3,135 articles
MobileNet
MobileNet is a family of efficient convolutional neural networks for computer vision, designed by Google researchers to run image classification and detection on mobile phones and embedded devices.
MoCo (machine learning)
MoCo (Momentum Contrast) is a contrastive self-supervised learning framework for visual representations, introduced by Kaiming He and colleagues at CVPR 2020, using a momentum-updated encoder and a queue of keys.
Mode Analytics
Mode Analytics is a code-first business intelligence platform combining a SQL editor, Python/R notebooks, and dashboards, founded in 2013 in San Francisco by Derek Steer, Benn Stancil, and Josh Ferguson and acquired by ThoughtSpot for $200 million in 2023.
Mode filter
The mode filter is a nonlinear image-processing filter that replaces each pixel with the most frequently occurring value among its neighbors, used for denoising and smoothing.
Mode filtering (image processing)
Mode filtering is an image processing filter that replaces each pixel with the most frequent value in its neighborhood, used for noise removal and cleaning classified maps.
Model distillation
Model distillation is a machine learning technique in which a small student model is trained to match a larger teacher's outputs, retaining over 95% of its performance.
Model editing (machine learning)
Model editing is a family of techniques that make targeted changes to a trained machine learning model's parameters or behavior without retraining it from scratch.
Model parallelism
Model parallelism is a family of techniques that split a single neural network across multiple devices, so no device holds the whole model; its main forms are tensor and pipeline parallelism.
Model repair
Model repair is a family of geometry processing algorithms that detect and fix defects such as holes, gaps, self-intersections, and non-manifold configurations in mesh and CAD models.
Model selection
Model selection, also called statistical model selection, is the task of choosing the candidate statistical or machine learning model expected to perform best on unseen data.
Model soup
A model soup is a neural network formed by averaging the weights of models fine-tuned from the same pre-trained initialization, introduced by Wortsman and colleagues in 2022.
Model tree (machine learning)
A model tree is a decision tree for regression whose leaves hold linear models, giving a piecewise linear predictor; the canonical algorithm is Quinlan's M5.
Model-agnostic meta-learning
Model-agnostic meta-learning (MAML) is a meta-learning algorithm, introduced by Chelsea Finn and colleagues in 2017, that trains initial parameters for fast adaptation to new tasks.
Model-free reinforcement learning
Model-free reinforcement learning is a class of algorithms that learn a policy or value function directly from an agent's experience, without estimating a model of the environment's dynamics.
ModelNet
ModelNet is a dataset of 127,915 3D CAD models in 660 object categories, created by Princeton researchers; its ModelNet10 and ModelNet40 subsets became standard benchmarks for 3D object classification.
Modern data stack
Modern data stack (MDS): modular, cloud-based tools for data ingestion, warehousing, transformation, BI, orchestration, observability, and governance. Built on ELT around cloud warehouses like Snowflake and Databricks; criticized as a marketing term amid tool sprawl and vendor consolidation.
Modular neural network
A modular neural network is a machine learning architecture that decomposes computation into specialized modules, combined by a routing or gating mechanism to solve the overall task.
MOEA/D
MOEA/D (Multi-Objective Evolutionary Algorithm Based on Decomposition) is an evolutionary algorithm, proposed by Qingfu Zhang and Hui Li in 2007, that decomposes a multiobjective optimization problem into scalar subproblems solved simultaneously.
Molecular representation learning
Molecular representation learning encodes chemical structures into reusable numerical embeddings, typically with graph neural networks, supporting property prediction, toxicity prediction, protein binding affinity, and drug discovery.
Molecular Transformer
The Molecular Transformer is a deep learning model that predicts chemical reaction products by treating reactions as translation between SMILES strings, developed at IBM Research in 2018.
Molecular variational autoencoder
Chemical VAE (chemical variational autoencoder) is a deep generative model that compresses molecules into a latent space to generate new compounds, introduced at Harvard in 2018.
MolFormer
MoLFormer is a large-scale chemical language model developed at IBM that encodes molecules as SMILES strings using linear attention, pretrained on about 1.1 billion unlabelled molecules from PubChem and ZINC.
MolGAN
MolGAN is a generative adversarial model that creates small molecular graphs directly, introduced by Nicola De Cao and Thomas Kipf in 2018; it reaches 98.1% chemically valid output but suffers from mode collapse.
Momentum method
The momentum method is an optimization technique in machine learning that adds a fraction of the previous update to each gradient step, dampening oscillations and speeding convergence.
MONAI
MONAI (Medical Open Network for AI) is a free, open-source deep learning framework for medical imaging built on PyTorch, developed by a consortium including NVIDIA, NIH, and King's College London.
MongoDB
MongoDB is a document-oriented NoSQL database, first released in 2009, that stores data as JSON-like BSON documents in schema-flexible collections instead of table rows, and ranks first among document stores.
Monocular depth estimation
Monocular depth estimation is a computer vision method that predicts a dense per-pixel depth map from a single RGB image, typically with a neural network.
Monte Carlo algorithm
A Monte Carlo algorithm is a randomized algorithm that always finishes in fixed time but may err with bounded probability, contrasted with the Las Vegas algorithm.
Monte Carlo ray tracing
Monte Carlo ray tracing is a family of stochastic rendering methods that estimate the light at each pixel by averaging randomly sampled light paths, introduced by James Kajiya in 1986.
Morphological filtering
Morphological filtering is a nonlinear image-processing technique that probes an image with a small template called a structuring element, built from erosions and dilations, to remove noise, enhance shapes, and extract features.