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Data diffusion machine (DDM) is a historical virtual shared memory architecture where data is free to migrate through the machine. Shared memory machines
Data_diffusion_machine
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
Image-generating machine learning model
donation from Stability and training data from non-profit organizations. Stable Diffusion is a latent diffusion model, a kind of deep generative artificial
Stable_Diffusion
Topics referred to by the same term
software to control Dante-enabled Devices, see Dante (networking) Data Diffusion Machine, a virtual shared memory computer architecture from the 1990s Digital
DDM
Subset of artificial intelligence
methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised
Machine_learning
Research field that lies at the intersection of machine learning and computer security
fabricated data that violates the statistical assumption. Most common attacks in adversarial machine learning include evasion attacks, data poisoning attacks
Adversarial_machine_learning
Geometric algorithm
of a data set into Euclidean space (often low-dimensional) whose coordinates can be computed from the eigenvectors and eigenvalues of a diffusion operator
Diffusion_map
Theory on how and why new ideas spread
Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The theory was popularized by Everett
Diffusion_of_innovations
Transport of dissolved species from the highest to the lowest concentration region
concept of diffusion is widely used in many fields, including physics (particle diffusion), chemistry, biology, sociology, economics, statistics, data science
Diffusion
Diffusion model over latent embedding space
The latent diffusion model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced
Latent_diffusion_model
Method of utilizing water in magnetic resonance imaging
the resulting data that uses the diffusion of water molecules to generate contrast in MR images. It allows the mapping of the diffusion process of molecules
Diffusion-weighted magnetic resonance imaging
Diffusion-weighted_magnetic_resonance_imaging
Tasks in machine learning
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Process of analyzing large data sets
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics
Data_mining
Data analysis technique
copies of existing data. Synthetic Minority Over-sampling Technique (SMOTE) is a method used to address imbalanced datasets in machine learning. In such
Data_augmentation
Overview of and topical guide to machine learning
penalties. Applications of machine learning Bioinformatics Biomedical informatics Computer vision Customer relationship management Data mining Earth sciences
Outline_of_machine_learning
Machine learning model
human-drawn art. Text-to-image models are generally latent diffusion models, which perform the diffusion process in a compressed latent space rather than directly
Text-to-image_model
Flaw in mathematical modelling
output when fed "validation data" that was not encountered during its training. The term overfitting has been used in machine learning at least since 1980's
Overfitting
Type of large language model
transformer-based models are used for text-to-image technologies such as diffusion and parallel decoding. Such kinds of models can serve as visual foundation
Generative pre-trained transformer
Generative_pre-trained_transformer
Set of methods for supervised statistical learning
vectors, developed in the support vector machines algorithm, to categorize unlabelled data.[citation needed] These data sets require unsupervised learning approaches
Support_vector_machine
Concept in machine learning
In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that
Leakage_(machine_learning)
Machine learning technique
November 2023). "DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models". NeurIPS 2023. arXiv:2305.16381. Retrieved 1 March 2024. Xu, Jiazheng;
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Machine learning technique
In machine learning, attention is a method that determines the importance of, or "attends to", each component in a sequence relative to the other components
Attention_(machine_learning)
Chemical data page for water
This page provides supplementary data to the article properties of water. Further comprehensive authoritative data can be found at the NIST Chemistry
Water_(data_page)
Process of automating the application of machine learning
search. In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form
Automated_machine_learning
semi-supervised machine-learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Type of machine learning model
computational and data constraints of their time. In the early 1990s, IBM's statistical models pioneered word alignment techniques for machine translation,
Large_language_model
Technique to solve partial differential equations
problems in mathematical physics, such as conservative laws, diffusion process, advection-diffusion systems, and kinetic equations. Given noisy measurements
Physics-informed neural networks
Physics-informed_neural_networks
Type of database that uses vectors to represent other data
audio, and other types of data, can all be vectorized. These feature vectors may be computed from the raw data using machine learning methods such as feature
Vector_database
Artificial intelligence research collective
2025. "CLIP-Guided Diffusion". EleutherAI. Archived from the original on 29 August 2023. Retrieved 20 August 2023. "CLIP Guided Diffusion HQ 256x256.ipynb
EleutherAI
Visual content generation through artificial intelligence
Diffusion Models Work". Towards Data Science. Archived from the original on 13 March 2025. Retrieved 12 June 2025. "Text-to-image: latent diffusion models"
AI_art
Partial differential equation describing the evolution of temperature in a region
