context of generative modeling

context of generative modeling

什么是生成建模（generative modeling）?

生成建模是指通过学习给定数据集的分布特征，以便能够生成与该数据集相似的新数据的一种方法。生成建模的目标是通过学习数据背后的模式和规律，建立一个能够产生新的、与原始数据相似的样本的模型。

为什么我们需要生成建模？

生成建模在许多领域中都具有重要的应用价值。首先，生成建模可以用于数据合成。在一些场景中，原始数据可能由于隐私、版权或其他因素无法公开，生成建模可以提供一种生成与原始数据相似的人工数据的方法，以便能够进行测试、研究等应用。其次，生成建模也可以用于数据增强。在一些机器学习任务中，数据的数量与多样性可以对模型的性能产生显著影响，生成建模可以通过生成新的数据样本来增加数据集的规模和多样性。最后，生成建模也可以用于数据缺失的填充。在一些情况下，原始数据可能存在缺失值，生成建模可以通过学习数据的分布特征，从而对缺失的数据进行估计和填充。

生成建模的方法有哪些？

生成建模涵盖了许多不同的方法，其中最常用的方法包括生成对抗网络

（GANs）、变分自编码器（VAEs）和生成式对抗网络（GANs）和自回归模型。

生成对抗网络（GANs）是一种由两个神经网络组成的框架，一个生成器和一个判别器。生成器试图生成与真实数据相似的数据样本，而判别器则试图区分生成器生成的样本和真实数据。通过对抗训练的方式，生成器逐渐学习到生成高质量的样本，而判别器则逐渐学习到更好地区分真实数据和生成数据的能力。

变分自编码器（VAEs）是一种生成模型，它试图学习给定数据集的潜在分布特征。VAE的思想是将数据编码到一个低维潜在空间中，并通过解码器将潜在空间的样本解码为与原始数据相似的样本。通过学习潜在空间的分布，VAE可以生成新的样本。

自回归模型是一类基于概率模型的生成模型，它可以建模数据样本中的依赖关系。常见的自回归模型包括条件随机场（CRF）和递归神经网络（RNN）。自回归模型通过逐步生成数据的每个特征来生成新的样本。

生成建模的评估和挑战？

生成建模的评估是一个相对复杂的问题。由于生成建模旨在模拟数据的分布特征，因此评估模型生成的样本质量是一个重要的任务。常用的评估指

标包括数据的多样性、真实度和一致性等。此外，对于某些特定的应用，还可以通过与真实数据的对比来评估模型的性能。

然而，生成建模也面临一些挑战。首先，模型的训练是一项困难的任务，需要解决梯度消失、模式崩溃等问题。此外，生成建模还需要处理数据的高维性和复杂性，使得模型的设计和训练过程更加困难。最后，生成建模的应用往往需要大量的计算资源和时间，使得模型的训练和推断过程变得昂贵和耗时。

结论

生成建模是一种重要的方法，可以用于数据合成、数据增强和数据缺失填充等任务。生成对抗网络、变分自编码器和自回归模型是生成建模的常用方法。然而，生成建模面临着模型训练的困难和计算资源的限制等挑战。未来，随着机器学习和深度学习技术的进一步发展，生成建模将在各个领域发挥更大的作用。

语言学

一、语言学术语，译成中文 1.Syntax句法学：is the study of the rules governing the ways different constituents are combimed to form sentences in a language, or the study of the interrelationships between elements in sentence structures. 2. Syntagmatic relations组合关系 3. paradigmatic relations聚合关系 4. there are totally six possible types of language, they are: SVO, VSO, SOV, OVS, OSV, VOS. English belongs to SVO type, though this does not mean that SVO is the only possible word order. 5. positional relations位置关系relations of substituability替代关系relations of co-occurrence同现关系 6. relations of co-occurrence partly belong to syntagmatic relations, partly to paradigmatic relations. 7. Immediate constituent analysis (IC analysis)直接成分分析法 8. subordinate constructions.从属关系coordinate constructions.并列关系 9. synonymy同义关系 The so-called synonymous are all context dependent. They all differ in: style: Little T om buy/purchase a toy bear. Connotations: I’m thrifty, T om are economical, and he is stingy. Dialectal difference: Autumn/fall Collective difference: look/watch Emotive difference: partnere 10. Antonymy Antonymy is the name for oppositeness relation. There are three subtypes: gradable, complementary and converse antonymy. 1.Gradable antonymy Gradable antonymy is the commonest type of antonymy. They are mainly adjectives, e.g. good / bad, long / short, big / small, etc. 2. Complementary antonymy The members of a pair in complementary antonymy are complementary to each other. That is, they divide up the whole of a semantic filed completely. Not only the assertion of one means the denial of the other, the denial of one also means the assertion of the other, e.g. alive / dead, hit / miss, male / female, boy / girl, etc. 3. Converse antonymy Converse antonyms are also called relational opposites. This is a special type of antonymy in that the members of a pair do not constitute a positive-negative opposition. They show the reversal of a relationship between two entities, e.g. buy / sell, parent / child, above / below, etc. 11.hyponymy上下意关系superordinate上坐标词hyponyms下坐标词 12. Sapir-Whorf hypothesis What the Sapir-Whorf hypothesis suggests is like this: our language helps mould our way of thinking and, consequently, different languages may probably express our unique ways of understanding the world. Following this argument, two important points could be captured in the

