Divergence measures for statistical data processing—An annotated bibliography
M Basseville - Signal Processing, 2013 - Elsevier
Divergence measures for statistical data processing—An annotated bibliography -
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A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF
This paper describes several important methods for the blind source separation of audio
signals in an integrated manner. Two historically developed routes are featured. One started …
signals in an integrated manner. Two historically developed routes are featured. One started …
BERTopic: Neural topic modeling with a class-based TF-IDF procedure
M Grootendorst - arxiv preprint arxiv:2203.05794, 2022 - arxiv.org
Topic models can be useful tools to discover latent topics in collections of documents.
Recent studies have shown the feasibility of approach topic modeling as a clustering task …
Recent studies have shown the feasibility of approach topic modeling as a clustering task …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Craft: Concept recursive activation factorization for explainability
Attribution methods are a popular class of explainability methods that use heatmaps to
depict the most important areas of an image that drive a model decision. Nevertheless …
depict the most important areas of an image that drive a model decision. Nevertheless …
Majorization-minimization algorithms in signal processing, communications, and machine learning
This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …
which can provide guidance in deriving problem-driven algorithms with low computational …
MOLI: multi-omics late integration with deep neural networks for drug response prediction
Motivation Historically, gene expression has been shown to be the most informative data for
drug response prediction. Recent evidence suggests that integrating additional omics can …
drug response prediction. Recent evidence suggests that integrating additional omics can …
Learning to separate object sounds by watching unlabeled video
Perceiving a scene most fully requires all the senses. Yet modeling how objects look and
sound is challenging: most natural scenes and events contain multiple objects, and the …
sound is challenging: most natural scenes and events contain multiple objects, and the …
A unified algorithmic framework for block-structured optimization involving big data: With applications in machine learning and signal processing
This article presents a powerful algorithmic framework for big data optimization, called the
block successive upper-bound minimization (BSUM). The BSUM includes as special cases …
block successive upper-bound minimization (BSUM). The BSUM includes as special cases …
Robust deep k-means: An effective and simple method for data clustering
Clustering aims to partition an input dataset into distinct groups according to some distance
or similarity measurements. One of the most widely used clustering method nowadays is the …
or similarity measurements. One of the most widely used clustering method nowadays is the …