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Adversarially-Trained Normalized Noisy-Feature Auto ...
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由 X Zhang 著作2018被引用 1 次 — Abstract:This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-Encoder (ATNNFAE) for byte-level text generation.
feature auto-encoder for text generation
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由 X Zhang 著作2018被引用 1 次 — This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-. Encoder (ATNNFAE) for byte-level text generation.
Adversarially-Trained Normalized Noisy-Feature Auto ...
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2018年11月10日 — Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation · Xiang Zhang, Yann LeCun · Published in arXiv.org 10 November ...
Xiang Zhang | ServiceNow Research
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Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation. Xiang Zhang, Yann LeCun. At ArXiv, 2018.
Text Generation | ServiceNow Research
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This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-Encoder (ATNNFAE) for byte-level text generation. An ATNNFAE … Xiang Zhang, Yann LeCun.
Yann Lecun
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An ATNNFAE consists of an auto-encoder where the internal code is normalized on the unit sphere and corrupted by additive noise. Decoder · Text Generation.
Adversarially Regularized Autoencoders
Proceedings of Machine Learning Research
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由 JJ Zhao 著作被引用 371 次 — In both our ARAE and standard AE experiments, the encoder output is normalized to lie on the unit sphere, and the generator output is bounded to lie in. (−1, 1) ...
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ADVERSARIALLY REGULARIZED AUTOENCODERS
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由 JJ Zhao 著作2018被引用 371 次 — ARAE jointly trains both a rich discrete-space encoder, such as an RNN, and a simpler continuous space generator function, while using generative adversarial ...
Adversarially Regularized Autoencoders for Generating ...
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Generative adversarial networks are an effective approach for learning rich latent representations of continuous data, but have proven difficult to apply ...
A deep learning adversarial autoencoder with dynamic ...
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由 KD Ko 著作2024被引用 2 次 — We propose a deep neural generative framework, the dynamic batching adversarial autoencoder (DB-AAE), which excels at denoising scRNA-seq datasets.