语言学家菲尔莫尔Fillmore与其场景框架语义学之简要介绍

?Charles J. Fillmore Charles J. Fillmore (born 1929) is an American linguist, and an Emeritus Professor of Linguistics at the University of California, Berkeley. He received his Ph.D. in Linguistics from the University of Michigan in 1961. Professor Fillmore spent ten years at The Ohio State University before joining Berkeley's Department of Linguistics in 1971. He has been a Fellow at the Center for Advanced Study in the Behavioral Sciences. Dr. Fillmore has been extremely influential in the areas of syntax and lexical semantics. He was a proponent of Noam Chomsky's theory of generative grammar during its earliest transformational grammar phase. In 1963, his seminal article The position of embedding transformations in a Grammar introduced the transformational cycle, which has been a foundational insight for theories of syntax since that time. He was one of the founders of cognitive linguistics, and developed the theories of Case Grammar (Fillmore 1968), and Frame Semantics (1976). In all of his research he has illuminated the fundamental importance of semantics, and its role in motivating syntactic and morphological phenomena. His earlier work, in collaboration with Paul Kay and George Lakoff, was generalized into the theory of Construction Grammar. He has had many students, including Laura Michaelis, Chris Johnson, Miriam R. L. Petruck, Len Talmy, and Eve Sweetser. His current major project is called FrameNet; it is a wide-ranging on-line description of the English lexicon. In this project, words are described in terms of the Frames they evoke. Data is gathered from the British National Corpus, annotated for semantic and syntactic relations, and stored in a database organized by both lexical items and Frames. The project is influential -- Issue 16 of the International Journal of Lexicography was devoted entirely to it. It has also inspired parallel projects, which investigate other languages, including Spanish, German, and Japanese. Frame semantics Frame semantics is a theory of linguistic meaning that extends Charles J. Fillmore's case grammar. It relates linguistic semantics to encyclopaedic knowledge. The basic idea is that one cannot understand the meaning of a single word without access to all the essential knowledge that relates to that word. For example, one would not be able to understand the word "sell" without knowing anything about the situation of commercial transfer, which also involves, among other things, a seller, a buyer, goods, money, the relation between the money and the goods, the relations between the seller and the goods and the money, the relation between the buyer and the goods and the money and so on. Thus, a word activates, or evokes, a frame of semantic knowledge relating to the specific concept it refers to (or highlights, in frame semantic terminology). A semantic frame is a collection of facts that specify "characteristic features, attributes, and functions of a denotatum, and its characteristic interactions with things necessarily or typically associated with it" (Keith Alan, Natural Language Semantics). A semantic frame can also be defined as a coherent structure of related concepts that are related such that without knowledge of all of them, one does not have complete knowledge of any one; they are in that sense types of gestalt. Frames are based on recurring experiences. So the commercial transaction frame is based on recurring experiences of commercial transactions. Words not only highlight individual concepts, but also specify a certain perspective from which the frame is viewed. For example "sell" views the situation from the perspective of the seller and "buy" from the perspective of the buyer. This, according to Fillmore, explains the observed asymmetries in many lexical relations. While originally only being applied to lexemes, frame semantics has now been expanded to grammatical constructions and other larger and more complex linguistic units and has more or less been integrated into construction grammar as the main semantic principle. Semantic frames are also becoming used in information modeling, for example in Gellish, especially in the form of 'definition models' and 'knowledge models'.

