Yerlan Idelbayev

According to our database, Yerlan Idelbayev authored at least 17 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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

CoRR, 2024

E²GAN: Efficient Training of Efficient GANs for Image-to-Image Translation.

Yifan Gong, Zheng Zhan, Qing Jin, Yanyu Li, Yerlan Idelbayev, Xian Liu, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren

CoRR, 2024

2022

Exploring the Effect of ℓ0/ℓ2 Regularization in Neural Network Pruning using the LC Toolkit.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Low-rank Compression of Neural Networks: LC Algorithms and Open-source Implementation.

Yerlan Idelbayev

PhD thesis, 2021

Model compression as constrained optimization, with application to neural nets. Part V: combining compressions.

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev

CoRR, 2021

More General and Effective Model Compression via an Additive Combination of Compressions.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

An Empirical Comparison of Quantization, Pruning and Low-rank Neural Network Compression using the LC Toolkit.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the International Joint Conference on Neural Networks, 2021

Beyond Flops In Low-Rank Compression Of Neural Networks: Optimizing Device-Specific Inference Runtime.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

Optimal Selection of Matrix Shape and Decomposition Scheme for Neural Network Compression.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the IEEE International Conference on Acoustics, 2021

Neural Network Compression via Additive Combination of Reshaped, Low-Rank Matrices.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the 31st Data Compression Conference, 2021

Optimal Quantization Using Scaled Codebook.

Yerlan Idelbayev, Pavlo Molchanov, Maying Shen, Hongxu Yin, Miguel Á. Carreira-Perpiñán, José M. Álvarez

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

LC: A Flexible, Extensible Open-Source Toolkit for Model Compression.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

A flexible, extensible software framework for model compression based on the LC algorithm.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

CoRR, 2020

Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer.

Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Structured Multi-Hashing for Model Compression.

Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2018

"Learning-Compression" Algorithms for Neural Net Pruning.

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Model compression as constrained optimization, with application to neural nets. Part II: quantization.

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev

CoRR, 2017