Search Results for author:
Found 19 papers, 2 papers with code
Chance-constrained OPF: A Distributed Method with Confidentiality Preservation
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.
Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method
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.
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech.
Improving Pseudo-label Training For End-to-end Speech Recognition Using Gradient Mask
In the recent trend of semi-supervised speech recognition, both self-supervised representation learning and pseudo-labeling have shown promising results.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
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.
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
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.
A fault detection scheme for PV panels in large scale PV stations with complex installation conditions
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.
VideoFlow: A Framework for Building Visual Analysis Pipelines
The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks.
PPG-based singing voice conversion with adversarial representation learning
Singing voice conversion (SVC) aims to convert the voice of one singer to that of other singers while keeping the singing content and melody.
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
The performance of deep learning-based methods strongly relies on the number of datasets used for training.
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation
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
Cannot Predict Comment Volume of a News Article before (a few) Users Read It
We show that the early arrival rate of comments is the best indicator of the eventual number of comments.
Scene-based Factored Attention for Image Captioning
Specifically, the proposed module first embeds the scene concepts into factored weights explicitly and attends the visual information extracted from the input image.
Privacy-Preserving Distributed Parameter Estimation for Probability Distribution of Wind Power Forecast Error
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.
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
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.
Sharp Attention Network via Adaptive Sampling for Person Re-identification
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.
Deep learning and its application to medical image segmentation
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.
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Deep learning-based methods achieved impressive results for the segmentation of medical images.
Convolutional Neural Knowledge Graph Learning
In this paper, we try to learn more complex connections between entities and relationships.
