Search Results for author:
Found 6 papers, 2 papers with code
Group Benefits Instances Selection for Data Purification
Comprehensive experimental results on synthetic and real-world datasets demonstrate the superiority of GRIP over the existing state-of-the-art methods.
AdaNAS: Adaptively Post-processing with Self-supervised Neural Architecture Search for Ensemble Rainfall Forecasts
Additionally, the proposed AdaNAS model is compared with other neural architecture search methods and previous studies.
Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects.
SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
In this paper, we propose a scalable evaluation methodology (SAIH) for analyzing the AI performance trend of HPC systems with scaling the problem sizes of customized AI applications.
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction
Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.
Learning Word Vectors for Sentiment Analysis
We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term–document information as well as rich sentiment content.
