標題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe [打印本頁] 作者: monster 時間: 2025-3-21 17:26
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2018影響因子(影響力)
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2018影響因子(影響力)學(xué)科排名
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2018網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2018被引頻次
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2018被引頻次學(xué)科排名
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2018讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2018讀者反饋學(xué)科排名
作者: Eeg332 時間: 2025-3-21 20:30 作者: LVAD360 時間: 2025-3-22 01:51 作者: GLUE 時間: 2025-3-22 05:37 作者: SPURN 時間: 2025-3-22 11:05 作者: 啤酒 時間: 2025-3-22 15:50 作者: nepotism 時間: 2025-3-22 20:45
A Convolutional Neural Network Approach for Modeling Semantic Trajectories and Predicting Future Locions. Current location prediction algorithms go beyond using plain location data and show that additional context information can lead to a higher performance. Moreover, it has been shown that using semantics and projecting GPS trajectories on so called semantic trajectories can further improve the 作者: 針葉 時間: 2025-3-22 23:39
Neural Networks for Multi-lingual Multi-label Document Classificational networks for this task with three different configurations. The first one uses static word2vec embeddings that are let as is, while the second one initializes it with word2vec and fine-tunes the embeddings while learning on the available data. The last method initializes embeddings randomly and t作者: 切割 時間: 2025-3-23 03:56 作者: Rotator-Cuff 時間: 2025-3-23 07:05 作者: nutrition 時間: 2025-3-23 09:51
DTI-RCNN: New Efficient Hybrid Neural Network Model to Predict Drug–Target Interactionshave been developed to discover new DTIs, whereas the prediction accuracy is not very satisfactory. Most existing computational methods are based on homogeneous networks or on integrating multiple data sources, without considering the feature associations between gene and drug data. In this paper, w作者: 甜食 時間: 2025-3-23 14:30 作者: 表否定 時間: 2025-3-23 19:13
Direct Training of Dynamic Observation Noise with UMarineNetervation noise, which is dynamic in our marine virtual sensor task. Typically, dynamic noise is not trained directly, but approximated through terms in the loss function. Unfortunately, this noise loss function needs to be scaled by a trade-off-parameter to achieve accurate uncertainties. In this pa作者: 圍巾 時間: 2025-3-24 01:49 作者: 恃強凌弱 時間: 2025-3-24 04:13
A Multi-level Attention Model for Text Matchinged models in machine translation, which the models can automatically search for parts of a sentence that are relevant to a target word, we propose a multi-level attention model with maximum matching matrix rank to simulate what human does when finding a good answer for a query question. Firstly, we 作者: 構(gòu)想 時間: 2025-3-24 06:35
Attention Enhanced Chinese Word Embeddingsof existing word representation methods, we improve CBOW in two aspects. Above all, the context vector in CBOW is obtained by simply averaging the representation of the surrounding words while our AWE model aligns the surrounding words with the central word by global attention mechanism and self att作者: custody 時間: 2025-3-24 11:36
Balancing Convolutional Neural Networks Pipeline in FPGAss. However, their processing power demand offers a challenge to their implementation in embedded real-time applications. To tackle this problem, we focused in this work on the FPGA acceleration of the convolutional layers, since they account for about 90% of the overall computational load. We implem作者: 相互影響 時間: 2025-3-24 15:45 作者: 愉快嗎 時間: 2025-3-24 19:57
https://doi.org/10.1007/978-3-030-01418-6artificial intelligence; classification; clustering; computational linguistics; computer networks; Human-作者: 小卒 時間: 2025-3-24 23:09
978-3-030-01417-9Springer Nature Switzerland AG 2018作者: 正論 時間: 2025-3-25 03:42
Artificial Neural Networks and Machine Learning – ICANN 2018978-3-030-01418-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 畢業(yè)典禮 時間: 2025-3-25 08:54 作者: Fecal-Impaction 時間: 2025-3-25 11:59 作者: 防水 時間: 2025-3-25 18:37
,Weitere Normen der Signalübertragung,correlation between feature vectors extracted from the off-the-shelf models. The feature extraction and the matching are two-stage learning process. This paper presents a novel two-stream convolutional neural network that integrates the feature extraction and the matching under an end-to-end manner.作者: foppish 時間: 2025-3-25 21:02
https://doi.org/10.1007/978-3-642-86694-4is trained based on the features defined implicitly by this kernel. This two-stage approach decouples data representation from learning, which is suboptimal. On the other hand, Convolutional Neural Networks (CNNs) have the capability to learn their own features directly from the raw data during trai作者: TOXIC 時間: 2025-3-26 01:08 作者: gregarious 時間: 2025-3-26 08:12
Sonderanwendungen des Fernsehens,volutional neural network. This is a method that learns the value function of Profit Sharing instead of the value function of Q Learning used in the Deep Q-Network. By changing to an error function based on the value function of Profit Sharing which can acquire probabilistic policy in a shorter time作者: 小平面 時間: 2025-3-26 09:50 作者: foliage 時間: 2025-3-26 13:28 作者: 攤位 時間: 2025-3-26 17:02
Lothar Krisch,Wolfgang Schneideral networks for this task with three different configurations. The first one uses static word2vec embeddings that are let as is, while the second one initializes it with word2vec and fine-tunes the embeddings while learning on the available data. The last method initializes embeddings randomly and t作者: DRAFT 時間: 2025-3-26 21:08