"Scale-Space and Edge Detection Using Anisotropic Diffusion" (PDF), IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (7): 629–639, Bibcode:1990ITPAM
Heat_equation
Programmable machine that processes data
semiconductive surfaces for controlled diffusion", issued 13 August 1957 Archived 20 March 2025 at the Wayback Machine. Moskowitz, Sanford L. (2016). Advanced
Computer
Solution to a stochastic differential equation
processes are examples of diffusion processes. It is used heavily in statistical physics, statistical analysis, information theory, data science, neural networks
Diffusion_process
AI that learns decision rules from data
"Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets". The Plant Cell. 23 (9): 3101–3116. Bibcode:2011PlanC
Rule-based_machine_learning
Homogenous cloud producer
air intended primarily to make light beams visible or create a subtle diffusion. Unlike theatrical fog, which is typically intended to be dense and/or
Haze_machine
AI that generates content
and Qwen; text-to-image models such as DALL-E, Adobe Firefly, Stable Diffusion, and Midjourney; text-to-speech models such as 15.ai and ElevenLabs; text-to-music
Generative_AI
Machine learning methods using multiple input modalities
in E-commerce". arXiv:2112.11294 [cs.CV]. "Stable Diffusion Repository on GitHub". CompVis - Machine Vision and Learning Research Group, LMU Munich. 17
Multimodal_learning
Anime-focused imageboard website
platform, uses a custom version of the Stable Diffusion text-to-image diffusion model called NovelAI Diffusion, which is reported to be trained on a Danbooru-based
Danbooru
Machine learning technique
gradient boosting in their machine-learned ranking engines. Gradient boosting is also utilized in High Energy Physics in data analysis. At the Large Hadron
Gradient_boosting
Group of samples that have been tagged with one or more labels
After obtaining a labeled dataset, machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a
Labeled_data
Machine learning technique
with 15 billion parameters. MoE Transformer has also been applied for diffusion models. A series of large language models from Google used MoE. GShard
Mixture_of_experts
Academic conference in machine learning
The International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Machine learning strategy
case of machine learning in which a learning algorithm can interactively query a human user (or some other information source) to label new data points
Active learning (machine learning)
Active_learning_(machine_learning)
Difficulties arising when analyzing data with many aspects ("dimensions")
dimension of the data. Dimensionally cursed phenomena occur in domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and
Curse_of_dimensionality
Method of machine learning
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update
Online_machine_learning
Type of convolutional neural network
U-Net architecture. The U-Net architecture has also been employed in diffusion models for iterative image denoising. This technology underlies many modern
U-Net
Algorithm for modelling sequential data
generators like DALL-E (2021), Stable Diffusion 3 (2024), and Sora (2024), use transformers to analyse input data (like text prompts) by breaking it down
Transformer_(deep_learning)
Copyright law in the use of AI
text-to-image models such as Stable Diffusion and large language models such as GPT. Large language models are trained on all the data which can be scraped from
Artificial intelligence and copyright
Artificial_intelligence_and_copyright
Paradigm in machine learning that uses no classification labels
a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Other frameworks
Unsupervised_learning
Automated recognition of patterns and regularities in data
based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities
Pattern_recognition
Machine learning framework
Realistic artificially generated media Deep learning – Branch of machine learning Diffusion model – Technique for the generative modeling of a continuous
Generative adversarial network
Generative_adversarial_network
Approach in data analysis
remainder of that set of data. Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience
Anomaly_detection
Type of feedforward neural network
activation functions, organized in layers, notable for being able to distinguish data that is not linearly separable. Modern neural networks are trained using
Multilayer_perceptron
Statistical method
"Hop-Diffusion Monte Carlo for Epipolar Geometry Estimation between Very Wide-Baseline Images". IEEE Transactions on Pattern Analysis and Machine Intelligence
Random_sample_consensus
Class of algorithms for pattern analysis
similarity function over all pairs of data points computed using inner products. The feature map in kernel machines is infinite dimensional but only requires
Kernel_method
Optimization algorithm
Hluchý, Ladislav (19 January 2019). "Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey" (PDF). Artificial
Stochastic_gradient_descent
Software user interface
decisions in building a model. HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model. In simulation
Human-in-the-loop
Neural network that learns efficient data encoding in an unsupervised manner
of data. Some of the most powerful AIs in the 2010s involved autoencoder modules as a component of larger AI systems, such as VAE in Stable Diffusion, discrete
Autoencoder
Measurable property or characteristic
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating
Feature_(machine_learning)
Property of a model
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions
Bias–variance_tradeoff
Problem setup in machine learning
Zero-shot learning (ZSL) is a problem setup in machine learning where, at test time, a learner observes samples from classes which were not observed during
Zero-shot_learning
Academic conference in machine learning
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year.
