時(shí)間: 2025-3-21 21:31 作者: APNEA 時(shí)間: 2025-3-22 03:23 作者: breadth 時(shí)間: 2025-3-22 05:45 作者: 流動(dòng)才波動(dòng) 時(shí)間: 2025-3-22 10:22
Stochastic and?Dual Adversarial GAN-Boosted Zero-Shot Knowledge Graph, generative adversarial network (GAN) has been used in zero-shot learning for KG completion. However, existing works on GAN-based zero-shot KG completion all use traditional simple architecture without randomness in generator, which greatly limits the ability of GAN mining knowledge on complex data作者: Overthrow 時(shí)間: 2025-3-22 14:34 作者: 親愛(ài) 時(shí)間: 2025-3-22 20:39 作者: Cloudburst 時(shí)間: 2025-3-22 23:56 作者: 山羊 時(shí)間: 2025-3-23 04:08
Region-Based Dense Adversarial Generation for Medical Image Segmentationmples, making robustness a key factor of DNNs when applied in the field of medical research. In this paper, in order to evaluate the robustness of medical image segmentation networks, we propose a novel Region-based Dense Adversary Generation (RDAG) method to generate adversarial examples. Specifica作者: 生意行為 時(shí)間: 2025-3-23 07:28 作者: hurricane 時(shí)間: 2025-3-23 09:41
Dynamic Network Embedding by Using Sparse Deep Autoencodercant attention. Almost all existing static network embedding and dynamic network embedding methods that employ deep models adopt dense structures. Deep models can ensure that the network embedding achieves a good effect on the task (link prediction, network reconstruction, etc.); however, all works 作者: 過(guò)剩 時(shí)間: 2025-3-23 15:07 作者: 草率男 時(shí)間: 2025-3-23 19:00 作者: Explicate 時(shí)間: 2025-3-24 01:48
A Novel Nonlinear Dictionary Learning Algorithm Based on Nonlinear-KSVD and Nonlinear-MOD most commonly applied method, and it is typically utilized to address various signal processing problems. However, linear dictionary learning cannot meet the requirements of nonlinear signal processing, and the nonlinear signals cannot be accurately simulated and processed. In this study, we first 作者: 并入 時(shí)間: 2025-3-24 02:24 作者: 乞討 時(shí)間: 2025-3-24 08:30
stance measure, and illustrate the rationality of the method with the analysis and comparison of numerical examples. Finally, the method is applied to practical medical diagnosis. The experimental results show that the new method has certain validity and feasibility.作者: arousal 時(shí)間: 2025-3-24 12:18 作者: licence 時(shí)間: 2025-3-24 15:02
Conference proceedings 2022igence, held in?Beijing, China, in August 2022.?CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI). ..The 164 papers were thoroughly reviewed and selected from 521 submissions.?CICAI aims to establis作者: 古代 時(shí)間: 2025-3-24 20:46 作者: Allergic 時(shí)間: 2025-3-25 01:21
Adaptive Combination of Filtered-X NLMS and Affine Projection Algorithms for Active Noise Control FxNLMS algorithm and FxAP algorithm, and a coupling factor designed by gradient descent is used to update the filter weights. The simulation experiment results in stationary and nonstationary scenarios demonstrate the better performance of the proposed algorithm as compared with the conventional algorithms.作者: Junction 時(shí)間: 2025-3-25 06:03 作者: 吞吞吐吐 時(shí)間: 2025-3-25 07:51 作者: heirloom 時(shí)間: 2025-3-25 13:47
Using Data Pump Export and Importping feature extraction ability as much as possible. The LS-YOLO detection speed is up to 1.2 ms, the accuracy (AP) is 96.6%, and the model size is only 3.8 MB. It can balance detection accuracy and detection speed, and provide reference for the construction of real-time detection network.作者: 泄露 時(shí)間: 2025-3-25 17:34 作者: llibretto 時(shí)間: 2025-3-25 20:01 作者: orthodox 時(shí)間: 2025-3-26 02:52 作者: Pantry 時(shí)間: 2025-3-26 05:19 作者: 品牌 時(shí)間: 2025-3-26 08:56
Region-Based Dense Adversarial Generation for Medical Image Segmentationimage segmentation on DRIVE and CELL datasets. The experimental results show that our proposed method achieves effective attack results on both datasets for medical image segmentation when compared with several state-of-the-art methods.作者: 類人猿 時(shí)間: 2025-3-26 13:43 作者: 強(qiáng)行引入 時(shí)間: 2025-3-26 17:03 作者: corn732 時(shí)間: 2025-3-26 23:02
0302-9743 ial Intelligence, held in?Beijing, China, in August 2022.?CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI). ..The 164 papers were thoroughly reviewed and selected from 521 submissions.?CICAI aims t作者: extinct 時(shí)間: 2025-3-27 02:04 作者: 任命 時(shí)間: 2025-3-27 08:27
Tooth Defect Segmentation in 3D Mesh Scans Using Deep Learningmentation. With predefined geometrical features as inputs and a focal loss for training guidance, we achieve state-of-the-art performance on 3D tooth defect segmentation. Our work exhibits the great potential of artificial intelligence for future digital dentistry.作者: EXCEL 時(shí)間: 2025-3-27 12:56 作者: zonules 時(shí)間: 2025-3-27 16:01 作者: transient-pain 時(shí)間: 2025-3-27 17:49
Installing the Oracle Database 10, RDBMSc generator and an additional classifier to improve the model’s ability of approximating features and classifying unseen tasks. The experiments on NELL-ZS and Wiki-ZS datasets show that the proposed SDA outperforms the classic methods in zero-shot KG completion task. In particular, the proposed SDA 作者: Torrid 時(shí)間: 2025-3-27 22:26
User Management and Database Securityy, we employ the proximal splitting method to update the multivariate optimization problem. Hence, the non-convex sparsity regularized dictionary learning-based RL is developed and validated in different benchmark RL environments. The proposed algorithm can obtain the best control performances among作者: Osteoporosis 時(shí)間: 2025-3-28 04:33
