標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2022; 31st International C Elias Pimenidis,Plamen Angelov,Mehmet Aydin Conference p [打印本頁(yè)] 作者: 吸收 時(shí)間: 2025-3-21 18:38
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022影響因子(影響力)
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022影響因子(影響力)學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022網(wǎng)絡(luò)公開度
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022被引頻次
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022被引頻次學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022年度引用
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022年度引用學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2022讀者反饋學(xué)科排名
作者: Protein 時(shí)間: 2025-3-21 20:28 作者: FECK 時(shí)間: 2025-3-22 03:07
Grundlagen zum Schneideneingriff,good detection effect for different sizes of fires. The mean Average Precision (mAP) value reaches 88.7%, 8% higher than that of YOLOv5s mAP. The proposed model has the advantages of strong generalization and high precision.作者: Cuisine 時(shí)間: 2025-3-22 06:08
Grundlagen zum Schneideneingriff,-of-the-art models on both intra-scenario H36M and cross-scenario 3DPW datasets and lead to appreciable improvements in poses with more similar local features. Notably, it yields an overall improvement of 3.4?mm in MPJPE (relative 6.8. improvement) over the previous best feature fusion based method?[.] on H36M dataset in 3D human pose estimation.作者: 直覺好 時(shí)間: 2025-3-22 09:41
Elektrochemisches Abtragen (ECM),on between local, global and contextual information of other feature layers. In order to optimize the anchor configurations, a differential evolution algorithm is employed to reconfigure the ratios and scales of anchors. Experimental results show that the proposed method achieves superior detection performance on the public dataset PASCAL VOC.作者: 使隔離 時(shí)間: 2025-3-22 15:57 作者: FICE 時(shí)間: 2025-3-22 20:50
https://doi.org/10.1007/978-3-540-48954-2e and computer science, respectively. In addition, the results of the classification are visualized by evaluating the sentence combinations in the abstract to clarify the details of the classification.作者: NEG 時(shí)間: 2025-3-23 00:18 作者: Loathe 時(shí)間: 2025-3-23 01:24
,Deep Feature Learning for?Medical Acoustics,fication systems may improve performance, especially in the field of medical acoustics. However, the usage of such frameworks makes the needed amount of data even larger. Consequently, they are useful if the amount of data available for training is adequately large to assist the feature learning process.作者: Cabg318 時(shí)間: 2025-3-23 07:35 作者: Nerve-Block 時(shí)間: 2025-3-23 11:46
,JointFusionNet: Parallel Learning Human Structural Local and?Global Joint Features for?3D Human Pos-of-the-art models on both intra-scenario H36M and cross-scenario 3DPW datasets and lead to appreciable improvements in poses with more similar local features. Notably, it yields an overall improvement of 3.4?mm in MPJPE (relative 6.8. improvement) over the previous best feature fusion based method?[.] on H36M dataset in 3D human pose estimation.作者: CHANT 時(shí)間: 2025-3-23 15:02 作者: Grievance 時(shí)間: 2025-3-23 21:08 作者: venous-leak 時(shí)間: 2025-3-23 23:03 作者: Traumatic-Grief 時(shí)間: 2025-3-24 03:46 作者: Infelicity 時(shí)間: 2025-3-24 10:03
Conference proceedings 2022ed from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications..作者: semiskilled 時(shí)間: 2025-3-24 11:25 作者: 顛簸下上 時(shí)間: 2025-3-24 16:09
0302-9743 ral Networks, ICANN 2022, held in Bristol, UK, in September 2022.. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artific作者: jaunty 時(shí)間: 2025-3-24 20:40 作者: Vldl379 時(shí)間: 2025-3-25 00:07 作者: constitute 時(shí)間: 2025-3-25 04:25
https://doi.org/10.1007/978-3-540-48954-2 algorithm is presented to optimize the convex objective function. The results compared with the other classical methods on gas sensor array data sets demonstrate that the proposed method can effectively reduce the number of sensors with higher classification accuracy.作者: nutrition 時(shí)間: 2025-3-25 07:38
,Analysing the?Predictivity of?Features to?Characterise the?Search Space,or transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.作者: 尊重 時(shí)間: 2025-3-25 14:14 作者: Indict 時(shí)間: 2025-3-25 17:40