Aufzeichnung von Fernsehprogrammen,mage recognition field due to the discriminative power of Convolutional Neural Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble CNN (MRE-CNN) framework for facial expression recognition, which aims to enhance the learning power of CNN models by capturing both the global a作者: anniversary 時間: 2025-3-27 03:00 作者: 首創(chuàng)精神 時間: 2025-3-27 07:08
W. Burkhardtsmaier,W. Buschbeckhave been developed to discover new DTIs, whereas the prediction accuracy is not very satisfactory. Most existing computational methods are based on homogeneous networks or on integrating multiple data sources, without considering the feature associations between gene and drug data. In this paper, w作者: Cardiac-Output 時間: 2025-3-27 12:43
,Fernsehübertragungen auf Leitungen,n this paper, we propose a hierarchical Convolution Neural Network (Hier-CNN) for emotion cause detection. Our Hier-CNN model deals with the feature sparse problem through a clause-level encoder, and handles the less event-based information problem by a subtweet-level encoder. In the clause-level en作者: FEAT 時間: 2025-3-27 14:57
https://doi.org/10.1007/978-3-642-79349-3ervation noise, which is dynamic in our marine virtual sensor task. Typically, dynamic noise is not trained directly, but approximated through terms in the loss function. Unfortunately, this noise loss function needs to be scaled by a trade-off-parameter to achieve accurate uncertainties. In this pa作者: obnoxious 時間: 2025-3-27 17:47
https://doi.org/10.1007/978-3-642-79349-3hey have been shown to work successfully in supervised classification and regression tasks, as well as in training unsupervised autoencoders. This work has two contributions: First, we show that dropout and dropconnect on input units, previously proposed for deep multi-layer neural networks, can als作者: 無關(guān)緊要 時間: 2025-3-28 00:06
https://doi.org/10.1007/978-3-642-79349-3ed models in machine translation, which the models can automatically search for parts of a sentence that are relevant to a target word, we propose a multi-level attention model with maximum matching matrix rank to simulate what human does when finding a good answer for a query question. Firstly, we 作者: Sedative 時間: 2025-3-28 04:53 作者: encomiast 時間: 2025-3-28 08:44 作者: 行為 時間: 2025-3-28 12:47
Thorsten Quandt,Jürgen Wilke,Thilo Papege text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsuper作者: Creatinine-Test 時間: 2025-3-28 17:17 作者: grotto 時間: 2025-3-28 19:32 作者: radiograph 時間: 2025-3-29 01:00 作者: 開始沒有 時間: 2025-3-29 06:08
Conference proceedings 2018NN 2018, held in Rhodes, Greece, in October 2018...The papers presented in these volumes was carefully reviewed and selected from? total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic C作者: 天氣 時間: 2025-3-29 07:59 作者: 表示向下 時間: 2025-3-29 12:44
https://doi.org/10.1007/978-3-642-86694-4hborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets. Code and data are publicly available (.).作者: 有權(quán)威 時間: 2025-3-29 19:12
Sonderanwendungen des Fernsehens,ri 2600, and the proposed method was compared with the conventional Deep Q-Network. As a result, we confirmed that the proposed method can learn from the earlier stage than Deep Q-Network and can obtain higher score finally.作者: 干涉 時間: 2025-3-29 19:43 作者: 角斗士 時間: 2025-3-30 02:09 作者: 偉大 時間: 2025-3-30 06:51
https://doi.org/10.1007/978-3-642-79349-3ard deviation (RRSD) will calculate the matching coverage score for all query words. Experiments on both question-answer task and learning to rank task have achieved state-of-the-art results compared to traditional statistical methods and deep neural network methods.作者: hurricane 時間: 2025-3-30 10:57
Two-Stream Convolutional Neural Network for Multimodal Matchingork is trained using an extreme multiclass classification loss by viewing each multimodal data as a class. Then a finetuning step is performed by a ranking constraint. Experimental results on Flickr30k datasets demonstrate the effectiveness of the proposed network for multimodal matching.作者: 減至最低 時間: 2025-3-30 15:48
Kernel Graph Convolutional Neural Networkshborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets. Code and data are publicly available (.).作者: 一起平行 時間: 2025-3-30 18:10 作者: 諷刺 時間: 2025-3-31 00:08 作者: 鑲嵌細工 時間: 2025-3-31 01:59 作者: 自由職業(yè)者 時間: 2025-3-31 05:10
A Multi-level Attention Model for Text Matchingard deviation (RRSD) will calculate the matching coverage score for all query words. Experiments on both question-answer task and learning to rank task have achieved state-of-the-art results compared to traditional statistical methods and deep neural network methods.作者: champaign 時間: 2025-3-31 10:47 作者: Erythropoietin 時間: 2025-3-31 14:45
,Fernsehübertragungen auf Leitungen,coder, the representation of a word is augmented with its context. In the subtweet-level encoder, the event-based features are extracted in term of microblogs. Experimental results show that our model outperforms several strong baselines and achieves the state-of-the-art performance.作者: Afflict 時間: 2025-3-31 20:57 作者: 雀斑 時間: 2025-4-1 00:12 作者: Debrief 時間: 2025-4-1 02:45 作者: 大雨 時間: 2025-4-1 08:52
Offenheitsmerkmale von Fernsehserien,ate that the AWE models far exceed the CBOW model, and achieve state-of-the-art performances on the task of word similarity. Last but not least, we also further verify the AWE models through attention visualization and case analysis.作者: 甜瓜 時間: 2025-4-1 11:04 作者: 肥料 時間: 2025-4-1 17:34
Further Advantages of Data Augmentation on Convolutional Neural Networksur results unveil a largely ignored advantage of data augmentation: networks trained with just data augmentation more easily adapt to different architectures and amount of training data, as opposed to weight decay and dropout, which require specific fine-tuning of their hyperparameters.作者: PRE 時間: 2025-4-1 18:41 作者: Frequency 時間: 2025-4-2 02:18
