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Characterizes a recent neural network architecture providing advances in time series deep learning
In deep learning, state space models (SSMs) are a type of neural network used to process long streams of sequential data, such as text, speech, or sensor
State space model (deep learning)
State_space_model_(deep_learning)
Deep learning architecture
Mamba is a deep learning architecture focused on sequence modeling. It was developed by two researchers Albert Gu from Carnegie Mellon University and
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the
Deep_reinforcement_learning
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data
Transformer_(deep_learning)
Subset of artificial intelligence
correct learning provides a mathematical and statistical framework for describing machine learning. Most traditional machine learning and deep learning algorithms
Machine_learning
Field of machine learning
explicitly designing the state space. The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement
Reinforcement_learning
Machine learning technique
reward model to represent preferences, which can then be used to train other models through reinforcement learning. In classical reinforcement learning, an
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Branch of machine learning
intend to model the brain function of organisms, and are generally seen as low-quality models for that purpose. Most modern deep learning models are based
Deep_learning
Type of machine learning model
Nvidia software development kit for deep learning inference vLLM – Open-source software for large language model inference Brown, Tom B.; Mann, Benjamin;
Large_language_model
Sub-field of reinforcement learning
discrimination. Similarly to single-agent reinforcement learning, multi-agent reinforcement learning is modeled as some form of a Markov decision process (MDP)
Multi-agent reinforcement learning
Multi-agent_reinforcement_learning
Techniques for lossy compression of neural networks
Model compression is a machine learning technique for reducing the size of trained models. Large models can achieve high accuracy, but often at the cost
Model_compression
and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
The state space of is Go is around 10^170 possible board states compared to the 10^120 board states for Chess. Prior to recent deep learning models, AI
Machine learning in video games
Machine_learning_in_video_games
Artificial intelligence model paradigm
foundation model (FM), also known as large x model (LxM, where "x" is a variable representing any text, image, sound, etc.), is a machine learning or deep learning
Foundation_model
Star Trek: Deep Space Nine is the third live-action television series in the Star Trek franchise and aired in syndication from January 1993 through June
List of Star Trek: Deep Space Nine episodes
List_of_Star_Trek:_Deep_Space_Nine_episodes
Research field in deep learning
deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models
Topological_deep_learning
Tabular comparison of deep learning software
for machine-learning research List of numerical-analysis software Mamba – deep learning architecture based on selective state-space models MLIR compiler
Comparison of deep learning software
Comparison_of_deep_learning_software
Machine learning technique
published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model of the topic. In 1981, a report
Transfer_learning
Model-free reinforcement learning algorithm
Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring
Q-learning
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
Self-supervised_learning
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
Class of reinforcement learning algorithm
In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward
Model-free (reinforcement learning)
Model-free_(reinforcement_learning)
Overview of and topical guide to machine learning
Semi-supervised learning Active learning Generative models Low-density separation Graph-based methods Co-training Transduction Deep learning Deep belief networks
Outline_of_machine_learning
Internal representation of world by AI
A world model in artificial intelligence is a machine learning system that builds an internal representation of an environment. Often this is via understanding
World model (artificial intelligence)
World_model_(artificial_intelligence)
Statistics and machine learning technique
Ensemble learning trains two or more machine learning algorithms on a specific classification or regression task. The algorithms within the ensemble model are
Ensemble_learning
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs
History of artificial neural networks
History_of_artificial_neural_networks
Machine learning model
technique is to train a model to generate low-resolution images or latent space, and use one or more auxiliary deep learning models to upscale or decode
Text-to-image_model
Machine learning technique
rather than only through the previous state. Additional surveys of the attention mechanism in deep learning are provided by Niu et al. and Soydaner
Attention_(machine_learning)
Foundation model allowing control of robot actions
In robot learning, a vision–language–action model (VLA) is a class of multimodal foundation models that integrates vision, language and actions. Given
Vision–language–action_model
Interdisciplinary research area
machine learning. QML algorithms use qubits and quantum operations to try to improve the space and time complexity of classical machine learning algorithms
Quantum_machine_learning
AI that generates content
high-quality and realistic outputs. Variational autoencoders (VAEs) are deep learning models that probabilistically encode data. They are typically used for tasks
Generative_AI
AI research laboratory
few days of play against itself using reinforcement learning. DeepMind has since trained models for game-playing (MuZero, AlphaStar), for mathematics
Google_DeepMind
Type of feedforward neural network
that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different
Convolutional_neural_network
Concept in machine learning
as tensors at each point in space, are useful in expressing mechanics such as stress or elasticity. In machine learning, the exact use of tensors depends
Tensor_(machine_learning)
Research field that lies at the intersection of machine learning and computer security
demonstrated the first gradient-based attacks on such machine-learning models (2012–2013). In 2012, deep neural networks began to dominate computer vision problems;
Adversarial_machine_learning
Artificial neural network algorithm
the weights and biases. Depending on the complexity of the model being simulated, the learning rule of the network can be as simple as an XOR gate or mean
Learning_rule
Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of a software
Action_model_learning
"Deep Learning". CiteSeerX 10.1.1.297.6176. {{cite CiteSeerX}}: Cite uses deprecated parameter |citeseerx= (help) S. Bozinovski (1981) "Teaching space:
Timeline_of_machine_learning
Deep learning artificial intelligence research team
Google Brain was a deep learning artificial intelligence research team that served as the sole AI branch of Google before being incorporated under the
Google_Brain
Series of language models developed by Google AI
language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It