Installing the Oracle Database 10, RDBMS. In the numerical experiments, our proposed DTSVN and MDTSVN are compared with the other four methods on MNIST, FASHION MNIST and CIFAR10 datasets. The results demonstrate that our DTSVN achieves the best prediction accuracy for the binary problem, and our MDTSVN significantly outperforms other exi作者: NORM 時(shí)間: 2025-3-28 06:53
User Management and Database Security make them much closer to IID and concentrating on the training the models in each cluster. Then we analyze the changing trend of model validity named model quality and define one suitable function to describe expiration dynamics. As a solution, we propose .ynamic .lustering .ederated .earning (DCFL作者: 烤架 時(shí)間: 2025-3-28 13:57
Installing the Oracle Database 10, RDBMSdata. Experimental results on simulated benchmark networks and real-world networks prove that, compared with existing network embedding methods utilizing dense structures, our method is able to greatly reduce the number of training weights, while minimally affecting or sometimes even improving the e作者: 某人 時(shí)間: 2025-3-28 15:51
User Management and Database Securityfeatures of the same node (positive samples) of the shallow GCN while alienating the features of other nodes (negative samples) , so that the deep GCN can learn the performance of the shallow GCN. Experiments show that our method can effectively alleviate the over-smoothing phenomenon. At the same t作者: AUGER 時(shí)間: 2025-3-28 19:43
Linguistic Interval-Valued Spherical Fuzzy Sets and Related Properties, we give the concept of the linguistic interval-valued spherical fuzzy number, and various operational laws, then the measure formula, score and accuracy functions of the linguistic interval-valued spherical fuzzy number are defined with a brief study of their related properties. At last, an admiss作者: 變形 時(shí)間: 2025-3-29 01:16
A Genetic Algorithm for?Causal Discovery Based on?Structural Causal Modelnd causal graph cyclicity, which effectively ensures the accuracy of causal discovery. In the search phase, an efficient random search is designed based on genetic algorithm, which greatly improves the causal discovery efficiency. This paper implements the corresponding algorithm, namely SCM-GA (Str作者: 畢業(yè)典禮 時(shí)間: 2025-3-29 06:04
Stochastic and?Dual Adversarial GAN-Boosted Zero-Shot Knowledge Graphc generator and an additional classifier to improve the model’s ability of approximating features and classifying unseen tasks. The experiments on NELL-ZS and Wiki-ZS datasets show that the proposed SDA outperforms the classic methods in zero-shot KG completion task. In particular, the proposed SDA 作者: SEED 時(shí)間: 2025-3-29 09:26
Dictionary Learning-Based Reinforcement Learning with Non-convex Sparsity Regularizery, we employ the proximal splitting method to update the multivariate optimization problem. Hence, the non-convex sparsity regularized dictionary learning-based RL is developed and validated in different benchmark RL environments. The proposed algorithm can obtain the best control performances among作者: FAWN 時(shí)間: 2025-3-29 14:06
Deep Twin Support Vector Networks. In the numerical experiments, our proposed DTSVN and MDTSVN are compared with the other four methods on MNIST, FASHION MNIST and CIFAR10 datasets. The results demonstrate that our DTSVN achieves the best prediction accuracy for the binary problem, and our MDTSVN significantly outperforms other exi作者: Hyperplasia 時(shí)間: 2025-3-29 19:08
Dynamic Clustering Federated Learning for Non-IID Data make them much closer to IID and concentrating on the training the models in each cluster. Then we analyze the changing trend of model validity named model quality and define one suitable function to describe expiration dynamics. As a solution, we propose .ynamic .lustering .ederated .earning (DCFL作者: 新鮮 時(shí)間: 2025-3-29 20:37
Dynamic Network Embedding by Using Sparse Deep Autoencoderdata. Experimental results on simulated benchmark networks and real-world networks prove that, compared with existing network embedding methods utilizing dense structures, our method is able to greatly reduce the number of training weights, while minimally affecting or sometimes even improving the e作者: Lice692 時(shí)間: 2025-3-30 02:27
Deep Graph Convolutional Networks Based on Contrastive Learning: Alleviating Over-smoothing Phenomenfeatures of the same node (positive samples) of the shallow GCN while alienating the features of other nodes (negative samples) , so that the deep GCN can learn the performance of the shallow GCN. Experiments show that our method can effectively alleviate the over-smoothing phenomenon. At the same t作者: 影響深遠(yuǎn) 時(shí)間: 2025-3-30 06:13 作者: 節(jié)省 時(shí)間: 2025-3-30 08:19 作者: Modify 時(shí)間: 2025-3-30 16:12 作者: exostosis 時(shí)間: 2025-3-30 19:08
Installing the Oracle Database 10, RDBMSthe basis for realizing strong artificial intelligence. However, the existing main causal discovery methods, including constraint-based methods, structural causal model based methods, and scoring-based methods, cannot find real causal relations accurately and quickly. In this paper, we propose a cau作者: CREEK 時(shí)間: 2025-3-30 21:41 作者: 宮殿般 時(shí)間: 2025-3-31 02:51