,Robust Sparse Learning Based Sensor Array Optimization for?Multi-feature Fusion Classification, algorithm is presented to optimize the convex objective function. The results compared with the other classical methods on gas sensor array data sets demonstrate that the proposed method can effectively reduce the number of sensors with higher classification accuracy.作者: Interstellar 時(shí)間: 2025-3-25 22:06 作者: Control-Group 時(shí)間: 2025-3-26 03:21
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162656.jpg作者: Dri727 時(shí)間: 2025-3-26 05:06
https://doi.org/10.1007/978-3-031-15937-4artificial intelligence; computer networks; computer science; computer systems; computer vision; database作者: 拘留 時(shí)間: 2025-3-26 09:38 作者: 牽索 時(shí)間: 2025-3-26 15:44
Schleifbarkeit unterschiedlicher Werkstoffe,edictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the mo作者: doxazosin 時(shí)間: 2025-3-26 17:40
Schleifbarkeit unterschiedlicher Werkstoffe,t approaches mainly concentrate on aggregating deep features from convolutional networks and introducing edge supervision for a guarantee of compact targets. Though significant progress has been accomplished, the problems of low-contrast by targets against backgrounds and the inconsistency of object作者: 肥料 時(shí)間: 2025-3-26 22:32
Schleifbarkeit unterschiedlicher Werkstoffe,getting problem in continual learning, researchers have put forward various solutions, which are simply summarized into three types: network structure-based methods, rehearsal-based methods and regularization-based methods. Inspired by pseudo-rehearsal and regularization methods, we propose a novel 作者: 兵團(tuán) 時(shí)間: 2025-3-27 04:53 作者: 火海 時(shí)間: 2025-3-27 06:33
Grundlagen zum Schneideneingriff, student. In general, the soft targets, the intermediate feature representation in hidden layers, or a couple of them from the teacher serve as the supervisory signal to educate the student. However, previous works aligned hidden layers one on one and cannot make full use of rich context knowledge. 作者: PUT 時(shí)間: 2025-3-27 11:15
Grundlagen zum Schneideneingriff,ethods mainly focus on the calibration of decoder features while ignore the recalibration of vital encoder features. Moreover, the fusion between encoder features and decoder features, and the transfer between boundary features and saliency features deserve further study. To address the above issues作者: Hirsutism 時(shí)間: 2025-3-27 14:06 作者: 善變 時(shí)間: 2025-3-27 19:52
Grundlagen zum Schneideneingriff,to detect the source of a fire before it spreads. The existing fire detection algorithms have a weak generalization and do not fully consider the influence of fire target size on detection. To enhance the ability of fire detection of different sizes, ground fire data and Unmanned Aerial Vehicle (UAV作者: Loathe 時(shí)間: 2025-3-27 22:05
Grundlagen zum Schneideneingriff,action bipartite graph is helpful for learning the collaborative signals between users and items. However, this modeling scheme ignores the influence of the objectively existing attribute information of item itself, and cannot well explain why users focus on items..A feature interaction-based graph 作者: 膠狀 時(shí)間: 2025-3-28 05:21 作者: 線 時(shí)間: 2025-3-28 06:36 作者: aspersion 時(shí)間: 2025-3-28 12:40
Elektrochemisches Abtragen (ECM),eatures at different scales, which suffers from the inconsistence of different high-level and low-level features due to the straightforward combination. In this paper, we propose a multi-scale vertical cross-layer feature aggregation and attention fusion network which not only has bottom-up and top-作者: 嬉耍 時(shí)間: 2025-3-28 18:08
https://doi.org/10.1007/978-3-540-48954-2of moiré and the dynamic nature of the moiré textures, it is difficult to effectively remove the moiré patterns. In this paper, we propose a multi-spectral dynamic feature encoding (MSDFE) network for image demoiréing. To solve the issue of moiré with distributed frequency spectrum, we design a mult作者: Initiative 時(shí)間: 2025-3-28 20:11
https://doi.org/10.1007/978-3-540-48954-2information about the dataset. While typical setups for feature importance ranking assess input features individually, in this study, we go one step further and rank the importance of groups of features, denoted as feature-blocks. A feature-block can contain features of a specific type or features d作者: 泛濫 時(shí)間: 2025-3-28 23:19 作者: Congestion 時(shí)間: 2025-3-29 05:04 作者: Apraxia 時(shí)間: 2025-3-29 07:32 作者: 含水層 時(shí)間: 2025-3-29 13:10