BERT_(language_model)
largest model is shown. The number of parameters is measured in billions, and the training cost is measured in petaFLOP-days. Comparison of deep learning software
List_of_large_language_models
Applications of machine learning to quantum physics
Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example
Machine_learning_in_physics
Large language model by Meta AI (2023–2026)
largest Llama 2. Compared to previous models, Zuckerberg stated the team was surprised that the 70B model was still learning even at the end of the 15T tokens
Llama_(language_model)
Computational model used in machine learning
In machine learning, a neural network (NN) or artificial neural network (ANN) is a computational model inspired by the structure and functions of biological
Neural network (machine learning)
Neural_network_(machine_learning)
2017 research paper by Google
research paper on machine learning authored by eight scientists and engineers working at Google. The paper introduced a new deep learning architecture known
Attention_Is_All_You_Need
Intelligence in machines
(2015). "Deep learning". Nature. 521: 436–444. doi:10.1038/nature14539. Sagar, Ram (3 June 2020). "OpenAI Releases GPT-3, The Largest Model So Far". Analytics
Artificial_intelligence
Type of artificial intelligence model trained on Earth observation and geoscientific data
Unlike conventional, task-specific deep learning systems, GFMs use massive cross-disciplinary datasets to model the Earth's interactive and complex dynamics
Geospatial_foundation_model
Structuring text as input to generative artificial intelligence
larger models than in smaller models. Unlike training and fine-tuning, which produce lasting changes, in-context learning is temporary. Training models to
Prompt_engineering
Class of artificial neural network
maint: miscellaneous url (link) Pearlmutter, Barak A. (1989-06-01). "Learning State Space Trajectories in Recurrent Neural Networks". Neural Computation. 1
Recurrent_neural_network
Model-free reinforcement learning algorithm
reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy
Proximal_policy_optimization
Image-generating deep learning model
2, and DALL-E 3 (stylised DALL·E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural
DALL-E
Machine learning framework
Neural operators are a class of deep learning architectures designed to learn maps between infinite-dimensional function spaces. Neural operators represent
Neural_operators
Subfield of control engineering
research. In comparison to traditional machine learning, due to their deep architecture, deep learning models are able to learn more complex structures from
Fault_detection_and_isolation
Technique to solve partial differential equations
adequate time and space discretization. Recently, solving the governing partial differential equations of physical phenomena using deep learning has emerged
Physics-informed neural networks
Physics-informed_neural_networks
Statistical Markov model
hidden state at time t is chosen given the hidden state at time t − 1. The hidden state space is assumed to consist of one of N possible values, modelled as
Hidden_Markov_model
Classification of Artificial Neural Networks (ANNs)
propagation (supervised learning). A convolutional neural network (CNN, or ConvNet or shift invariant or space invariant) is a class of deep network, composed
Types of artificial neural networks
Types_of_artificial_neural_networks
Algorithm for supervised learning of binary classifiers
linear model can produce some behavior seen in real neurons. The solution spaces of decision boundaries for all binary functions and learning behaviors
Perceptron
American artificial intelligence subsidiary of SpaceX
models. It operates a data center business centered on its Colossus cluster; Anthropic and Google are major customers. In Musk v. Altman, Musk stated
SpaceXAI
Set of methods for supervised statistical learning
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that
Support_vector_machine
Solving multiple machine learning tasks at the same time
result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Inherently
Multi-task_learning
Machine learning technique
experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous regions. MoE
Mixture_of_experts
Automated recognition of patterns and regularities in data
of analyzing large data sets Deep learning – Branch of machine learning Grey box model – Mathematical data production model with limited structure Information
Pattern_recognition
Digital technology which directs AI models to perform tasks
Code, OpenAI's Codex, Google DeepMind's Antigravity, and SpaceXAI's Cursor. China's Qwen, DeepSeek, Kimi, and GLM models are also offered with official
Agent_harness
Artificial intelligence program by DeepMind
program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold
AlphaFold
Concept in decision-making
possible in small and discrete state space. Density-based exploration extends count-based exploration by using a density model ρ n ( s ) {\displaystyle \rho
Exploration–exploitation dilemma
Exploration–exploitation_dilemma
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
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
Machine learning technique useful for dimensionality reduction
nodes an input has in the map. Deep learning Hybrid Kohonen self-organizing map Learning vector quantization Liquid state machine Neocognitron Neural gas
Self-organizing_map
Methods in artificial intelligence research
articles. New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance
Symbolic artificial intelligence
Symbolic_artificial_intelligence
Memorization technique based on repetition
alternatives to rote learning include meaningful learning, associative learning, spaced repetition and active learning. Rote learning is widely used in the
Rote_learning
Attribute of machine learning models
running a Monte Carlo tree search. It is equivalent to a model-free brute force search in the state space. In contrast, a high-efficiency algorithm has a low
Sample_complexity
Flaw in mathematical modelling
some generative deep learning models such as Stable Diffusion and GitHub Copilot being sued for copyright infringement because these models have been found
Overfitting
Software for understanding biological data
structure prediction, this proved difficult. Machine learning techniques such as deep learning can learn features of data sets rather than requiring
Machine learning in bioinformatics
Machine_learning_in_bioinformatics
Family of large language models by Google
Gemma is a series of source-available large language models developed by Google DeepMind. It is based on similar technologies as Gemini. The first version
Gemma_(language_model)
Artificial neural network that mimics neurons
networks. These models leverage timing of discrete spikes as the main information carrier. In addition to neuronal and synaptic state, SNNs incorporate
Spiking_neural_network
Large language model developed by Google
large language models (LLMs) developed by Google DeepMind, and the successor to LaMDA and PaLM 2. Comprising Gemini Pro, Gemini Deep Think, Gemini Flash
Gemini_(language_model)
Iterative method for finding maximum likelihood estimates in statistical models
solutions require estimates of the state-space model parameters. EM algorithms can be used for solving joint state and parameter estimation problems.
