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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties Conneau, Alexis; Kruszewski, German; Lample, Guillaume... In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01898412 ; ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.2126-2136 (2018) BASE Show details 2 XNLI: Evaluating Cross-lingual Sentence Representations ... Conneau, Alexis; Lample, Guillaume; Rinott, Ruty. - : arXiv, 2018 BASE Show details 3 What you can cram into a single vector: Probing sentence embeddings for linguistic properties ... Conneau, Alexis; Kruszewski, German; Lample, Guillaume. - : arXiv, 2018 BASE Show details 4 Fader Networks: Manipulating Images by Sliding Attributes Lample, Guillaume; Zeghidour, Neil; Usunier, Nicolas; Bordes, Antoine; Denoyer, Ludovic; Ranzato, Marc In: 31st Conference on Neural Information Processing Systems (NIPS 2017) ; https://hal.archives-ouvertes.fr/hal-02275215 ; 31st Conference on Neural Information Processing Systems (NIPS 2017), Dec 2017, Long Beach, CA, United States. pp.5969-5978 (2017) Abstract: International audience ; This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images. Keyword: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] URL: https://hal.archives-ouvertes.fr/hal-02275215/file/1706.00409.pdf https://hal.archives-ouvertes.fr/hal-02275215 https://hal.archives-ouvertes.fr/hal-02275215/document BASE Hide details 5 Word Translation Without Parallel Data ... Conneau, Alexis; Lample, Guillaume; Ranzato, Marc'Aurelio. - : arXiv, 2017 BASE Show details 6 Massively Multilingual Word Embeddings ... Ammar, Waleed; Mulcaire, George; Tsvetkov, Yulia. - : arXiv, 2016 BASE Show details 7 Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ... Tsvetkov, Yulia; Sitaram, Sunayana; Faruqui, Manaal. - : arXiv, 2016 BASE Show details 8 What you can cram into a single $&!#* vector: probing sentence embeddings for linguistic properties Kruszewski, German; Barrault, Loïc; Baroni, Marco. - : ACL (Association for Computational Linguistics) BASE Show details 9 Neural architectures for named entity recognition Kawakami, Kazuya; Ballesteros, Miguel; Lample, Guillaume. - : ACL (Association for Computational Linguistics) BASE Show details

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