Results 11 to 20 of about 83,749 (307)
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
doaj +1 more source
CoRR, 2022 Recent progress in Generative Artificial Intelligence (AI) relies on efficient data representations, often featuring encoder-decoder architectures. We formalize the mathematical problem of finding the optimal encoder-decoder pair and characterize its solution, which we name the "benign autoencoder" (BAE). We prove that BAE projects data onto a manifold
Semyon Malamud +4 more
openaire +2 more sources
On the Regularization of Autoencoders
CoRR, 2021 While much work has been devoted to understanding the implicit (and explicit) regularization of deep nonlinear networks in the supervised setting, this paper focuses on unsupervised learning, i.e., autoencoders are trained with the objective of reproducing the output from the input. We extend recent results [Jin et al.
Harald Steck, Dario García-García
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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
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
Autoencoding Variational Autoencoder
CoRR, 2020 Neurips ...
A. Taylan Cemgil +4 more
openaire +2 more sources
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
doaj +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
Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway
Applied Sciences, 2020 Recently, novelty detection with reconstruction along projection pathway (RaPP) has made progress toward leveraging hidden activation values. RaPP compares the input and its autoencoder reconstruction in hidden spaces to detect novelty samples ...
Seung Yeop Shin, Han-joon Kim
doaj +1 more source