標(biāo)題: Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat [打印本頁] 作者: 加冕 時(shí)間: 2025-3-21 18:15
書目名稱Neural Information Processing影響因子(影響力)
書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名
書目名稱Neural Information Processing網(wǎng)絡(luò)公開度
書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Neural Information Processing被引頻次
書目名稱Neural Information Processing被引頻次學(xué)科排名
書目名稱Neural Information Processing年度引用
書目名稱Neural Information Processing年度引用學(xué)科排名
書目名稱Neural Information Processing讀者反饋
書目名稱Neural Information Processing讀者反饋學(xué)科排名
作者: 凹槽 時(shí)間: 2025-3-21 22:04 作者: 虛度 時(shí)間: 2025-3-22 03:04
Hybridized Character-Word Embedding for Korean Traditional Document Translationding to supplement loss from unknown words with character embedding. Experimental results show that the proposed method is efficient to translate old Korean archives (Hanja) to modern Korean documents (Hangul).作者: 話 時(shí)間: 2025-3-22 08:20 作者: Antioxidant 時(shí)間: 2025-3-22 11:32 作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-22 16:12
Conference proceedings 2018018, held in Siem Reap, Cambodia, in December 2018..The 401?full papers presented were carefully?reviewed and selected from 575 submissions. The papers?address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across differen作者: 隱語 時(shí)間: 2025-3-22 17:52
Topic-Bigram Enhanced Word Embedding Modelding topic-bigram information. The topic relevance weights are updated with word embeddings simultaneously during the training process. To verify the validity and accuracy of the model, we conduct experiments on word analogy task and word similarity task. The results show that the TBWE model can achieve the better performance in both two tasks.作者: magenta 時(shí)間: 2025-3-23 00:22
Conference proceedings 2018s?address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains.?The third volume, LNCS 11303, is organized in topical sections on embedded learning, transfer learning, reinforcement learning, and other learning approaches..作者: conjunctiva 時(shí)間: 2025-3-23 01:38 作者: 挫敗 時(shí)間: 2025-3-23 07:28 作者: Bricklayer 時(shí)間: 2025-3-23 12:15
A Sentence Similarity Model Based on Word Embeddings and Dependency Syntax-Treeop words and perform morphological restoration. Then, some important operations will be performed, such as passive flipping, negative flipping, and so on. Finally, the similarity of two sentence pairs is calculated by weighting the block embeddings of the syntactic tree. Experiments show the effectiveness of this method.作者: 期滿 時(shí)間: 2025-3-23 14:02
Semi-coupled Transform Learningtechnique has been applied in two problems. The first being image super-resolution and the second, cross lingual document retrieval. In both the cases, our proposed transform learning based formulation excels considerably over existing techniques.作者: Delirium 時(shí)間: 2025-3-23 19:19 作者: 泥沼 時(shí)間: 2025-3-24 01:48
Named Entity Disambiguation via Probabilistic Graphical Model with Embedding FeaturesRank algorithm are implemented for model parameters learning and inference. We evaluate . on existing dataset against several state-of-the-art NED systems, which validates the effectiveness of our proposed method.作者: 笨拙的你 時(shí)間: 2025-3-24 05:14 作者: ALLAY 時(shí)間: 2025-3-24 09:27 作者: ticlopidine 時(shí)間: 2025-3-24 11:17
Delving into Diversity in Substitute Ensembles and Transferability of Adversarial Exampleserability of crafted adversarial examples. Experimental results show that proposed ensemble adversarial attack strategies can successfully attack the DL system with ensemble adversarial training defense mechanism and the greater the diversity in substitute ensembles enables stronger transferability.作者: vitreous-humor 時(shí)間: 2025-3-24 18:27
0302-9743 ,?ICONIP 2018, held in Siem Reap, Cambodia, in December 2018..The 401?full papers presented were carefully?reviewed and selected from 575 submissions. The papers?address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques acros作者: 使人煩燥 時(shí)間: 2025-3-24 21:39
