Gaussian Mixture Models

By Douglas A. Reynolds. 2018

2,798 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

The Curse of Recursion: Training on Generated Data Makes Models Forget (2023, methodology): “In this subsection we evaluate the performance of Gaussian Mixture Models (GMM) [Reynolds et al., 2009].”
Local Aggregation for Unsupervised Learning of Visual Embeddings (2019, methodology): “Given the need for cluster-based identification, a variety of alternative approaches to k-means are theoretically possible, including DBSCAN [17], Affinity Propagation [19], spectral methods [63, 66], and gaussian mixtures [60, 61].”
Unsupervised Continual Learning Via Pseudo Labels (2021, methodology): “We focus on using a general clustering method, K-means [29], while the experimental results on other existing clustering methods such as Gaussian Mixture Models (GMMs) [40] is also shown in Section 5.”
The effects of data quality on machine learning performance on tabular data (2022, positive): “…to use one algorithm from each of the five most commonly used categories of clustering algorithms [59]: the Gaussian mixture clustering algorithm [58] from the distribution-based family; the 𝑘 -means [66] and the 𝑘 -prototypes [35] algorithms from the centroid-based family; the agglomerative…”
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation (2023, background): “However, as mentioned in the Gaussian Mixture Model (GMM) [33], the natural samples from the same category should come from the mixture of multiple Gaussian distributions.”
Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse (2021, neutral): “Validation of the simulation through GMM-based propagation modes.”
The Eﬀects of Data Quality on Machine Learning Performance (2022, methodology): “…to use one algorithm from each of the ﬁve most commonly used categories of clustering algorithms [58]: The Gaussian Mixture Clustering al-gorithm [57] from the distribution-based family, the k-Means [65] and the k-Proto-types [36] algorithms from the centroid-based family, the Agglomerative…”
Continual Learning Using Bayesian Neural Networks (2019, methodology): “When n = k, this process can be interpreted as a special case of ensemble methods since there are k set of parameters without being merged. q(w1:k) = k∑ j αq(wj) ≈ q(w1:n) = n∑ j βq(wj) where n <= k (2) To obtain the final posterior distribution q(w1:n) and restrict the sudden increase in the number”
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions (2023, background): “Support vector machines (SVMs) [34], linear or nonlinear dynamical systems, and Gaussian mixture models (GMMs) are an example of these designs [46].”
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors (2018, methodology): “…Bayesian Nonparametric models to learn different word representations for each sense of a word, Nguyen et al. (2017) use a Gaussian Mixture Model (Reynolds 2009) and Athiwaratkun, Wilson, and Anandkumar (2018) extend FastText word representations (Bojanowski et al. 2016) with a Gaussian Mixture…”
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture (2018, background): “Although a large number of data clustering methods have been proposed [1],[2],[3],[4],[5], conventional clustering methods usually have poor performance on high-dimensional data, due to the inefficiency of similarity measures used in these methods.”
Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences (2015, methodology): “We used a Gaussian Mixture Model (GMM) [27] initialised with K-means++ [1], where K was found by using 10% of the set and a validation set to find the best likelihood.”
Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “Gaussian Mixture Model GMM assumes that a mixture of multiple Gaussian distributions with unknown parameters generates all the data points, where each Gaussian distribution is associated with one cluster [194, 195].”
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, methodology): “GMM parameters (i.e., the proportions, the mean vectors and the covariance matrices of the Gaussian components) are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or through Maximum a posteriori (MAP) estimation from a well-trained prior model [99].”
Diagonal rnns in symbolic music modeling (2017, neutral): “Index Terms— Recurrent Neural Networks, Symbolic Music Modeling”
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT (2023, background): “Generative models have a long history in artificial intelligence, dating back to the 1950s with the development of Hidden Markov Models (HMMs) [20] and Gaussian Mixture Models (GMMs) [21].”
Oriented RepPoints for Aerial Object Detection (2021, methodology): “[37] build a score of box samples in classification and localization between its regressed box and ground truth, where PAA [37] formulates the box sample selection as a likelihood maximization with one-dimensional GMM [40].”