標題: Titlebook: Representation Learning for Natural Language Processing; Zhiyuan Liu,Yankai Lin,Maosong Sun Book‘‘‘‘‘‘‘‘ 2023Latest edition The Editor(s) [打印本頁] 作者: Lipase 時間: 2025-3-21 18:35
書目名稱Representation Learning for Natural Language Processing影響因子(影響力)
書目名稱Representation Learning for Natural Language Processing影響因子(影響力)學科排名
書目名稱Representation Learning for Natural Language Processing網(wǎng)絡(luò)公開度
書目名稱Representation Learning for Natural Language Processing網(wǎng)絡(luò)公開度學科排名
書目名稱Representation Learning for Natural Language Processing被引頻次
書目名稱Representation Learning for Natural Language Processing被引頻次學科排名
書目名稱Representation Learning for Natural Language Processing年度引用
書目名稱Representation Learning for Natural Language Processing年度引用學科排名
書目名稱Representation Learning for Natural Language Processing讀者反饋
書目名稱Representation Learning for Natural Language Processing讀者反饋學科排名
作者: refraction 時間: 2025-3-21 21:21 作者: Abominate 時間: 2025-3-22 04:00 作者: 周年紀念日 時間: 2025-3-22 05:34
Ning Ding,Yankai Lin,Zhiyuan Liu,Maosong Sunnded domain. By applying the properties of Mittag–Leffler functions and the method of eigenfunction expansion, we establish some results about the existence and uniqueness of mild solutions of the proposed problem based on the compact technique. Due to the ill-posedness of backward problem in the se作者: 劇本 時間: 2025-3-22 09:04
Cheng Yang,Yankai Lin,Zhiyuan Liu,Maosong Sunon coordinate translation is constructed to convert input limitation into the form of saturation. With the help of this transform and the Euler-Lagrange mechanical equation, a dynamic model of STS deployment in elliptical orbits is established. To tackle tether tension limitation and thruster satura作者: ARCHE 時間: 2025-3-22 13:48
Yuan Yao,Zhiyuan Liu,Yankai Lin,Maosong Sunon coordinate translation is constructed to convert input limitation into the form of saturation. With the help of this transform and the Euler-Lagrange mechanical equation, a dynamic model of STS deployment in elliptical orbits is established. To tackle tether tension limitation and thruster satura作者: leniency 時間: 2025-3-22 18:54
Ganqu Cui,Zhiyuan Liu,Yankai Lin,Maosong Suntrategy to obtain an excellent contouring motion performance for multidimensional systems. Specifically, the simplified Newton-based CEE (SNE) is modified from Newton extremum seeking algorithm. Compared with existing CEE, SNE method keeps the high precision meanwhile requires less computing resourc作者: Infinitesimal 時間: 2025-3-22 23:29 作者: 猛烈責罵 時間: 2025-3-23 05:20
Yujia Qin,Zhiyuan Liu,Yankai Lin,Maosong Suncomposites and laminates. These advanced materials hold immense potential for applications requiring self-repair functionality. The chapter explores into the design and engineering of bio-based components embedded within the nanocomposites. These components mimic the sophisticated self-healing mecha作者: infringe 時間: 2025-3-23 07:05 作者: modish 時間: 2025-3-23 13:02
Word Representation Learning,s chapter, we introduce the approaches for word representation learning to show the paradigm shift from symbolic representation to distributed representation. We also describe the valuable efforts in making word representations more informative and interpretable. Finally, we present applications of 作者: 擺動 時間: 2025-3-23 16:44
Sentence and Document Representation Learning,lenging task because many important applications of natural language processing (NLP) lie in understanding sentences and documents. This chapter first introduces symbolic methods to sentence and document representation learning. Then we extensively introduce neural network-based methods for the far-作者: 結(jié)果 時間: 2025-3-23 18:47
Pre-trained Models for Representation Learning,ocuments in a self-supervised manner. Pre-trained models not only unify semantic representations of multiple tasks, multiple languages, and multiple modalities but also emerge high-level capabilities approaching human beings. In this chapter, we introduce pre-trained models for representation learni作者: 泛濫 時間: 2025-3-23 23:30
Graph Representation Learning,a sequence of word tokens, massive additional information in NLP is in the graph structure, such as syntactic relations between words in a sentence, hyperlink relations between documents, and semantic relations between entities. Hence, it is critical for NLP to encode these graph data with graph rep作者: AXIS 時間: 2025-3-24 04:52 作者: 有毒 時間: 2025-3-24 08:29 作者: SPER 時間: 2025-3-24 11:11 作者: GOUGE 時間: 2025-3-24 15:56 作者: 陳列 時間: 2025-3-24 22:04 作者: 模仿 時間: 2025-3-25 02:57 作者: 武器 時間: 2025-3-25 05:41 作者: BUST 時間: 2025-3-25 10:05
