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pro vyhledávání: '"Seidl Thomas"'
Finding meaningful groups, i.e., clusters, in high-dimensional data such as images or texts without labeled data at hand is an important challenge in data mining. In recent years, deep clustering methods have achieved remarkable results in these task
Externí odkaz:
http://arxiv.org/abs/2410.09491
Autor:
Winkel, David, Strauß, Niklas, Bernhard, Maximilian, Li, Zongyue, Seidl, Thomas, Schubert, Matthias
Allocation tasks represent a class of problems where a limited amount of resources must be allocated to a set of entities at each time step. Prominent examples of this task include portfolio optimization or distributing computational workloads across
Externí odkaz:
http://arxiv.org/abs/2409.18735
Autor:
Margraf, Valentin, Wever, Marcel, Gilhuber, Sandra, Tavares, Gabriel Marques, Seidl, Thomas, Hüllermeier, Eyke
In settings where only a budgeted amount of labeled data can be afforded, active learning seeks to devise query strategies for selecting the most informative data points to be labeled, aiming to enhance learning algorithms' efficiency and performance
Externí odkaz:
http://arxiv.org/abs/2406.17322
We introduce SA-DQAS in this paper, a novel framework that enhances the gradient-based Differentiable Quantum Architecture Search (DQAS) with a self-attention mechanism, aimed at optimizing circuit design for Quantum Machine Learning (QML) challenges
Externí odkaz:
http://arxiv.org/abs/2406.08882
Publikováno v:
ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Krakow, Poland
Portfolio optimization tasks describe sequential decision problems in which the investor's wealth is distributed across a set of assets. Allocation constraints are used to enforce minimal or maximal investments into particular subsets of assets to co
Externí odkaz:
http://arxiv.org/abs/2404.10683
Locating specific moments within long videos (20-120 minutes) presents a significant challenge, akin to finding a needle in a haystack. Adapting existing short video (5-30 seconds) grounding methods to this problem yields poor performance. Since most
Externí odkaz:
http://arxiv.org/abs/2312.06729
Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by results showing that combining semi-supervised learning (SSL) methods
Externí odkaz:
http://arxiv.org/abs/2308.08224
Publikováno v:
ECML PKDD 2023: Machine Learning and Knowledge Discovery in Databases: Research Track pp 75-91
Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data. To keep annotation costs low, active graph learning focuses on selecting the most qualitative subset of node
Externí odkaz:
http://arxiv.org/abs/2308.00146
Autor:
Hannan, Tanveer, Koner, Rajat, Bernhard, Maximilian, Shit, Suprosanna, Menze, Bjoern, Tresp, Volker, Schubert, Matthias, Seidl, Thomas
Recent trends in Video Instance Segmentation (VIS) have seen a growing reliance on online methods to model complex and lengthy video sequences. However, the degradation of representation and noise accumulation of the online methods, especially during
Externí odkaz:
http://arxiv.org/abs/2305.17096
Autor:
Koner, Rajat, Hannan, Tanveer, Shit, Suprosanna, Sharifzadeh, Sahand, Schubert, Matthias, Seidl, Thomas, Tresp, Volker
Publikováno v:
Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-2023)
Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by fu
Externí odkaz:
http://arxiv.org/abs/2208.10547