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of 3 492
pro vyhledávání: '"Song, Kun"'
Autor:
Song, Kun, Solozabal, Ruben, hao, Li, Ren, Lu, Abdar, Moloud, Li, Qing, Karray, Fakhri, Takac, Martin
Hyperbolic representation learning is well known for its ability to capture hierarchical information. However, the distance between samples from different levels of hierarchical classes can be required large. We reveal that the hyperbolic discriminan
Externí odkaz:
http://arxiv.org/abs/2410.22026
In this paper, we utilize information-theoretic metrics like matrix entropy and mutual information to analyze supervised learning. We explore the information content of data representations and classification head weights and their information interp
Externí odkaz:
http://arxiv.org/abs/2409.16767
Robot exploration aims at constructing unknown environments and it is important to achieve it with shorter paths. Traditional methods focus on optimizing the visiting order based on current observations, which may lead to local-minimal results. Recen
Externí odkaz:
http://arxiv.org/abs/2409.10878
Due to its high speed and low latency, DVS is frequently employed in motion deblurring. Ideally, high-quality events would adeptly capture intricate motion information. However, real-world events are generally degraded, thereby introducing significan
Externí odkaz:
http://arxiv.org/abs/2407.20502
Autor:
Ma, Linhan, Guo, Dake, Song, Kun, Jiang, Yuepeng, Wang, Shuai, Xue, Liumeng, Xu, Weiming, Zhao, Huan, Zhang, Binbin, Xie, Lei
With the development of large text-to-speech (TTS) models and scale-up of the training data, state-of-the-art TTS systems have achieved impressive performance. In this paper, we present WenetSpeech4TTS, a multi-domain Mandarin corpus derived from the
Externí odkaz:
http://arxiv.org/abs/2406.05763
In real-world cooperative manipulation of objects, multiple mobile manipulator systems may suffer from disturbances and asynchrony, leading to excessive interaction wrenches and potentially causing object damage or emergency stops. This paper present
Externí odkaz:
http://arxiv.org/abs/2406.05613
In this paper, we use matrix information theory as an analytical tool to analyze the dynamics of the information interplay between data representations and classification head vectors in the supervised learning process. Specifically, inspired by the
Externí odkaz:
http://arxiv.org/abs/2406.03999
Mutual localization serves as the foundation for collaborative perception and task assignment in multi-robot systems. Effectively utilizing limited onboard sensors for mutual localization between marker-less robots is a worthwhile goal. However, due
Externí odkaz:
http://arxiv.org/abs/2405.11726
Rendezvous aims at gathering all robots at a specific location, which is an important collaborative behavior for multirobot systems. However, in an unknown environment, it is challenging to achieve rendezvous. Previous researches mainly focus on spec
Externí odkaz:
http://arxiv.org/abs/2405.08345
During complex object manipulation, manipulator systems often face the configuration disconnectivity problem due to closed-chain constraints. Although regrasping can be adopted to get a piecewise connected manipulation, it is a challenging problem to
Externí odkaz:
http://arxiv.org/abs/2312.06168