Context

Context 1.g eneral introduction 1)Malinowski (1923) first proposed two terms: context of situation (情境语境) and context of culture (文化语境). Context of situation == linguistic context; context of culture == non-linguistic context. [language is a mode of action (行为方式), not a countersign of thought (不是思想的符号)] 2)Firth: social environment of language (语 言的社会环境), namely, the relationship between language and social environment. [ context is like grammar which can serves as the criteria of measurements (语法与衡量的标准). It is also the inner mental state (内部心理状态). Thus, language has two meanings : contextual meaning (情境意义) and internal meaning (内部意义)] Malinowski and Firth: meaning = usage (意义等于用法)

3)Halliday (1973), John Lyons (1977) and Searle (1979): social context (社会语境) which includes historic-social context, pragmatic-social context. 4)Saeed: contextual information (情境信息). The following can be regarded as context: knowledge, discourse and background knowledge. 5)Hymes: context = SPEAKING S == setting, scene P == participants E == ends (purpose and results) A == act sequence K == key (style) I == instrumentalities (media) N == norms (rules or disciplines) G == genres (scope) 6) 王昌龄在《诗格》中提到“物镜”、“情境”、“意境”。

语言学-chomsky

T eamwork---Linguistic Course By:Business English Class 1---Group 4 Task:Survey on Noam Chomsky Leader&Editor: Material Offering: Proofreader: （注：按照姓氏拼音首字母a b c……顺序排列）

Noam Chomsky Brief Introduction: ●中文名：诺姆·乔姆斯基 ●外文名：Noam Chomsky ●国籍：美国 ●出生地：美国宾夕法尼亚州的费 城 ●出生日期：1928年12月7日 ●职业：学术人物语言学家 ●毕业院校：宾夕法尼亚大学 ●主要成就：语言学家，转换-生 成语法创始人 ●代表作品：《现代希伯莱语语素 音位学》《转换分析》《句法结构》 Chomsky’s Revolution in Linguistic “转换生成语法”（Transformashionl Generative 自20世纪50年代中期开始， Grammar，简称TG）取代描写语言学，成为美国语言学的主流。转换生成理论很快发展成为现代欧美语言学中最有影响的一种理论。① 乔姆斯基是一位富有探索精神的语言学家。他的父亲是希伯来语学者。受其影响，乔姆斯基最初把兴趣点放在研究希伯来语上。他用结构主义的方法研究希伯莱语，后来发现这种方法有很大的局限性，转而探索新的方法，逐步建立起转换－生成语法，1957年出版的《句法结构》就是这一新方法的标志。可这种用离散数学方法研究句法结构的方法论早在1930年便由鲁道夫·卡尔纳(Rudolph Car nap)提出。转换规则由乔姆斯基的老师Zellig Harris提出。而形态音位规则（Morphophonemic’s rule）则与布费尔德学派如出一辙。乔姆斯基在《句法结构》一书中只是将这三种早已存在的研究方法进行了综合。这种分析方法风靡全世界，冲垮了结构语言学的支配地位，因而被人们称为"乔姆斯基革命"。 后来他又不断丰富和发展转换－生成语法的理论和方法，相继发表了《句法理论要略》、《深层结构、表层结构和语义解释》、《支配和约束论集》等重要著作，对世界语言学的发展方向产生了巨大的影响。现在，转换-生成语法仍在继续发展之中。 ①徐志民：《欧美语言学简史》2005年8月第一版，第269页。

