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Diffusion models have emerged as preeminent contenders in the realm of generative models.
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
Zhou, S., Ni, Z., Zhou, X., Wen, H., Wu, Y., and Zou, Y · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
PACT: Parameterized Clipping Activation for Quantized Neural Networks
Choi, J., Wang, Z., Venkataramani, S., Chuang, P. I., Srinivasan, V., and Gopalakrishnan, K · 2018
Earlier work this paper cites.
Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2018
Earlier work this paper cites.
Efficient neural architecture search via parameters sharing
Pham, H., Guan, M., Zoph, B., Le, Q., and Dean, J · 2018
Earlier work this paper cites.
HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-precision
Dong, Z., Yao, Z., Gholami, A., Mahoney, M. W., and Keutzer, K · 2019
Earlier work this paper cites.
Searching for MobileNetV3
Howard, A., Pang, R., Adam, H., Le, Q. V., Sandler, M., Chen, B., Wang, W., Chen, L., Tan, M., Chu, G., Vasudevan, V., and Zhu, Y · 2019
Earlier work this paper cites.
Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks
Li, Y., Dong, X., and Wang, W · 2019
Earlier work this paper cites.
Importance estimation for neural network pruning
Molchanov, P., Mallya, A., Tyree, S., Frosio, I., and Kautz, J · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Earlier work this paper cites.
HAQ: Hardware-aware Automated Quantization With Mixed Precision
Wang, K., Liu, Z., Lin, Y., Lin, J., and Han, S · 2019
Earlier work this paper cites.
Rethinking Differentiable Search for Mixed-precision Neural Networks
Cai, Z. and Vasconcelos, N · 2020
Earlier work this paper cites.
HAWQ-V2: Hessian Aware trace-weighted Quantization of Neural Networks
Dong, Z., Yao, Z., Arfeen, D., Gholami, A., Mahoney, M. W., and Keutzer, K · 2020
Earlier work this paper cites.
ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks
Elthakeb, A. T., Pilligundla, P., Mireshghallah, F., Yazdanbakhsh, A., and Esmaeilzadeh, H · 2020
Cited alongside, same era.
Learned Step Size quantization
Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., and Modha, D. S · 2020
Cited alongside, same era.
Single Path One-shot Neural Architecture Search with Uniform Sampling
Guo, Z., Zhang, X., Mu, H., Heng, W., Liu, Z., Wei, Y., and Sun, J · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Up or down? adaptive rounding for post-training quantization
Nagel, M., Amjad, R. A., Van Baalen, M., Louizos, C., and Blankevoort, T · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, J · 2022
Later among the works it cites.
Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
Later among the works it cites.
Dreamdiffusion: Generating high-quality images from brain eeg signals
Bai, Y., Wang, X., Cao, Y.-p., Ge, Y., Yuan, C., and Shan, Y · 2023
Later among the works it cites.
One transformer fits all distributions in multi-modal diffusion at scale
Bao, F., Nie, S., Xue, K., Li, C., Pu, S., Wang, Y., Yue, G., Cao, Y., Su, H., and Zhu, J · 2023
Later among the works it cites.
Diffusiondet: Diffusion model for object detection
Chen, S., Sun, P., Song, Y., and Luo, P · 2023
Later among the works it cites.
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Towards Mixed-precision Quantization of Neural Networks via Constrained Optimization
Chen, W., Wang, P., and Cheng, J · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Accurate post training quantization with small calibration sets
Hubara, I., Nahshan, Y., Hanani, Y., Banner, R., and Soudry, D · 2021
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models, 2021
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2021
Cited alongside, same era.
An adaptive logarithm quantization method for dnn compression
Wang, Y., He, Z., Tang, C., Wang, Z., and Zhu, W · 2021
Cited alongside, same era.
Learning fast samplers for diffusion models by differentiating through sample quality
Watson, D., Chan, W., Ho, J., and Norouzi, M · 2021
Cited alongside, same era.
Diffusion action segmentation
Liu, D., Li, Q., Dinh, A.-D., Jiang, T., Shah, M., and Xu, C · 2023
Later among the works it cites.
Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
Later among the works it cites.
Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
Later among the works it cites.
Cads: Unleashing the diversity of diffusion models through condition-annealed sampling
Sadat, S., Buhmann, J., Bradley, D., Hilliges, O., and Weber, R. M · 2023
Later among the works it cites.
Post-training quantization on diffusion models
Shang, Y., Yuan, Z., Xie, B., Wu, B., and Yan, Y · 2023
Later among the works it cites.
Towards accurate data-free quantization for diffusion models
Wang, C., Wang, Z., Xu, X., Tang, Y., Zhou, J., and Lu, J · 2023
Later among the works it cites.
Structural pruning for diffusion models
Fang, G., Ma, X., and Wang, X · 2024
Closest in time.
Ptqd: Accurate post-training quantization for diffusion models
He, Y., Liu, L., Liu, J., Wu, W., Zhou, H., and Zhuang, B · 2024
Closest in time.
Retraining-free model quantization via one-shot weight-coupling learning
Tang, C., Meng, Y., Jiang, J., Xie, S., Lu, R., Ma, X., Wang, Z., and Zhu, W · 2024
Closest in time.