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pro vyhledávání: '"Graph-Cuts"'
Graph cuts are among the most prominent tools for clustering and classification analysis. While intensively studied from geometric and algorithmic perspectives, graph cut-based statistical inference still remains elusive to a certain extent. Distribu
Externí odkaz:
http://arxiv.org/abs/2407.15297
In this paper, we address the challenge of differential privacy in the context of graph cuts, specifically focusing on the multiway cut and the minimum $k$-cut. We introduce edge-differentially private algorithms that achieve nearly optimal performan
Externí odkaz:
http://arxiv.org/abs/2407.06911
Akademický článek
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A common way of partitioning graphs is through minimum cuts. One drawback of classical minimum cut methods is that they tend to produce small groups, which is why more balanced variants such as normalized and ratio cuts have seen more success. Howeve
Externí odkaz:
http://arxiv.org/abs/2402.04732
Unsupervised image segmentation aims at grouping different semantic patterns in an image without the use of human annotation. Similarly, image clustering searches for groupings of images based on their semantic content without supervision. Classicall
Externí odkaz:
http://arxiv.org/abs/2311.01475
Akademický článek
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Conditional gradients for total variation regularization with PDE constraints: a graph cuts approach
Total variation regularization has proven to be a valuable tool in the context of optimal control of differential equations. This is particularly attributed to the observation that TV-penalties often favor piecewise constant minimizers with well-beha
Externí odkaz:
http://arxiv.org/abs/2310.19777
Due to their computational complexity, graph cuts for cluster detection and identification are used mostly in the form of convex relaxations. We propose to utilize the original graph cuts such as Ratio, Normalized or Cheeger Cut to detect clusters in
Externí odkaz:
http://arxiv.org/abs/2308.09613
The BDD package Adiar manipulates Binary Decision Diagrams (BDDs) in external memory. This enables handling big BDDs, but the performance suffers when dealing with moderate-sized BDDs. This is mostly due to initializing expensive external memory data
Externí odkaz:
http://arxiv.org/abs/2307.04488
Autor:
Robinson, Kyle, Brown, Dan
Publikováno v:
Late Breaking/Demo at the 20th International Society for Music Information Retrieval, Delft, The Netherlands, 2019
The problem of transitioning smoothly from one audio clip to another arises in many music consumption scenarios, especially as music consumption has moved from professionally curated and live-streamed radios to personal playback devices and services.
Externí odkaz:
http://arxiv.org/abs/2301.13380