[1] M. Alizadeh, A. Behboodi, M. van Baalen, C. Louizos, T. Blankevoort, and M. Welling. "Gradient L1 Regularization for Quantization Robustness". arXiv e-prints. 2020.
[2] Y. Bai, Y.-X. Wang, and E. Liberty. "ProxQuant: Quantized Neural Networks via Proximal Operators". In: International Conference on Learning Representations. 2018.
[3] R. Banner, Y. Nahshan, and D. Soudry. "Post training 4-bit quantization of convolutional networks for rapid-deployment". In: Advances in Neural Information Processing Systems. 2019, pp. 7948-7956.
[4] Y. Bengio, N. Léonard, and A. Courville. "Estimating or propagating gradients through stochastic neurons for conditional computation". arXiv preprint arXiv:1308.3432. 2013.
[5] Y. Bhalgat, J. Lee, M. Nagel, T. Blankevoort, and N. Kwak. "LSQ+: Improving low-bit quantization through learnable offsets and better initialization". In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 2020, pp. 2978-2985.
[6] S. Bhojanapalli, A. Chakrabarti, A. Veit, M. Lukasik, H. Jain, F. Liu, Y.-W. Chang, and S. Kumar. "Leveraging redundancy in attention with Reuse Transformers". arXiv preprint arXiv:2110.06821. 2021.
[7] S. Biderman et al. "Pythia: A suite for analyzing large language models across training and scaling". arXiv preprint arXiv:2304.01373 (2023).
[8] T. Brown et al. "Language models are few-shot learners". Advances in neural information processing systems, vol. 33 (2020), pp. 1877-1901.
[9] Y. Cai, Z. Yao, Z. Dong, A. Gholami, M. W. Mahoney, and K. Keutzer. “Zeroq: A novel zero shot quantization framework”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, pp. 13169–13178.
[10] J. Choi, Z. Wang, S. Venkataramani, P. I.-J. Chuang, V. Srinivasan, and K. Gopalakrishnan. "Pact: Parameterized clipping activation for quantized neural networks". arXiv preprint arXiv:1805.06085. 2018.
[11] M. Courbariaux, Y. Bengio, and J.-P. David. "Binaryconnect: Training deep neural networks with binary weights during propagations". In: Advances in Neural Information Processing Systems. 2015.
[12] S. Darabi, M. Belbahri, M. Courbariaux, and V. P. Nia. "Regularized binary network training". In: NeurIPS Workshop on Energy Efficient Machine Learning and Cognitive Computing. 2019.
[13] T. Dockhorn, Y. Yu, E. Sari, M. Zolnouri, and V. Partovi Nia. "Demystifying and Generalizing BinaryConnect". In: Advances in Neural Information Processing Systems. 2021, pp. 13202-13216.
[14] A. Dosovitskiy et al. "An image is worth 16x16 words: Transformers for image recognition at scale". In: International Conference on Learning Representations. 2021.
[15] S. K. Esser, J. L. McKinstry, D. Bablani, R. Appuswamy, and D. S. Modha. "Learned step size quantization". arXiv preprint arXiv:1902.08153. 2019.
[16] A. Ghaffari, M. S. Tahaei, M. Tayaranian, M. Asgharian, and V. P. Nia. "Is Integer Arithmetic Enough for Deep Learning Training?" In: Advances in Neural Information Processing Systems. 2022.
[17] S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan. "Deep Learning with Limited Numerical Precision". In: Proceedings of the 32nd International Conference on Machine Learning (ICML). 2015, pp. 1737-1746.