Machine Learning

It would be better to produce a simpler one to simulate a child's mind and then to subject it to a course of education.

Recent work has seen the resurgence of feature-based methods used in conjunction with machine learning techniques and complex optimization frameworks.

Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing.

Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems.

Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models.

The use of recommender systems is pervasive, with commonly recognised examples including the generation of playlists for video and music streaming services, product recommendations for e-commerce platforms, and topics and individual items posted on social media platforms and the open web. Online services employ machine learning or specialized deep learning recommendation models (DLRMs) that analyze user behavior and preferences to generate personalized content feeds for millions of users at any moment.

machine learning, data science, and network science

biomedical simulation, machine learning

one of the first machine learning algorithms

computer vision, machine learning

specialist in artificial neural networks

Professor in the Machine Learning department at the University of Copenhagen

In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one.

Seppo Ilmari Linnainmaa (born 28 September 1945) is a Finnish mathematician and computer scientist known for creating the modern version of backpropagation.

a foundational heuristic in machine learning and reinforcement learning

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.

the k-Nearest Neighbor (k-NN) algorithm

His most important contributions were his work on classification and regression trees, and ensembles of trees fit to bootstrap samples.

also known as Gaussian process regression, is a method of interpolation

In machine learning, Platt scaling or Platt calibration

In 1998, Platt invented sequential minimal optimization, a widely used algorithm for speeding up the training of support vector machines

has been further developed within the general fields of statistics, theoretical computer science and machine learning

The k-medoids method is used in unsupervised learning.

In data mining and machine learning fields, k-means++ is an algorithm

real-time deep learning image enhancement and upscaling technologies developed by Nvidia

VMAF uses existing image quality metrics, which are fused by a machine-learning model into a single quality score.

It is often used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.

it has been suggested as a theoretical foundation for deep learning

He worked on the mathematical and statistical theory of learning and biological adaptation.

Mamba is a deep learning architecture focused on sequence modeling.

is a paradigm in machine learning

artificial neural network models which use supervised and unsupervised learning methods

k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method that assigns weightage only to the k (number of) nearest neighbors of an entity in making a decision about the entity.

AdaBoost (short for Adaptive Boosting) is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gödel Prize for their work.

Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting.

perceptron is an algorithm for supervised learning of binary classifiers.

t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.

ID3 (Iterative Dichotomiser 3) is a greedy algorithm invented by Ross Quinlan used to generate a decision tree from a dataset.

C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan.

Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM).

Label propagation is a semi-supervised algorithm in machine learning that assigns labels to previously unlabeled data points.

Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data.

Viola-Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones.

Complete-linkage clustering is one of several methods of agglomerative hierarchical clustering.

UPGMA (unweighted pair group method with arithmetic mean) is a simple agglomerative (bottom-up) hierarchical clustering method.

PagedAttention is an attention algorithm for efficient serving of large language models (LLMs).

WPGMA (Weighted Pair Group Method with Arithmetic Mean) is a simple agglomerative (bottom-up) hierarchical clustering method, generally attributed to Sokal and Michener.

Symbolic regression (SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity.

Computer science, Machine learning

Kurt Bollacker is an American computer scientist with a research background in the areas of machine learning, digital libraries, semantic networks, and electro-cardiographic modeling.

Artificial neural network, Neural Networks Network on a chip, Networks on Chip Microprocessors

Machine learning Statistical learning theory Statistics Computer science

Fuzzy neural network, Soft computing, Machine intelligence, Pattern recognition

Machine Learning Kernel Methods Causal Inference

Computer vision Artificial intelligence Machine learning Discrete mathematics

Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training.

In statistics, naive (sometimes simple or idiot's) Bayes classifiers are a family of "probabilistic classifiers" which assume that the features are conditionally independent, given the target class.

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 a model of the environment (model-free).

A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.

In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates.

Neural style transfer (NST) software algorithms are able to manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation.

The junction tree algorithm (also known as 'Clique Tree') is a method used in machine learning to extract marginalization in general graphs.

Previously, she was a senior director of Machine Learning research at NVIDIA and a principal scientist at Amazon Web Services.

Cho is a researcher in machine learning, especially deep learning.

He was an early and influential researcher in German computer science, and was the developer of the Lernmatrix, an early implementation of artificial neural networks.

His research spans computational geometry, computer graphics, computer vision, machine learning, and robotics, with contributions including foundational data structures, the earth mover's distance for image retrieval, Metropolis light transport, and the PointNet architecture for deep learning on point clouds.

Her research takes a statistical approach to kernel methods, dimensionality reduction, and regularization in machine learning.