Model compression as constrained optimization, with application to neural nets. Part II: quantization. - Miguel Á. Carreira-Perpiñán; Yerlan Idelbayev (CoRR, 2017)

"Learning-Compression" Algorithms for Neural Net Pruning. - Miguel Á. Carreira-Perpiñán; Yerlan Idelbayev (CVPR, 2018)

Structured Multi-Hashing for Model Compression. - Elad Eban; Yair Movshovitz-Attias; Hao Wu; Mark Sandler 002; Andrew Poon; Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (CVPR, 2020)

Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (CVPR, 2020)

A flexible, extensible software framework for model compression based on the LC algorithm. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (CoRR, 2020)

LC: A Flexible, Extensible Open-Source Toolkit for Model Compression. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (CIKM, 2021)

Optimal Quantization Using Scaled Codebook. - Yerlan Idelbayev; Pavlo Molchanov 001; Maying Shen; Hongxu Yin; Miguel Á. Carreira-Perpiñán; José M. Álvarez 004 (CVPR, 2021)

Neural Network Compression via Additive Combination of Reshaped, Low-Rank Matrices. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (DCC, 2021)

Optimal Selection of Matrix Shape and Decomposition Scheme for Neural Network Compression. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (ICASSP, 2021)

Beyond Flops In Low-Rank Compression Of Neural Networks: Optimizing Device-Specific Inference Runtime. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (ICIP, 2021)

An Empirical Comparison of Quantization, Pruning and Low-rank Neural Network Compression using the LC Toolkit. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (IJCNN, 2021)

More General and Effective Model Compression via an Additive Combination of Compressions. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (ECML/PKDD (3), 2021)

Model compression as constrained optimization, with application to neural nets. Part V: combining compressions. - Miguel Á. Carreira-Perpiñán; Yerlan Idelbayev (CoRR, 2021)

Low-rank Compression of Neural Networks: LC Algorithms and Open-source Implementation. - Yerlan Idelbayev (2021)

Exploring the Effect of ℓ0/ℓ2 Regularization in Neural Network Pruning using the LC Toolkit. - Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (ICASSP, 2022)

TextCraftor: Your Text Encoder can be Image Quality Controller. - Yanyu Li; Xian Liu; Anil Kag; Ju Hu; Yerlan Idelbayev; Dhritiman Sagar; Yanzhi Wang; Sergey Tulyakov; Jian Ren 005 (CVPR, 2024)

Efficient Training with Denoised Neural Weights. - Yifan Gong 004; Zheng Zhan 001; Yanyu Li; Yerlan Idelbayev; Andrey Zharkov; Kfir Aberman; Sergey Tulyakov; Yanzhi Wang; Jian Ren 003 (ECCV (83), 2024)

E2GAN: Efficient Training of Efficient GANs for Image-to-Image Translation. - Yifan Gong 004; Zheng Zhan 001; Qing Jin; Yanyu Li; Yerlan Idelbayev; Xian Liu; Andrey Zharkov; Kfir Aberman; Sergey Tulyakov; Yanzhi Wang; Jian Ren 005 (ICML, 2024)

BitsFusion: 1.99 bits Weight Quantization of Diffusion Model. - Yang Sui; Yanyu Li; Anil Kag; Yerlan Idelbayev; Junli Cao; Ju Hu; Dhritiman Sagar; Bo Yuan 001; Sergey Tulyakov; Jian Ren 005 (NeurIPS, 2024)

E2GAN: Efficient Training of Efficient GANs for Image-to-Image Translation. - Yifan Gong 004; Zheng Zhan 001; Qing Jin; Yanyu Li; Yerlan Idelbayev; Xian Liu; Andrey Zharkov; Kfir Aberman; Sergey Tulyakov; Yanzhi Wang; Jian Ren 005 (CoRR, 2024)

Efficient Training with Denoised Neural Weights. - Yifan Gong 004; Zheng Zhan 001; Yanyu Li; Yerlan Idelbayev; Andrey Zharkov; Kfir Aberman; Sergey Tulyakov; Yanzhi Wang; Jian Ren 005 (CoRR, 2024)

SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training. - Jierun Chen; Dongting Hu; Xijie Huang; Huseyin Coskun; Arpit Sahni; Aarush Gupta; Anujraaj Goyal; Dishani Lahiri; Rajesh Singh; Yerlan Idelbayev; Junli Cao; Yanyu Li; et al. (CVPR, 2025)