语言学专业词汇中英文对照版

语言学术语（英-汉对照）表appropriateness适宜性得体性broadening词义扩大Aapproximant无摩擦延续音Browncorpus布朗语料库 abbreviation缩写词，略语aptitudetest素质测试C ablative夺格，离格Arabic阿拉伯语calculability可计算性 accent重音（符）arbitrariness任意性calque仿造仿造词语 accusative宾格argument中项中词主目cancellability可删除 achievementtest成绩测试article冠词cardinalnumeral基数 acousticphonetics声学语音学articulation发音cardinalvowel基本元音 acquisition习得articulator发音器官case格 acronym缩略语articulatoryphonetics发音语音学casegrammar格语法 actionprocess动作过程artificialspeech人工言语casetheory格理论 actor动作者aspect体category范畴 addressform称呼形式aspirated吐气送气categoricalcomponent范畴成分 addressee受话人assimilation同化causative使役的使投动词 addresser发话人associative联想center中心词 adjective形容词associativemeaning联想意义centraldeterminer中心限定词 adjunct修饰成分附加语assonance准压韵半谐音chainrelation链状关系 adverb副词attributive属性修饰语定语chainsystem链状系统 affix词缀auditoryphonetics听觉语音学choice选择 affixation词缀附加法authenticinput真实投入choicesystem选择系统 affricate塞擦音authorialstyle权威风格circumstance环境因子 agreement一致关系authoringprogram编程class词类 airstream气流autonomy自主性classshift词性变换 alliteration头韵auxiliary助词clause小句从句 allomorph词/语素变体auxiliaryverb助动词click吸气音咂音 allophone音位变体Bclipping截断法 allophonicvariation音位变体babblingstage婴儿语阶段closedclass封闭类 allophony音位变体现象back-formation逆构词法closedsyllable闭音节 alveolarridge齿龈basecomponent基础部分cluster音丛 alveolar齿龈音behaviouralprocess行为过程coarticulation协同发音 ambiguity歧义behaviourism行为主义coda结尾音节符尾 analogicalcreation类推造字bilabial双唇音code语码信码 anapest抑抑扬格bilabialnasal双唇鼻音cognitivepsychology认知心理学 anaphor前指替代bilateralopposition双边对立cognitivesystem认知系统anaphoricreference前指照应bilingualism双语现象coherence相关关联 animate有生命的binarydivision二分法cohension衔接 annotation注解binaryfeature二分特征co-hyponym同下义词 antecedent先行词前在词binarytaxonomy二分分类学colligation类连结anthropologicallinguistics人类语言binding制约collocativemeaning搭配意义 学bindingtheory制约论colorword色彩词 anticipatorycoarticulation逆化协同blade舌叶舌面前部colorwordsystem色彩词系统 发音blankverse无韵诗command指令 antonomasia换称代类名blending混成法commoncore共核 antonym反义词borrowing借用借词commonnoun普通名词 antonymy反义(关系)boundmorpheme粘着语素communication交际 appellative称谓性boundingtheory管辖论communicativecompetence交际能appliedlinguistics应用语言学bracketing括号法 力appliedsociolinguistics应用社会语brevitymaxim简洁准则communicativedynamism,CD交际言学bridging架接

大学语言学考试1-7章 试题和答案

12maximal onset principle states that when there is a choice as to where to place a consonant. it is put into the onset rather than the coda. E.g. The correct syllabification of the word country should be //. It shouldn?t be // or // according to this principle. 第一章，填空 1. The study of the meaning of lingustic words, phrases is called semantics. 2. Displacement is a design feature of human language that enables speakers to talk about a wild range of things free from barriers caused by 4. Morpheme is the smallest meaningful unit of language. 5. If a linguistic study describes and analyzes the language people actually use, it is said to be descriptive. 6. Chomsky defines“competence” as the ideal user's knowledge of the rules of his language. 7. Language is a means of verbal communication. It is informative in that communicating by speaking or writing is a purposeful act. 8. The link between a linguistic sign and its meaning is a matter of ??? 9. Language is distinguished from traffic lights in that the former has the designing feature of duality. 10. In linguistics research, both quantity and quality approaches are preferred. 判断： 11. The writing system of a language is always a later invention used to record speech,

generative agent-based models

generative agent-based models 英文版 Generative Agent-Based Models In the realm of artificial intelligence and computational modeling, generative agent-based models have emerged as a powerful tool for simulating complex systems. These models are designed to replicate real-world scenarios by creating virtual agents that interact with each other and their environment, generating data that reflects the underlying dynamics of the system. The fundamental principle of agent-based modeling lies in its recognition that systems are composed of multiple interacting entities, known as agents. These agents can range from individuals in a social network to cells in a biological system. Each agent is programmed with a set of rules and behaviors that govern its interactions with other agents and the environment. As the agents interact, they generate a wealth of

diffusion模型解读

diffusion模型解读 ## Diffusion Models Explained. Diffusion models are a type of generative model that has gained popularity in recent years for their ability to generate high-quality images, text, and other data from scratch. They work by gradually "denoising" a random input, adding details and structure until a coherent output is produced. Diffusion models are based on the idea of diffusion, a process that spreads out a substance over time. In the context of generative modeling, diffusion is used to spread out the noise in a random input, making it gradually more uniform. By reversing the diffusion process, the model can then learn to generate data that is increasingly structured and detailed. The training process for a diffusion model involves two main steps:

