[
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning (PMLR), 70, 214-223.
]Search in Google Scholar
[
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. Virtual Event. Canada.
]Search in Google Scholar
[
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.
]Search in Google Scholar
[
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33.
]Search in Google Scholar
[
Brunner, G., Konrad, A., Wang, Y., & Wattenhofer, R. (2018). MIDI-VAE: Modeling dynamics and instrumentation of music with applications to style transfer. 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 747–754. Paris, France.
]Search in Google Scholar
[
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. https://doi.org/10.48550/arXiv.2303.12712
]Search in Google Scholar
[
Chesney, R., & Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107, 1753.
]Search in Google Scholar
[
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30.
]Search in Google Scholar
[
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171–4186. Minneapolis, Minnesota. Association for Computational Linguistics.
]Search in Google Scholar
[
Donahue, C., McAuley, J., & Puckette, M. (2018). Adversarial audio synthesis. https://doi.org/10.48550/arXiv.1802.04208
]Search in Google Scholar
[
Engel, J., Resnick, C., Roberts, A., Dieleman, S., Norouzi, M., Eck, D., & Simonyan, K. (2017). Neural audio synthesis of musical notes with WaveNet autoencoders. Proceedings of the 34th International Conference on Machine Learning (PMLR), 70, 1068–1077.
]Search in Google Scholar
[
Feng, Y. (2022). The rise of virtual image endorsement in visual culture context. 4th International Conference on Economic Management and Cultural Industry (ICEMCI), pp. 1622–1629. Atlantis Press.
]Search in Google Scholar
[
Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2017). Word embeddings quantify 100 years of gender and ethnic stereotypes. https://doi.org/10.1073/pnas.1720347115
]Search in Google Scholar
[
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423. Las Vegas. U.S.A.
]Search in Google Scholar
[
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
]Search in Google Scholar
[
IFR. (2023). International Federation of Robotics. https://ifr.org/worldrobotics/
]Search in Google Scholar
[
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2018). Progressive growing of GANs for improved quality, stability, and variation. 6th International Conference on Learning Representations (ICLR). Vancouver. Canada.
]Search in Google Scholar
[
Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4396–4405. Long Beach, USA. https://doi.org/10.1109/CVPR.2019.00453
]Search in Google Scholar
[
Kingma, D.P., & Welling, M. (2014). Auto-encoding variational Bayes. https://doi.org/10.48550/arXiv.1312.6114
]Search in Google Scholar
[
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
]Search in Google Scholar
[
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. https://doi.org/10.48550/arXiv.2304.02819
]Search in Google Scholar
[
Liu, Y., Han, T., Ma, S. Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., Wu, Z., Zhu, D., Li, X., Qiang, N., Shen, D., Liu, T., & Ge, B. (2023). Summary of ChatGPT/GPT-4 research and perspective towards the future of large language models. https://doi.org/10.48550/arXiv.2304.01852
]Search in Google Scholar
[
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1906–1919.
]Search in Google Scholar
[
Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. (2017). Medical image synthesis with context-aware generative adversarial networks. Medical Image Computing and Computer Assisted Intervention−MICCAI 2017: 20th International Conference, pp. 417–425. Quebec, Canada.
]Search in Google Scholar
[
OpenAI. (2023). GPT-4 Technical Report. https://doi.org/10.48550/arXiv.2303.08774
]Search in Google Scholar
[
Oord van den, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: A generative model for raw audio. https://doi.org/10.48550/arXiv.1609.03499
]Search in Google Scholar
[
Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. 4th International Conference on Learning Representations (ICLR). San Juan. Puerto Rico.
]Search in Google Scholar
[
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1−67.
]Search in Google Scholar
[
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. Florence. Italy.
]Search in Google Scholar
[
Vaswani, A., Shazeer. N., Parmar N., Uszkoreit, J., Jones, J., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
]Search in Google Scholar
[
Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks for Visual Recognition, 11, 1–8.
]Search in Google Scholar
[
Zhao, J., Mathieu, M., & LeCun, Y. (2017). Energy-based generative adversarial networks. 5th International Conference on Learning Representations (ICLR). Toulon. France.
]Search in Google Scholar
[
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242–2251. Venice. Italy.
]Search in Google Scholar