3,135 articles
Planar separator theorem
The planar separator theorem states that any n-vertex planar graph can be divided by at most 2√(2n) separator vertices, found in linear time by Lipton and Tarjan's 1979 algorithm.
Planarity testing
Planarity testing, also called graph planarity, is the algorithmic problem of deciding whether a graph can be drawn without crossing edges, solvable in linear time since 1974.
Plane wave expansion method
The plane wave expansion (PWE) method is a numerical technique that expands wave fields in plane waves, converting periodic-medium equations into matrix eigenvalue problems to compute photonic band structures.
Platt scaling
Platt scaling, also known as Platt calibration, is a post-hoc calibration method that fits a sigmoid to a classifier's raw scores, converting them into probabilities; John Platt introduced it for support vector machines.
Point cloud
A point cloud, or point cloud data, is a large set of 3D points representing object or terrain surfaces, typically produced by lidar or derived from overlapping photographs.
Point Cloud Library
The Point Cloud Library (PCL) is an open-source C++ library for 2D/3D image and point cloud processing, introduced by Radu Bogdan Rusu and Steve Cousins of Willow Garage in 2011.
Point in polygon
The point in polygon (PIP) problem asks whether a query point lies inside, outside, or on the boundary of a polygon, a basic operation in computational geometry.
Point operation (image processing)
A point operation is an image processing method that computes each output pixel's value from only the corresponding input pixel, through mappings such as brightness, contrast, or gamma adjustment.
Point pair features
Point pair features are local geometric descriptors that encode the relative position and orientation of two oriented points, underpinning a widely used 2010 method for 6D pose estimation in robotic bin picking.
Point set registration
Point set registration, also known as scan matching, is a computational geometry method that aligns point sets by estimating a spatial transformation, fundamental in robotics and computer vision.
Point tracking
Point tracking is a computer vision method that follows individually specified points across video frames, estimating each point's full trajectory and occlusion status, as in the tracking any point (TAP) formulation.
Point-in-time correctness
Point-in-time correctness: building ML training data with an AS OF join so each label row sees only feature values known at label time, preventing temporal data leakage that inflates offline scores; implemented in Feast, Tecton, Hopsworks, and native SQL ASOF joins.
Pointer network
A pointer network is a neural network whose decoder points at input positions instead of a fixed vocabulary, introduced by Vinyals, Fortunato, and Jaitly at NeurIPS 2015.
Polar code
A polar code is an error-correcting code that provably achieves channel capacity by polarizing bit-channels into reliable and unreliable ones, used in 5G control channels.
Polars (software)
Polars: open-source Rust DataFrame library and query engine for Python, R, NodeJS, using Apache Arrow columnar format, lazy query optimization, and streaming execution; first committed 23 June 2020 by Ritchie Vink.
Policy distillation
Policy distillation is a reinforcement learning technique that transfers a trained agent's policy into a smaller student network, compressing expensive policies or merging several expert agents into one multi-task model.
Policy gradient method
A policy gradient method is a reinforcement learning technique that optimizes a parameterized policy directly by gradient ascent on expected return, rather than learning a value function.
Polyglot persistence
Polyglot persistence: use of multiple, specialized database technologies within one application or enterprise, each chosen for its data's needs; introduced via Neal Ford's polyglot programming idea, popularized by Martin Fowler, central to NoSQL and microservices.
Polymer Genome
Polymer Genome is a free web-based platform that predicts polymer properties from chemical structure in seconds, developed by Rampi Ramprasad and co-workers at Georgia Tech.
Polynomial interpolation
Polynomial interpolation, also called interpolating polynomial, is the problem of finding the unique polynomial of degree at most n that passes exactly through n+1 given data points.
Polynomial kernel
The polynomial kernel is a kernel function in machine learning that computes a scaled, shifted inner product raised to a degree, letting support vector machines learn nonlinear boundaries.
Pooling layer
A pooling layer is a component of a convolutional neural network that downsamples feature maps by summarizing small local regions, most often with max or average pooling, reducing computation.
Population balance model
A population balance model is a mathematical framework describing how populations of particles, droplets, or cells evolve under growth, nucleation, aggregation, and breakage, predicting particle size distributions.
Pore network model
A pore network model (PNM) is a simulation method representing the void space of a porous medium as pores connected by throats, used in petroleum engineering.
Pose estimation (computer vision)
Pose estimation in computer vision is the task of determining from images the position and orientation of objects, cameras, or human bodies, supporting robotics, augmented reality, and motion capture.
Pose graph optimization
Pose graph optimization is a nonlinear least-squares back-end for SLAM that estimates a robot's trajectory from relative-pose constraints, serving as the standard global correction step after loop closure.
Positive-unlabeled learning
Positive-unlabeled learning is a machine learning method that trains a binary classifier from only positive and unlabeled examples, used when negative data is unavailable or too costly to collect.
Positronic brain
A positronic brain is a fictional device invented by Isaac Asimov as the thinking organ of his robots, built around the Three Laws of Robotics.
Possibility theory
Possibility theory is a mathematical framework for reasoning under uncertainty from incomplete or imprecise information rather than randomness, introduced by Lotfi Zadeh in 1978 and developed by Dubois and Prade.
Post-training quantization
Post-training quantization (PTQ) is a model-compression technique that converts trained neural network weights to lower bit-widths like INT8 without retraining, reducing model size and latency.