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Autoencoders
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Autoencoders
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An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation, and then decode it back such that the reconstructed input is similar as possible to the original one. This chapter surveys the different types of autoencoders that are mainly used today. It also describes various applications and use-cases of autoencoders.
Forward citations
Cited by 9 Pith papers
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Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders
Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
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Quantum Masked Autoencoders for Vision Learning
Quantum masked autoencoders reconstruct masked MNIST-family images in quantum states and achieve 12.86% higher average classification accuracy than prior quantum autoencoders under masking.
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Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training
TART improves clean accuracy in adversarial training by modulating perturbation bounds according to the tangential component of adversarial examples.
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Layer-Wise Decision Fusion for Fake Audio Detection Using XLS-R
Per-layer late fusion of one-class softmax classifiers on frozen XLS-R features achieves 6.90% EER on In-the-Wild fake-audio detection, outperforming feature-fusion and single-layer baselines.
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BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks
An active-inference xApp with a hand-coded generative model outperforms DRL baselines on dynamic radio resource slicing and provides belief-based explanations of its decisions.
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The contribution of the color space in LSST-like photometry for the selection of extragalactic globular cluster candidates
With ugrizY colors alone, an LSST-like catalog yields at best ~35% contamination and ~19% completeness for globular-cluster candidates; principal components help marginally, autoencoders do not.
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Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neu...
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OCTANE -- Optimal Control for Tensor-based Autoencoder Network Emergence: Explicit Case
Autoencoders as continuous-time optimal control problems solved with rank-adaptive tensor compression, yielding memory savings and automatic layer-width profiles on MNIST denoising and deblurring.
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GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU
VAE models quantized for Edge TPUs deliver over 42x compression of GNSS signals with F2-score 0.915 for classifying 72 interference types, nearly matching uncompressed performance of 0.923.
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