latent diffusion models讲解

Latent Diffusion Models Introduction Latent diffusion models are a class of probabilistic models used in machine learning and natural language processing (NLP). These models are particularly useful for tasks such as image generation, language modeling, and representation learning. In this article, we will provide a comprehensive overview of latent diffusion models, explaining their concept, applications, and training techniques. What are Latent Diffusion Models? Latent diffusion models are generative models that learn the underlying probability distribution of a set of data points. They aim to model the data points as a series of transformations from a simple initial distribution to the target distribution. These transformations are controlled by a series of diffusion steps, each step introducing a certain amount of noise into the data. The main idea behind latent diffusion models is to iteratively apply these diffusion steps and learn the parameters that govern the transformation process. Applications of Latent Diffusion Models Latent diffusion models have found applications in various fields, including: 1.Image Generation: Latent diffusion models can learn the distribution of images and generate new samples by transforming noise vectors. By iteratively applying diffusion steps, these models can produce visually appealing and realistic images. https://www.wendangku.net/doc/f019272590.html,nguage Modeling: Latent diffusion models can also be used to model the distribution of text data. By learning the underlying structure of the text, these models can generate coherent and contextually relevant sentences.

transformers for image recognition at scale代码讲解

Transformers for Image Recognition at Scale Introduction In recent years, deep learning models have achieved remarkable performance in image recognition tasks. However, as the scale and complexity of image datasets continue to increase, traditional convolutional neural networks (CNNs) often struggle to extract meaningful and accurate features. This has led to the development and adoption of transformer-based models in the field of image recognition. In this article, we will delve into the use of transformers for image recognition at scale, exploring their architecture, advantages, and applications. Understanding Transformers Transformers were initially introduced for natural language processing tasks but have shown great potential in various domains since then. Unlike CNNs, which rely on convolutional and pooling operations to extract spatial features from images, transformers leverage a self-attention mechanism to capture global dependencies within the image. Key Components of Transformers 1.Encoder: The encoder component processes the input image and extracts useful features. It comprises multiple layers of self- attention and feed-forward neural networks, allowing the model to capture both local and global image information. 2.Self-Attention: Self-attention is the core mechanism of transformers. It allows each pixel or patch in the image to attend to all other pixels or patches, enabling the model to focus on relevant parts of the image for feature extraction. This mechanism facilitates long-range dependencies and helps capture contextual relationships. 3.Positional Encoding: To incorporate spatial information, positional encodings are added to the input image. These encodings

gpt术语

gpt术语 GPT术语解析 GPT（Generative Pre-trained Transformer）是一种基于Transformer架构的预训练语言模型，由OpenAI开发。它在自然语言处理领域具有广泛的应用。下面将对GPT术语进行解析。 1. Transformer：Transformer是一种基于注意力机制的神经网络架构，由Vaswani等人于2017年提出。它在机器翻译任务上取得了显著的成果，被广泛应用于自然语言处理领域。 2. 预训练（Pre-training）：预训练是指在大规模无标签数据上进行模型训练，以学习通用的语言表示。GPT模型通过大规模的无监督数据预训练，使得模型具有丰富的语言知识。 3. 微调（Fine-tuning）：微调是指在特定任务的有标签数据上对预训练模型进行进一步训练，以适应具体任务的要求。GPT模型可以通过在特定任务数据上微调，得到在该任务上的高性能表现。 4. 生成（Generation）：生成是指根据给定的输入，通过模型生成相应的输出。GPT模型可以生成连贯、合理的文本，被广泛应用于文章创作、对话生成等任务。 5. 语言建模（Language Modeling）：语言建模是指通过学习语言的统计规律和概率分布，对给定的序列进行概率估计。GPT模型可以

用于语言建模任务，通过预测下一个词的概率，生成连贯的文本。 6. 无监督学习（Unsupervised Learning）：无监督学习是指在没有标签数据的情况下，通过学习数据的内在结构和特征，进行模型训练和表示学习。GPT模型采用无监督学习方法进行预训练，可以学习到丰富的语言表示。 7. 注意力机制（Attention Mechanism）：注意力机制是一种机制，用于在序列数据中对不同位置的信息进行加权聚合。Transformer 模型中的注意力机制可以捕捉输入序列中不同位置的依赖关系，从而提高模型的表示能力。 8. 上下文（Context）：上下文是指在进行语言理解或生成任务时，考虑到的相关信息。GPT模型通过对输入序列的上下文进行建模，能够生成与上下文一致的输出。 9. 词嵌入（Word Embedding）：词嵌入是指将离散的词语映射到连续的向量空间表示。GPT模型使用词嵌入技术将词语转换为连续的向量表示，以便于神经网络进行处理。 10. 上下文无关（Context-agnostic）：上下文无关是指模型的输出只与输入的词语本身有关，与其上下文无关。GPT模型具有上下文相关的特性，可以根据输入序列的上下文生成相应的输出。 11. 负采样（Negative Sampling）：负采样是一种用于优化模型训