Expectation–maximization algorithm
Expectation–maximization_algorithm
Toolkit for deploying inference neural network model on Intel hardware
optimizing and deploying deep learning models. It supports several popular model formats and workloads, including large language models, computer vision, generative
OpenVINO
Difficulties arising when analyzing data with many aspects ("dimensions")
state space. This is a significant obstacle when the dimension of the "state variable" is large. In machine learning problems that involve learning a
Curse_of_dimensionality
Machine learning-powered structure design
design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par with
Neural_architecture_search
Statistical method
influence on the result. The RANSAC algorithm is a learning technique to estimate parameters of a model by random sampling of observed data. Given a dataset
Random_sample_consensus
Machine learning model for vision processing
exaFLOPs. Transformer (machine learning model) Convolutional neural network Attention (machine learning) Perceiver Deep learning PyTorch TensorFlow All positional
Vision_transformer
German computer scientist (born 1963)
2021. Wang, Brian (14 June 2017). "Father of deep learning AI on General purpose AI and AI to conquer space in the 2050s". Next Big Future. Retrieved 27
Jürgen_Schmidhuber
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Mechanism for enabling artificial agents to exhibit curiosity
difficulty of these models is the intractability of computing probability distributions over large discrete or continuous state spaces. Nonetheless, a considerable
Intrinsic motivation (artificial intelligence)
Intrinsic_motivation_(artificial_intelligence)
Process of acquiring new knowledge
with learning favored in environments that are neither completely stable nor entirely unpredictable. Recent syntheses have also noted that models of learning
Learning
Type of supervised learning in machine learning
In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually
Multiple_instance_learning
Machine learning framework
generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The
Generative adversarial network
Generative_adversarial_network
Artificial intelligence concept
et al. (2017). "Data-efficient deep reinforcement learning for dexterous manipulation". arXiv:1704.03073 [cs.LG]. "Learning from Human Preferences". OpenAI
Reward_hacking
Machine-learning process
in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or
Grammar_induction
Branch of artificial intelligence
the use of state constraints (see STRIPS, graphplan) partial-order planning Action model learning (sometimes abbreviated action learning) is an area
Automated planning and scheduling
Automated_planning_and_scheduling
Technology made by American organization
many projects focused on reinforcement learning (RL). OpenAI has been viewed as an important competitor to DeepMind. Announced in 2016, Gym was an open-source
Products and applications of OpenAI
Products_and_applications_of_OpenAI
Recurrent neural network architecture
gap length is its advantage over other RNNs, hidden Markov models, and other sequence learning methods. It aims to provide a short-term memory for RNN that
Long_short-term_memory
Type of regression analysis
the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is
Symbolic_regression
Conversational software
conversation partner. Chatbots have existed for decades, but chatbots based on deep learning have gained popularity during the AI boom of the 2020s, with the releases
Chatbot
Smooth approximation of one-hot arg max
Distributions". Deep Learning. MIT Press. pp. 180–184. ISBN 978-0-26203561-3. Bishop, Christopher M. (2006). Pattern Recognition and Machine Learning. Springer
Softmax_function
Reinforcement learning algorithms
exploration. Deep Deterministic Policy Gradient (DDPG): Specialized for continuous action spaces. Reinforcement learning Policy gradient method Deep reinforcement
Actor-critic_algorithm
Quantum Mechanics in Neural Networks
involves combining classical artificial neural network models (which are widely used in machine learning for the important task of pattern recognition) with
Quantum_neural_network
Technique for the generative modeling of a discrete probability distribution
machine learning, discrete diffusion models are a class of diffusion models, which themselves are a class of latent variable generative models. Each discrete
Discrete_diffusion_model
Biological theory of intelligence
Professor Kunihiko Fukushima in 1987, is one of the first deep learning neural network models. Artificial consciousness Artificial general intelligence
Hierarchical_temporal_memory
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STATE SPACE-MODEL-DEEP-LEARNING
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