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pro vyhledávání: '"Sa P"'
Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT, which brought the phenomenon to wide public attention. As the deployment of AI products rises geometric
Externí odkaz:
http://arxiv.org/abs/2411.03449
This study addresses the computational inefficiencies in point cloud classification by introducing novel MLP-based architectures inspired by recent advances in CNN optimization. Traditional neural networks heavily rely on multiplication operations, w
Externí odkaz:
http://arxiv.org/abs/2409.01998
Autor:
Li, Sheng-Wei, Wei, Zi-Xiang, Chen, Wei-Jie, Yu, Yi-Hsin, Yang, Chih-Yuan, Hsu, Jane Yung-jen
Existing zero-shot skeleton-based action recognition methods utilize projection networks to learn a shared latent space of skeleton features and semantic embeddings. The inherent imbalance in action recognition datasets, characterized by variable ske
Externí odkaz:
http://arxiv.org/abs/2407.13460
In-context learning (ICL) leverages in-context examples as prompts for the predictions of Large Language Models (LLMs). These prompts play a crucial role in achieving strong performance. However, the selection of suitable prompts from a large pool of
Externí odkaz:
http://arxiv.org/abs/2407.05693
It was shown that pre-trained models with self-supervised learning (SSL) techniques are effective in various downstream speech tasks. However, most such models are trained on single-speaker speech data, limiting their effectiveness in mixture speech.
Externí odkaz:
http://arxiv.org/abs/2407.02826
Autor:
Cortês, Marina, Liddle, Andrew R., Emmanouilidis, Christos, Kelly, Anthony E., Matusow, Ken, Ragunathan, Ragu, Suess, Jayne M., Tambouratzis, George, Zalewski, Janusz, Bray, David A.
Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p
Externí odkaz:
http://arxiv.org/abs/2410.01808
Autor:
Aoyama, Temma
We deform the heat kernel and the Brownian motion on $\mathbb{R}^{N}$ from the perspective of "$(k,a)$-generalized Fourier analysis" with $k=0$. This is a new type of harmonic analysis proposed by S.Ben Sa\"id--T.Kobayashi--B.{\O}rsted from the repre
Externí odkaz:
http://arxiv.org/abs/2407.03664
Autor:
Chekan, Vera, Geniet, Colin, Hatzel, Meike, Pilipczuk, Michał, Sokołowski, Marek, Seweryn, Michał T., Witkowski, Marcin
For a group $\Gamma$, a $\Gamma$-labelled graph is an undirected graph $G$ where every orientation of an edge is assigned an element of $\Gamma$ so that opposite orientations of the same edge are assigned inverse elements. A path in $G$ is non-null i
Externí odkaz:
http://arxiv.org/abs/2408.16344
Graph neural networks (GNNs) have achieved impressive impressions for graph-related tasks. However, most GNNs are primarily studied under the cases of signal domain with supervised training, which requires abundant task-specific labels and is difficu
Externí odkaz:
http://arxiv.org/abs/2408.09189
An \emph{induced packing} of cycles in a graph is a set of vertex-disjoint cycles with no edges between them. We generalise the classic Erd\H{o}s-P\'osa theorem to induced packings of cycles. More specifically, we show that there exists a function ${
Externí odkaz:
http://arxiv.org/abs/2407.05883