Results 11 to 20 of about 75,818 (178)
Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder
Frontiers in Genetics, 2022 The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics.
Yishuai Geng +3 more
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RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data
Journal of Biological Engineering, 2023 Background In the current genomic era, gene expression datasets have become one of the main tools utilized in cancer classification. Both curse of dimensionality and class imbalance problems are inherent characteristics of these datasets.
Ahmed Arafa +3 more
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Autoencoder untuk Sistem Prediksi Berat Lahir Bayi
Jurnal Teknologi Informasi dan Ilmu Komputer, 2022 Salah satu ukuran terpenting saat awal persalinan adalah keakuratan prediksi berat lahir. Dengan menggunakan metode prediksi yang tepat, perkiraan ekstrim berat lahir bayi dapat dideteksi lebih atau kurang sehingga beberapa tindakan pencegahan dapat ...
Fitra Septia Nugraha +1 more
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Graph Masked Autoencoder for Sequential Recommendation [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in ...
Yaowen Ye, Lianghao Xia, Chao Huang
semanticscholar +1 more source
Age Progression/Regression by Conditional Adversarial Autoencoder [PDF]
Computer Vision and Pattern Recognition, 2017 If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look
Zhifei Zhang, Yang Song, H. Qi
semanticscholar +1 more source
Learning two-phase microstructure evolution using neural operators and autoencoder architectures [PDF]
npj Computational Materials, 2022 Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials.
Vivek Oommen +4 more
semanticscholar +1 more source
Bearing Vibration Abnormal Detection Based on Improved Autoencoder Network [PDF]
Jisuanji kexue yu tansuo, 2022 In recent years, autoencoders and neural network technologies have been widely studied and applied to abnormal data detection problems of industrial data such as bearing vibration, but there are still problems such as large training data, network ...
LI Beibei, PENG Li
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Denoising Adversarial Autoencoders [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space.
Antonia Creswell, Anil Anthony Bharath
openaire +5 more sources
Contrastive Audio-Visual Masked Autoencoder [PDF]
International Conference on Learning Representations, 2022 In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and ...
Yuan Gong +6 more
semanticscholar +1 more source
Anomaly Detection for Agricultural Vehicles Using Autoencoders
Sensors, 2022 The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this
Esma Mujkic +4 more
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