https://doi.org/10.1007/978-3-540-48954-2papers, they also increase the possibility of encountering inferior papers. However, it is difficult to predict the quality of a paper just from a glance at the paper. In this paper, we propose a machine learning approach to predicting the quality of scientific papers. Specifically, we predict the q作者: Omniscient 時(shí)間: 2025-3-29 19:01
Elektrochemisches Abtragen (ECM),it also largely increases parameters and calculations. In this paper, we propose the following problems. (1) How to build a lighter module that integrates CNN and Transformer? We propose the ML-block module in this paper. Especially, for one thing, reducing the number of channels after the convoluti作者: Explicate 時(shí)間: 2025-3-29 22:20
,Boosting Feature-Aware Network for?Salient Object Detection,d while highlighting the weak features. In addition, considering the different responses of channels to output, we present a weighted aggregation block (WAB) to strengthen the significant channel features and recalibrate channel-wise feature responses. Extensive experiments on five benchmark dataset作者: 浮雕 時(shí)間: 2025-3-30 00:02 作者: meditation 時(shí)間: 2025-3-30 06:58
Feature Fusion Distillation,detection, and semantic segmentation on individual benchmarks show FFD jointly assist the student in achieving encouraging performance. It is worth mentioning that when the teacher is ResNet34, the ultimately educated student ResNet18 achieves . top-1 accuracy on ImageNet-1K.作者: Fsh238 時(shí)間: 2025-3-30 10:52 作者: Magnificent 時(shí)間: 2025-3-30 16:14
,Feature Selection for?Trustworthy Regression Using Higher Moments,egression can be extended to take into account the complete distribution by making use of higher moments. We prove that the resulting method can be applied to preserve various certainty measures for regression tasks, including variance and confidence intervals, and we demonstrate this in example app作者: NEEDY 時(shí)間: 2025-3-30 17:25 作者: Commonwealth 時(shí)間: 2025-3-30 22:12
,Multi-scale Feature Extraction and?Fusion for?Online Knowledge Distillation,e and fuse the former processed feature maps via feature fusion to assist the training of student models. Extensive experiments on CIFAR-10, CIFAR-100, and CINIC-10 show that MFEF transfers more beneficial representational knowledge for distillation and outperforms alternative methods among various 作者: 模范 時(shí)間: 2025-3-31 02:13
,Ranking Feature-Block Importance in?Artificial Multiblock Neural Networks,gs, knock-in and knock-out strategies evaluate the block as a whole via a mutual information criterion. Our experiments consist of a simulation study validating all three approaches, followed by a case study on two distinct real-world datasets to compare the strategies. We conclude that each strateg作者: 鋸齒狀 時(shí)間: 2025-3-31 07:41
,Stimulates Potential for?Knowledge Distillation,eatures are transferred to the student to guide the student network learning. Extensive experimental results demonstrate that our SPKD has achieved significant classification results on the benchmark datasets CIFAR-10 and CIFAR-100.作者: Debate 時(shí)間: 2025-3-31 12:42
Artificial Neural Networks and Machine Learning – ICANN 202231st International C作者: 懸崖 時(shí)間: 2025-3-31 15:25 作者: 我不明白 時(shí)間: 2025-3-31 19:00
Schleifbarkeit unterschiedlicher Werkstoffe,tion process to extract the dark knowledge from the old task model to alleviate the catastrophic forgetting. We compare KRCL with the Finetune, LWF, IRCL and KRCL_real baseline methods on four benchmark datasets. The result shows that the KRCL model achieves state-of-the-art performance in standard 作者: 澄清 時(shí)間: 2025-3-31 22:19 作者: 值得 時(shí)間: 2025-4-1 03:44
Grundlagen zum Schneideneingriff,useful internal saliency information. MSFRC is designed to aggregate multi-source features and reduce parameter imbalance between saliency features and boundary features. Compared with previous methods, more layers are applied to generate boundary features, which sufficiently leverage the complement作者: detach 時(shí)間: 2025-4-1 09:32 作者: 起來(lái)了 時(shí)間: 2025-4-1 10:24
