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Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing
Rui Yu, Yifeng Li, Wenpeng Lu +1
In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historic…
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning
Qi Zhang, Liang Hu, Chongyang Shi +2
Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is a trending yet challenging and computationally expensive task in the real world. A promising approach is to…
Dual Representation Learning for Out-of-Distribution Detection
Zhilin Zhao, Longbing Cao
To classify in-distribution samples, deep neural networks explore strongly label-related information and discard weakly label-related information according to the information bottl…
Out-of-distribution Detection by Cross-class Vicinity Distribution of In-distribution Data
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
Deep neural networks for image classification only learn to map in-distribution inputs to their corresponding ground truth labels in training without differentiating out-of-distrib…
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
The discrepancy between in-distribution (ID) and out-of-distribution (OOD) samples can lead to \textit{distributional vulnerability} in deep neural networks, which can subsequently…
Gray Learning from Non-IID Data with Out-of-distribution Samples
Zhilin Zhao, Longbing Cao, Chang-Dong Wang
The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an i…