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D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013
Original
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” arXiv preprint arXiv:1406.2661 , 2014
Original
2014
Earlier work this paper cites.
O. Bojar, C. Buck, C. Federmann, B. Haddow, P. Koehn, J. Leveling, C. Monz, P. Pecina, M. Post, H. Saint-Amand et al. , “Findings of the 2014 workshop on statistical machine translation,” in Proceedings of the ninth workshop on statistical machine translation , 2014, pp. 12–58
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” arXiv preprint arXiv:1807.03039 , 2018
Original
2018
Earlier work this paper cites.

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C. Donahue, J. McAuley, and M. Puckette, “Adversarial audio synthesis,” arXiv preprint arXiv:1802.04208 , 2018
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M. Post, “A call for clarity in reporting BLEU scores,” in Proceedings of the Third Conference on Machine Translation: Research Papers . Belgium, Brussels: Association for Computational Linguistics, Oct. 2018, pp. 186–191. [Online]. Available: https://www.aclweb.org/anthology/W18-6319
2018
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
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2018
Cited alongside, same era.
A. Razavi, A. v. d. Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” arXiv preprint arXiv:1906.00446 , 2019
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2019
Cited alongside, same era.

Then

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8110–8119
2020
Later among the works it cites.
A. Vahdat and J. Kautz, “Nvae: A deep hierarchical variational autoencoder,” arXiv preprint arXiv:2007.03898 , 2020
Original
2020
Later among the works it cites.
N. Chen, Y. Zhang, H. Zen, R. J. Weiss, M. Norouzi, and W. Chan, “Wavegrad: Estimating gradients for waveform generation,” arXiv preprint arXiv:2009.00713 , 2020
Original
2020
Later among the works it cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” arXiv preprint arXiv:2105.05233 , 2021
Original
2021
Closest in time.

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2021

Zero-Shot Translation using Diffusion Models

Nachmani, Eliya, Dovrat, Shaked

Understand

In this work, we show a novel method for neural machine translation (NMT), using a denoising diffusion probabilistic model (DDPM), adjusted for textual data, following recent advances in the field.

We show that it's possible to translate sentences non-autoregressively using a diffusion model conditioned on the source sentence.
We also show that our model is able to translate between pairs of languages unseen during training (zero-shot learning).

Built on

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013
Original
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” arXiv preprint arXiv:1406.2661 , 2014
Original
2014
Earlier work this paper cites.
O. Bojar, C. Buck, C. Federmann, B. Haddow, P. Koehn, J. Leveling, C. Monz, P. Pecina, M. Post, H. Saint-Amand et al. , “Findings of the 2014 workshop on statistical machine translation,” in Proceedings of the ninth workshop on statistical machine translation , 2014, pp. 12–58
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” arXiv preprint arXiv:1807.03039 , 2018
Original
2018
Earlier work this paper cites.

Similar

C. Donahue, J. McAuley, and M. Puckette, “Adversarial audio synthesis,” arXiv preprint arXiv:1802.04208 , 2018
Original
2018
Cited alongside, same era.
M. Post, “A call for clarity in reporting BLEU scores,” in Proceedings of the Third Conference on Machine Translation: Research Papers . Belgium, Brussels: Association for Computational Linguistics, Oct. 2018, pp. 186–191. [Online]. Available: https://www.aclweb.org/anthology/W18-6319
2018
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Cited alongside, same era.
A. Razavi, A. v. d. Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” arXiv preprint arXiv:1906.00446 , 2019
Original
2019
Cited alongside, same era.

Then

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8110–8119
2020
Later among the works it cites.
A. Vahdat and J. Kautz, “Nvae: A deep hierarchical variational autoencoder,” arXiv preprint arXiv:2007.03898 , 2020
Original
2020
Later among the works it cites.
N. Chen, Y. Zhang, H. Zen, R. J. Weiss, M. Norouzi, and W. Chan, “Wavegrad: Estimating gradients for waveform generation,” arXiv preprint arXiv:2009.00713 , 2020
Original
2020
Later among the works it cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” arXiv preprint arXiv:2105.05233 , 2021
Original
2021
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

L. Barrault, O. Bojar, M. R. Costa-Jussa, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, P. Koehn, S. Malmasi et al. , “Findings of the 2019 conference on machine translation (wmt19),” in Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1) , 2019, pp. 1–61

2019

Cited alongside, same era.

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” arXiv preprint arXiv:2006.11239 , 2020

Original

2020

Cited alongside, same era.

E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling, “Argmax flows and multinomial diffusion: Towards non-autoregressive language models,” arXiv preprint arXiv:2102.05379 , 2021

Original

2021

Closest in time.

J. Austin, D. Johnson, J. Ho, D. Tarlow, and R. v. d. Berg, “Structured denoising diffusion models in discrete state-spaces,” arXiv preprint arXiv:2107.03006 , 2021

Original

2021

Closest in time.