3,135 articles
NeMo Curator
NeMo Curator is NVIDIA's open-source, GPU-accelerated data curation framework for text, image, video, and audio datasets used in LLM and generative AI training, built on Ray and RAPIDS with deduplication, filtering, and multimodal pipeline stages.
Neo4j
Neo4j is a graph database management system that stores data as nodes connected by typed relationships, first released as open source in 2007 and known for the Cypher query language.
NequIP
NequIP (Neural Equivariant Interatomic Potentials) is an E(3)-equivariant graph neural network method for learning interatomic potentials for molecular dynamics, introduced in Nature Communications in 2022 and known for needing up to 1000 times less training data.
Nerfstudio
Nerfstudio is an open-source, modular PyTorch framework for developing, training, and deploying Neural Radiance Field (NeRF) methods, presented at SIGGRAPH 2023 by Matthew Tancik and colleagues from UC Berkeley.
Nesterov accelerated gradient
Nesterov accelerated gradient, also called Nesterov momentum, is an optimization method that evaluates the gradient at a look-ahead point, converging faster than plain gradient descent.
Netflix Prize
The Netflix Prize was a machine learning competition run by Netflix from 2006 to 2009, awarding $1 million for improving its movie recommendation algorithm by 10 percent.
Network clustering
Network clustering, also called community detection, is a graph analysis method that partitions a network's nodes into groups with dense internal connections and sparse links between groups.
Network model
The network model, also called the network data model, is a database model formalized by CODASYL in 1971 in which programs navigate records linked by pointers.
Network representation learning
Network representation learning maps each node of a graph to a dense low-dimensional vector, or embedding, preserving network structure for tasks like node classification and link prediction.
Neural architecture search
Neural architecture search (NAS) is a machine learning method that automatically discovers neural network architectures for a task, using a search space, a search strategy, and performance estimation.
Neural belief propagation
Neural belief propagation, also called weighted belief propagation (WBP), is a family of neural decoders for error-correcting codes, introduced by Nachmani, Be'ery, and Burshtein in 2016.
Neural cellular automata
Neural cellular automata, or NCAs, are machine learning models in which a grid of cells is updated by a learned local rule, training patterns to grow and self-repair.
Neural collaborative filtering
Neural collaborative filtering (NCF) is a recommendation framework proposed in 2017 that replaces matrix factorization's dot product with a neural network that learns the user–item interaction function from implicit feedback.
Neural collapse
Neural collapse is a set of geometric regularities, named by Papyan, Han, and Donoho in 2020, that emerge in a deep network's last layer during terminal-phase training.
Neural field
A neural field, also known as an implicit neural representation or coordinate-based network, is a field parameterized by a neural network, popularized by NeRF in 2020 for novel view synthesis.
Neural gas
Neural gas is an unsupervised vector-quantization algorithm introduced by Thomas Martinetz and Klaus Schulten in 1991, in which prototype vectors adapt by a rank-based update rule like gas molecules distributing in a space.
Neural graph fingerprints
The neural graph fingerprint, often abbreviated NFP, is a learned, real-valued molecular fingerprint computed by a convolutional neural network directly on a molecule's graph, introduced by Duvenaud, Maclaurin, and colleagues in 2015.
Neural machine translation
Neural machine translation (NMT) is an approach that translates whole sentences using a neural network, first deployed in Google Translate in 2016 and now the dominant paradigm.
Neural mass model
A neural mass model is a computational model simulating the averaged activity of large neuron populations, the preferred approach to modeling EEG and MEG since the 1970s.
Neural network
A neural network, or artificial neural network (ANN), is a computational model of layered units whose weights are adjusted from data, now among the most used AI methods.
Neural network (machine learning)
A neural network, or artificial neural network, is a machine learning model built from layers of neurons whose weights are trained by backpropagation and stochastic gradient descent.
Neural network Gaussian process
A neural network Gaussian process is the Gaussian process reached as a Bayesian neural network's hidden units tend to infinity, first shown by Radford Neal in 1994.
Neural network quantization
Neural network quantization converts a model's weights and activations from floating-point numbers to low-bit integers, reducing memory, power, and latency; methods include post-training quantization and quantization-aware training.
Neural operators
Neural operators are neural network architectures that learn mappings between infinite-dimensional function spaces, formalized by Kovachki and Li in 2023 and used as fast surrogates for partial differential equation solvers.
Neural Ordinary Differential Equation
A neural ordinary differential equation, or neural ODE, is a neural network model whose hidden state evolves continuously and is computed by an ODE solver, introduced in 2018.
Neural Radiance Field
Neural Radiance Field, or NeRF, is a 2020 computer vision method that represents a 3D scene as a neural network to render photorealistic novel views.
Neural style transfer
Neural style transfer is a computer vision technique that renders one image in the artistic style of a second image, using features from a deep convolutional neural network.
Neural tangent kernel
The neural tangent kernel (NTK) is a kernel built from a network's output gradients, introduced by Jacot, Gabriel, and Hongler at NeurIPS 2018 to describe training in function space.
NeuralGCM
NeuralGCM (Neural General Circulation Models) is a hybrid weather and climate model combining physics with machine learning, developed by Google Research and published in Nature in 2024.
Neuro-symbolic AI
Neuro-symbolic AI, also written neurosymbolic AI or NeSy, is the subfield of artificial intelligence that combines neural-network machine learning with symbolic knowledge representation and reasoning.