fMRI Semantic Category Decoding Using Linguistic Encoding of Word Embeddingse spatial patterns of neural activation in the brain are correlated with thinking about different semantic categories of words (for example, tools, animals, and buildings) or when viewing the related pictures. In this paper, we present a computational model that learns to predict the neural activati作者: inhibit 時(shí)間: 2025-3-25 00:24
Named Entity Disambiguation via Probabilistic Graphical Model with Embedding Features Wikipedia. State-of-the-art NED solutions harness neural networks to generate abstract representations, i.e., embeddings, of mentions and entities, based on which the disambiguation process can be achieved by finding entity with the most similar representation to mention. Nevertheless, the coherenc作者: 改良 時(shí)間: 2025-3-25 06:05 作者: 燦爛 時(shí)間: 2025-3-25 10:46
Unsupervised Ensemble Learning Based on Graph Embedding for Image Clusterings well on the large-scale data, has been proposed for manifold learning. To improve the clustering performance, a novel Unsupervised Ensemble Learning based on Graph Embedding (UEL-GE) is explored, which takes ULGE to get low-dimensional embeddings of the given data and uses the .-means method to ob作者: irreducible 時(shí)間: 2025-3-25 13:53 作者: 蓋他為秘密 時(shí)間: 2025-3-25 16:45
Event Causality Identification by Modeling Events and Relation Embeddingion. Traditional approaches of causality relation identification rely on the recognition of casual relationship connectives or manual features of causality relationships, and these methods have disadvantage of low recognition coverage and being lack of adaptive. To solve this problem, we propose a n作者: lanugo 時(shí)間: 2025-3-25 20:34 作者: 似少年 時(shí)間: 2025-3-26 01:32
Hybridized Character-Word Embedding for Korean Traditional Document Translationatical patterns. In recent times, a neural network-based machine translation architecture such as sequence-to-sequence (seq2seq) model showed superior performance in translation. However, it suffers out-of-vocabulary (OOV) issue when dealing with very complex and vocabulary languages such as Chinese作者: 擁護(hù)者 時(shí)間: 2025-3-26 04:22
Word Embedding Based on Low-Rank Doubly Stochastic Matrix Decompositionachine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning. In this paper we propose a novel neighbor embedding method which directly learns an embedding simplex where the similarities between the mapped words are opt作者: 個(gè)阿姨勾引你 時(shí)間: 2025-3-26 09:25
Meta-path Based Heterogeneous Graph Embedding for Music Recommendationtion techniques which are based on conventional collaborative filtering or acoustic content features usually sufffer from data sparsity or time-consuming computation problems, respectively. In fact, online music services not only generate listening history for each user but also accumulate a large a作者: Intruder 時(shí)間: 2025-3-26 14:20
Knowledge Graph Embedding via Entities’ Type Mapping Matrixowever, KG remains incomplete, inconsistent, and not completely accurate. To deal with the challenges of KGs, many state-of-the-art models, such as TransE, TransH, and TransR, have been proposed. TransE and TransH use one semantic space for entities and relations, whereas TransR uses two different s作者: 分發(fā) 時(shí)間: 2025-3-26 18:49 作者: 主動(dòng) 時(shí)間: 2025-3-27 00:48 作者: 編輯才信任 時(shí)間: 2025-3-27 02:33 作者: calorie 時(shí)間: 2025-3-27 07:17
Convolutional Transform Learningearned that analyses the image to generate the representation from the image. Here, we learn a set of independent convolutional filters that operate on the images to produce representations (one corresponding to each filter). The major advantage of our proposed approach is that it is completely unsu作者: 緯線 時(shí)間: 2025-3-27 10:03 作者: 雄偉 時(shí)間: 2025-3-27 17:29