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Machine learning paradigm
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather
Self-supervised_learning
Recurrent neural network architecture
has wide applications in classification, data processing, time series analysis tasks, speech recognition, machine translation, speech activity detection
Long_short-term_memory
Method of machine learning
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge
Incremental_learning
Properties of the operation of a secure cipher
output (ciphertext) by varying the application of the key to the data, while diffusion is hiding the plaintext statistics by spreading it over a larger
Confusion_and_diffusion
Machine learning technique
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization
Normalization (machine learning)
Normalization_(machine_learning)
Plot of machine learning model performance over time or experience
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and
Learning curve (machine learning)
Learning_curve_(machine_learning)
Intelligence in machines
unexplainable in deep learning. Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about
Artificial_intelligence
Set of learning techniques in machine learning
feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to
Representation_learning
Early unclassified symmetric-key block cipher
The Data Encryption Standard (DES /ˌdiːˌiːˈɛs, dɛz/) is a symmetric-key algorithm for the encryption of digital data. Although its short key length of
Data_Encryption_Standard
Method used to normalize the range of independent variables
data preprocessing step. Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly
Feature_scaling
Extracting features from raw data for machine learning
In supervised machine learning and statistical modeling, feature engineering is a preprocessing step which transforms raw data into a more effective set
Feature_engineering
Structuring text as input to generative artificial intelligence
depth-first, or beam. In 2022, text-to-image models like DALL-E 2, Stable Diffusion, and Midjourney were released to the public. These models take text prompts
Prompt_engineering
Conversational software
presented it more as a debunking exercise: In artificial intelligence, machines are made to behave in wondrous ways, often sufficient to dazzle even the
Chatbot
Machine-learning and computational-neuroscience conference
Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Iterative method for finding maximum likelihood estimates in statistical models
data points. For example, a mixture model can be described more simply by assuming that each observed data point has a corresponding unobserved data point
Expectation–maximization algorithm
Expectation–maximization_algorithm
Machine learning algorithm
learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision
Decision_tree_learning
New technologies actively in development
"Does quantum mechanics offer the best way to protect our most valuable data?". The Independent. 31 March 2011. Archived from the original on 2 April
List_of_emerging_technologies
Method in natural language processing
training data. Furthermore, word embeddings can even amplify these biases. Brown clustering Distributional–relational database Embedding (machine learning)
Word_embedding
ZestFinance's Zest Automated Machine Learning (ZAML) platform is used for credit underwriting. It uses machine learning to analyze data, including purchase transactions
Applications of artificial intelligence
Applications_of_artificial_intelligence
Software program
research group out of the University of Sussex created a Hallucination Machine, applying the DeepDream algorithm to a pre-recorded panoramic video, allowing
DeepDream
Similarity measure for number sequences
In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the
Cosine_similarity
Statistics and machine learning technique
stock market data and detect suspicious symptom of stock price manipulation. Implementation of ensemble learning not only with machine learning models
Ensemble_learning
Deep learning generative model to encode data representation
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in
Variational_autoencoder
Class of artificial neural network
recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where the order of elements is important
Recurrent_neural_network
Vector quantization algorithm minimizing the sum of squared deviations
simplicity and computational efficiency. It was later adopted in early machine learning and data analysis tasks involving large datasets. Despite its widespread
K-means_clustering
Technique for the generative modeling of a discrete probability distribution
In machine learning, discrete diffusion models are a class of diffusion models, which themselves are a class of latent variable generative models. Each
Discrete_diffusion_model
Paradigm in machine learning
paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to the large amount of data required
Weak_supervision
American entrepreneur (born 1981)
Figure Eight (formerly CrowdFlower), a data labeling and crowdsourcing company that created datasets for training machine learning models. Figure Eight was
Lukas_Biewald
Class of artificial neural networks
universal machine learning interatomic potentials (MLIPs) — models trained across diverse chemical domains rather than on system-specific data. Examples
Graph_neural_network
Machine learning technique
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related
Transfer_learning
Type of artificial neural network
output node j in the n-th data point (training example) by ej(n)=dj(n)−yj(n), where dj(n) is the desired target value for n-th data point at node j, and yj(n)
Feedforward_neural_network
2018 text-generating language model
primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets that
GPT-1
Field of machine learning
of Deep Reinforcement Learning to Policy Induction Attacks". Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science
Reinforcement_learning
Machine learning technique
In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression
Relevance_vector_machine
Computer-based technologies
controlled diffusion", issued 13 August 1957 Frosch, C. J.; Derick, L (1957). "Surface Protection and Selective Masking during Diffusion in Silicon"
Information_technology
Online service for AI media creation
concern that it is a paid service reliant on a diffusion model, while the original machine learning training data consists of images used without the consent
NovelAI
Algorithm for supervised learning of binary classifiers
perceptron units, to allow for processing sequential data, analyzing audio (instead of images). The machine was shipped from Cornell to Smithsonian in 1967
Perceptron
Framework for machine learning
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory
Statistical_learning_theory
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DATA DIFFUSION-MACHINE
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DATA DIFFUSION-MACHINE
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DATA DIFFUSION-MACHINE
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DATA DIFFUSION-MACHINE
DATA DIFFUSION-MACHINE
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