Ten Key Problems of Pre-trained Models: An Outlook of Representation Learning, models (i.e., big models) are the state of the art of representation learning for NLP and beyond. With the rapid growth of data scale and the development of computation devices, big models bring us to a new era of AI and NLP. Standing on the new giants of big models, there are many new challenges a作者: 我不死扛 時間: 2025-3-25 11:46
Sentence and Document Representation Learning,ument representation learning. Finally, we present representative applications of sentence and document representation, including text classification, sequence labeling, reading comprehension, question answering, information retrieval, and sequence-to-sequence generation.作者: ARCH 時間: 2025-3-25 19:44
Graph Representation Learning,ter, we introduce a variety of graph representation learning methods that embed graph data into vectors with shallow or deep neural models. After that, we introduce how graph representation learning helps NLP tasks.作者: 比喻好 時間: 2025-3-25 23:11
Knowledge Representation Learning and Knowledge-Guided NLP,knowledge, including knowledge representation learning, knowledge-guided NLP, and knowledge acquisition. For linguistic knowledge, commonsense knowledge, and domain knowledge, we will introduce them in detail in subsequent chapters considering their unique knowledge properties.作者: 神秘 時間: 2025-3-26 03:37 作者: uveitis 時間: 2025-3-26 06:39
Legal Knowledge Representation Learning,gal AI. In this chapter, we summarize the existing knowledge-intensive legal AI approaches regarding knowledge representation, acquisition, and application. Besides, future directions and ethical considerations are also discussed to promote the development of legal AI.作者: MUTE 時間: 2025-3-26 10:04 作者: Aids209 時間: 2025-3-26 14:10 作者: antidepressant 時間: 2025-3-26 17:55 作者: Coeval 時間: 2025-3-26 22:19 作者: 原來 時間: 2025-3-27 03:33 作者: 樹上結(jié)蜜糖 時間: 2025-3-27 06:57
Robust Representation Learning,ies different robustness needs and characterizes important robustness problems in NLP representation learning, including backdoor robustness, adversarial robustness, out-of-distribution robustness, and interpretability. We also discuss current solutions and future directions for each problem.作者: stress-response 時間: 2025-3-27 11:52
Biomedical Knowledge Representation Learning,cal research. In this chapter, with biomedical knowledge as the core, we launch a discussion on knowledge representation and acquisition as well as biomedical knowledge-guided NLP tasks and explain them in detail with practical scenarios. We also discuss current research progress and several future directions.作者: 松果 時間: 2025-3-27 15:49
Pre-trained Models for Representation Learning,ng, from pre-training tasks to adaptation approaches for specific tasks. After that, we discuss several advanced topics toward better pre-trained representations, including better model architecture, multilingual, multi-task, efficient representations, and chain-of-thought reasoning.作者: UTTER 時間: 2025-3-27 18:33
OpenBMB: Big Model Systems for Large-Scale Representation Learning, computation and expertise of big model applications. In this chapter, we will introduce the core toolkits in OpenBMB, including BMTrain for efficient training, OpenPrompt and OpenDelta for efficient tuning, BMCook for efficient compression, and BMInf for efficient inference.作者: wall-stress 時間: 2025-3-27 23:34 作者: 忘川河 時間: 2025-3-28 03:50 作者: 清醒 時間: 2025-3-28 07:00 作者: 鬼魂 時間: 2025-3-28 14:28 作者: 表否定 時間: 2025-3-28 17:15
Shengding Hu,Zhiyuan Liu,Yankai Lin,Maosong Sunnt error estimates between the regularized solution and the exact solution under two parameter choice rules. In Sect. 5.3, we consider the terminal value problem of determining the initial value, in a general class of time fractional wave equation with the Caputo derivative, from a given final value作者: 高調(diào) 時間: 2025-3-28 21:23 作者: 水土 時間: 2025-3-29 00:31 作者: 吸引人的花招 時間: 2025-3-29 06:05 作者: 炸壞 時間: 2025-3-29 08:26
Ganqu Cui,Zhiyuan Liu,Yankai Lin,Maosong Sunlity in LMI form is given out. Furthermore, the dynamics of the entire system are also analyzed in the paper. At last, to validate the effective of the proposed SNE-DFSMC strategy, groups of comparative experiments are implemented on a 2-DOF linear motor table, whose results validate the efficiency 作者: Accord 時間: 2025-3-29 12:08
Xu Han,Weize Chen,Zhiyuan Liu,Yankai Lin,Maosong Sunformance of the proposed methods. To the best of our knowledge, it is the first time in the literature that tether tension limitation and thruster saturation have been considered and handled for nonlinear models in elliptical orbits using a FO sliding mode controller simultaneously.作者: conformity 時間: 2025-3-29 19:35 作者: Migratory 時間: 2025-3-29 23:13 作者: EVICT 時間: 2025-3-30 03:26
Ning Ding,Weize Chen,Zhengyan Zhang,Shengding Hu,Ganqu Cui,Yuan Yao,Yujia Qin,Zheni Zeng,Xu Han,Zhiy
