Everything about Generative model
Model for generating observable data in probability and statistics
Generative models are a class of computational models frequently used for classification. In machine learning, it typically models the joint distribution of inputs and outputs, such as P(X,Y), or it models how inputs are distributed within each class, such as P(X∣Y) together with a class prior P(Y). Because it describes a full data-generating process, a generative model can be used to draw new samples that resemble the observed data, a process often referred to as synthetic data generation. Generative models are used for density estimation, simulation, and learning with missing or partially labeled data. In classification, they can predict labels by combining P(X∣Y) and P(Y) and applying Bayes' rule. Generative models are often contrasted with discriminative models, which focus on predicting outputs from inputs directly. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Generative modelA 5-minute orientation
- Skim a recent paper: Towards a general-purpose foundation model for computational pathologyInfluential work since 2022
- Pick up a classic book: TrilbyThe most-reprinted book on the subject
- Read the landmark paper: Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular modelCited 11,922 times
- Go sideways: Discriminative modelA closely related topic
- Go sideways: Linear classifierA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model
A generative model for inorganic materials design
A Generative Model for Volume Rendering
An all-atom protein generative model
A generative model for music transcription
Insufficient Data Generative Model for Pipeline Network Leak Detection Using Generative Adversarial Networks
Generative AI model privacy: a survey
A Generative Model of Mathematics Learning
🆕 Influential since 2022
Towards a general-purpose foundation model for computational pathology
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
A foundation model for generalizable disease detection from retinal images
The imperative for regulatory oversight of large language models (or generative AI) in healthcare
The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers
📚 Books worth reading
TrilbyGeorge du Maurier · 1894
El túnelErnesto Sabato · 1948
Introductory Mathematical AnalysisErnest F. Haeussler, Richard S. Paul · 1973
アフターダーク村上春樹 · 2004
Daring to DreamNora Roberts · 1996
Time Series AnalysisGeorge E. P. Box, Gwilym M. Jenkins · 1970
Princess DaisyJudith Krantz · 1980
Tempest in EdenSandra Brown · 1983
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Discriminative model Discriminative models, also referred to as conditional models, are a class of models frequently used for classification. In machine learning, it typically…
- Linear classifier In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. A simpler definition…
- Supervised learning In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based…
- Naive Bayes classifier In statistics, naive (sometimes simple or idiot's) Bayes classifiers are a family of "probabilistic classifiers" which assume that the features are conditionall…
- Probabilistic classification In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over…
- Conditional random field In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction…
- Pattern recognition Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is…
- Hidden Markov model In probability theory, a hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred…