53 citations · 83 across the 8 of their papers we have counts for
8 papers
Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
Haoliang Wang, Chen Zhao, Xujiang Zhao +1
During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels. While features at different layers cou…
SEED: Sound Event Early Detection via Evidential Uncertainty
Xujiang Zhao, Xuchao Zhang, Wei Cheng +4
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes. However, most of the existing methods focus on the offline sound e…
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
Liyan Xu, Xuchao Zhang, Xujiang Zhao +3
Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on t…
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Krishnateja Killamsetty, Xujiang Zhao, Feng Chen +1
Semi-supervised learning (SSL) algorithms have had great success in recent years in limited labeled data regimes. However, the current state-of-the-art SSL algorithms are computati…
Multidimensional Uncertainty-Aware Evidential Neural Networks
Yibo Hu, Yuzhe Ou, Xujiang Zhao +2
Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However,…
Uncertainty Aware Semi-Supervised Learning on Graph Data
Xujiang Zhao, Feng Chen, Shu Hu +1
Thanks to graph neural networks (GNNs), semi-supervised node classification has shown the state-of-the-art performance in graph data. However, GNNs have not considered different ty…
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
Xujiang Zhao, Yuzhe Ou, Lance Kaplan +2
Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of…
Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data
Xujiang Zhao, Feng Chen, Jin-Hee Cho
Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing mult…