Given the increased percentage of wind power in power systems, chance-constrained optimal power flow (CC-OPF) calculation, as a means to take wind power uncertainty into account with a guaranteed security level, is being promoted.
This method is based on a high-precision linear power flow model, whose precision is even further improved in this paper by an original correction approach.
For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech.
In the recent trend of semi-supervised speech recognition, both self-supervised representation learning and pseudo-labeling have shown promising results.
no code implementations • 19 Aug 2021 • Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku MORI
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction on target behavior.
Then, an orientation independent vector C is developed to eliminate the probability distribution differences of power outputs caused by varying azimuth angles and tilt angles.
The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks.
Singing voice conversion (SVC) aims to convert the voice of one singer to that of other singers while keeping the singing content and melody.
no code implementations • 28 Sep 2020 • Pochuan Wang, Chen Shen, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Kazunari Misawa, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Wei-Chung Wang, Kensaku MORI
The performance of deep learning-based methods strongly relies on the number of datasets used for training.
Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution.
Ranked #3 on Unbiased Scene Graph Generation on Visual Genome
We show that the early arrival rate of comments is the best indicator of the eventual number of comments.
Specifically, the proposed module first embeds the scene concepts into factored weights explicitly and attends the visual information extracted from the input image.
Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for deriving the conditional probability distribution.
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images.
In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem.
However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision.
Deep learning-based methods achieved impressive results for the segmentation of medical images.