Transfer Learning Using Progressive Neural Networks and NMT for Classification Tasks in NLP of the drawbacks of these techniques is that they need large amounts of training data. Even though there is a lot of data being generated everyday, not all tasks have large amounts of data. One possible solution when data is not sufficient is using transfer learning techniques. In this paper, we ex作者: Constitution 時(shí)間: 2025-3-27 21:20 作者: 倫理學(xué) 時(shí)間: 2025-3-27 23:23
t multiple timing constraints with complex inter-dependencies. To facilitate temporal testing, the paper systematically analyzes the characteristics of timing constraints and their correlation patterns, using a modeling technique called Extended Semantic Interface Automata (ESIA). A Correlation-Base作者: 雄偉 時(shí)間: 2025-3-28 02:51
Subba Reddy Oota,Naresh Manwani,Raju S. Bapit multiple timing constraints with complex inter-dependencies. To facilitate temporal testing, the paper systematically analyzes the characteristics of timing constraints and their correlation patterns, using a modeling technique called Extended Semantic Interface Automata (ESIA). A Correlation-Base作者: Conflict 時(shí)間: 2025-3-28 06:44
Weixin Zeng,Jiuyang Tang,Xiang Zhao,Bin Ge,Weidong Xiaoistribution of Docker images have received much research effort. There are also some efforts which use Peer-to-Peer file downloading method to speed-up Docker image distribution. However, these systems just package image as a file for sharing without considering the layering structure of the Docker 作者: 斥責(zé) 時(shí)間: 2025-3-28 12:36 作者: 冷淡一切 時(shí)間: 2025-3-28 14:58 作者: 租約 時(shí)間: 2025-3-28 21:15
Shengyue Luo,Wei Fangallows to charge users for the resource consumption of their deployed components, while resource control can limit the resource consumption of components in order to prevent denial-of-service attacks. In the approach presented here program transformations enable resource management in Java-based env作者: CERE 時(shí)間: 2025-3-29 01:07 作者: Lobotomy 時(shí)間: 2025-3-29 05:54 作者: 混雜人 時(shí)間: 2025-3-29 09:27 作者: 冷淡周邊 時(shí)間: 2025-3-29 13:18 作者: 暫時(shí)別動(dòng) 時(shí)間: 2025-3-29 18:15
Qianqi Fang,Ling Liu,Junliang Yu,Junhao Wenrs used to create software and covers setting up a Docker environment. Next, you will learn about repositories and version control along with its uses. Now that you are ready to program, you’ll go through the basics of Python, the ideal language to learn as a novice software engineer. Many modern ap作者: 沙發(fā) 時(shí)間: 2025-3-29 23:01 作者: arthroscopy 時(shí)間: 2025-3-30 02:48
Wenfeng Liu,Peiyu Liu,Jing Yi,Yuzhen Yang,Weitong Liu,Nana Lint and incorporating new knowledge into their behavior. These highly dynamic systems are also known as ensembles. To ensure correct behavior of ensembles it is necessary to support their development through appropriate methods and tools which can guarantee that an autonomic system lives up to its in作者: cinder 時(shí)間: 2025-3-30 07:02 作者: 尋找 時(shí)間: 2025-3-30 10:35
Jyoti Maggu,Angshul Majumdarneed to adapt. They also have strong requirements in terms of performances, resource usage, reliability, or security. To face this inherent complexity it is crucial to develop adequate tools and underlying models to analyze these properties at design time. Proposed models must be able to capture ess作者: 刺耳的聲音 時(shí)間: 2025-3-30 15:47 作者: Baffle 時(shí)間: 2025-3-30 19:36 作者: Extricate 時(shí)間: 2025-3-31 00:35
Jie Hang,KeJi Han,Yun Liware abstraction, hardware and software integration, multiple levels of abstraction and control, the development of a programming tool for a group of users which may become wide and diverse, etc. In this document we will introduce CoolBOT, a component oriented programming framework implementing prim作者: Narcissist 時(shí)間: 2025-3-31 03:49 作者: Contend 時(shí)間: 2025-3-31 06:29 作者: florid 時(shí)間: 2025-3-31 12:34
Neural Information Processing978-3-030-04182-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
