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Robust multi-task feature learning [PDF]
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012 Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning algorithms have received increasing attention and they have been successfully applied to many applications involving high-dimensional data.
Pinghua, Gong +2 more
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IEEE Access, 2019 In recent years, semantic segmentation methods based on fully convolutional networks (FCNs) have achieved great success. However, these methods tend to produce inconsistency and isolated class labels, mainly because the end-to-end mapping of FCN ...
Zhengeng Yang +4 more
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GraRep++: Flexible Learning Graph Representations With Weighted Global Structural Information
IEEE Access, 2023 The key to vertex embedding is to learn low-dimensional representations of global graph information, and integrating information from multiple steps is an effective strategy.
Mengcen Ouyang +3 more
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Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
Information, 2019 Automatic Classification of Wireless Signals (ACWS), which is an intermediate step between signal detection and demodulation, is investigated in this paper.
Arfan Haider Wahla +3 more
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Remote Sensing, 2018 Detailed information about built-up areas is valuable for mapping complex urban environments. Although a large number of classification algorithms for such areas have been developed, they are rarely tested from the perspective of feature engineering and ...
Tao Zhang, Hong Tang
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Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment
Sensors, 2022 CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same.
Xiaoxun Zhu +4 more
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Multimodal Sparse Coding for Event Detection [PDF]
, 2016 Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities.
Brady, Kevin +5 more
core
Attention-based Wav2Text with Feature Transfer Learning
, 2017 Conventional automatic speech recognition (ASR) typically performs multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units.
Nakamura, Satoshi +2 more
core +1 more source
Learning Feature Pyramids for Human Pose Estimation
, 2017 Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens.
Li, Hongsheng +4 more
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, 2014 A key element in transfer learning is representation learning; if representations can be developed that expose the relevant factors underlying the data, then new tasks and domains can be learned readily based on mappings of these salient factors. We propose that an important aim for these representations are to be unbiased.
Li, Yujia +2 more
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