Fetching the paper…
Reading the bibliography…
As emerging hardware begins to support mixed bit-width arithmetic computation, mixed-precision quantization is widely used to reduce the complexity of neural networks.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009
2009
Earlier work this paper cites.
D. D. Lin, S. S. Talathi, and V. S. Annapureddy, “Fixed point quantization of deep convolutional networks,” in ICML , 2016
2016
Earlier work this paper cites.
S. Shin, K. Hwang, and W. Sung, “Fixed-point performance analysis of recurrent neural networks,” in ICASSP , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in CVPR , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Z. G. Liu and M. Mattina, “Learning low-precision neural networks without straight-through estimator (STE),” in IJCAI , S. Kraus, Ed., 2019
2019
Earlier work this paper cites.
R. Banner, Y. Nahshan, and D. Soudry, “Post training 4-bit quantization of convolutional networks for rapid-deployment,” in NeurIPS , H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and R. Garnett, Eds., 2019
2019
Earlier work this paper cites.
Z. Dong, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer, “HAWQ: hessian aware quantization of neural networks with mixed-precision,” in ICCV , 2019
2019
Earlier work this paper cites.
K. Wang, Z. Liu, Y. Lin, J. Lin, and S. Han, “HAQ: hardware-aware automated quantization with mixed precision,” in CVPR , 2019
2019
Earlier work this paper cites.
P. Molchanov, A. Mallya, S. Tyree, I. Frosio, and J. Kautz, “Importance estimation for neural network pruning,” in CVPR , 2019
2019
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in ECCV , 2020
2020
Cited alongside, same era.
B. Zhuang, L. Liu, M. Tan, C. Shen, and I. D. Reid, “Training quantized neural networks with a full-precision auxiliary module,” in CVPR , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
M. Nagel, R. A. Amjad, M. van Baalen, C. Louizos, and T. Blankevoort, “Up or down? adaptive rounding for post-training quantization,” in ICML , 2020
2020
Cited alongside, same era.
I. Hubara, Y. Nahshan, Y. Hanani, R. Banner, and D. Soudry, “Improving post training neural quantization: Layer-wise calibration and integer programming,” CoRR , 2020
S. A. Tailor, J. Fernández-Marqués, and N. D. Lane, “Degree-quant: Quantization-aware training for graph neural networks,” in ICLR , 2021
2021
Later among the works it cites.
P. Stock, A. Fan, B. Graham, E. Grave, R. Gribonval, H. Jégou, and A. Joulin, “Training with quantization noise for extreme model compression,” in ICLR , 2021
2021
Later among the works it cites.
Y. Li, R. Gong, X. Tan, Y. Yang, P. Hu, Q. Zhang, F. Yu, W. Wang, and S. Gu, “BRECQ: pushing the limit of post-training quantization by block reconstruction,” in ICLR , 2021
2021
Later among the works it cites.
Z. Yao, Z. Dong, Z. Zheng, A. Gholami, J. Yu, E. Tan, L. Wang, Q. Huang, Y. Wang, M. W. Mahoney, and K. Keutzer, “HAWQ-V3: dyadic neural network quantization,” in ICML , 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
Z. Dong, Z. Yao, D. Arfeen, A. Gholami, M. W. Mahoney, and K. Keutzer, “HAWQ-V2: hessian aware trace-weighted quantization of neural networks,” in NeurIPS , 2020
2020
Cited alongside, same era.
Q. Lou, F. Guo, M. Kim, L. Liu, and L. Jiang, “Autoq: Automated kernel-wise neural network quantization,” in ICLR , 2020
2020
Cited alongside, same era.
S. Uhlich, L. Mauch, F. Cardinaux, K. Yoshiyama, J. A. García, S. Tiedemann, T. Kemp, and A. Nakamura, “Mixed precision dnns: All you need is a good parametrization,” in ICLR , 2020
2020
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16 × \times 16 words: Transformers for image recognition at scale,” in ICLR , 2021
2021
Cited alongside, same era.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in ICML , 2021
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in ICCV , 2021
2021
Cited alongside, same era.
S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr et al. , “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in CVPR , 2021
2021
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Graham, A. El-Nouby, H. Touvron, P. Stock, A. Joulin, H. Jégou, and M. Douze, “Levit: a vision transformer in convnet’s clothing for faster inference,” in ICCV , 2021
2021
Later among the works it cites.
Z. Liu, Y. Wang, K. Han, W. Zhang, S. Ma, and W. Gao, “Post-training quantization for vision transformer,” 2021
2021
Later among the works it cites.
S. Mehta and M. Rastegari, “Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer,” in ICLR , 2022
2022
Later among the works it cites.
J. Zhang, H. Peng, K. Wu, M. Liu, B. Xiao, J. Fu, and L. Yuan, “Minivit: Compressing vision transformers with weight multiplexing,” in CVPR , 2022
2022
Later among the works it cites.
Y. Xu, Z. Zhang, M. Zhang, K. Sheng, K. Li, W. Dong, L. Zhang, C. Xu, and X. Sun, “Evo-vit: Slow-fast token evolution for dynamic vision transformer,” in AAAI , 2022
2022
Later among the works it cites.