Yerlan Idelbayev

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 19

Top Topics

Matrix Decomposition

Constrained Optimization

Top Venues

Publications

Yang Sui, Yanyu Li, Anil Kag, Yerlan Idelbayev, Junli Cao, Ju Hu, Dhritiman Sagar, Bo Yuan, Sergey Tulyakov, Jian Ren
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model. CoRR (2024)
Yanyu Li, Xian Liu, Anil Kag, Ju Hu, Yerlan Idelbayev, Dhritiman Sagar, Yanzhi Wang, Sergey Tulyakov, Jian Ren
TextCraftor: Your Text Encoder Can be Image Quality Controller. CoRR (2024)
Yifan Gong, Zheng Zhan, Qing Jin, Yanyu Li, Yerlan Idelbayev, Xian Liu, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren
GAN: Efficient Training of Efficient GANs for Image-to-Image Translation. CoRR (2024)
Yifan Gong, Zheng Zhan, Yanyu Li, Yerlan Idelbayev, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren
Efficient Training with Denoised Neural Weights. CoRR (2024)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Exploring the Effect of ℓ0/ℓ2 Regularization in Neural Network Pruning using the LC Toolkit. ICASSP (2022)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Neural Network Compression via Additive Combination of Reshaped, Low-Rank Matrices. DCC (2021)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Beyond Flops In Low-Rank Compression Of Neural Networks: Optimizing Device-Specific Inference Runtime. ICIP (2021)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
An Empirical Comparison of Quantization, Pruning and Low-rank Neural Network Compression using the LC Toolkit. IJCNN (2021)
Yerlan Idelbayev, Pavlo Molchanov, Maying Shen, Hongxu Yin, Miguel Á. Carreira-Perpiñán, Jose M. Alvarez
Optimal Quantization Using Scaled Codebook. CVPR (2021)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Optimal Selection of Matrix Shape and Decomposition Scheme for Neural Network Compression. ICASSP (2021)
Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions. CoRR (2021)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
More General and Effective Model Compression via an Additive Combination of Compressions. ECML/PKDD (3) (2021)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
LC: A Flexible, Extensible Open-Source Toolkit for Model Compression. CIKM (2021)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer. CVPR (2020)
Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Structured Multi-Hashing for Model Compression. CVPR (2020)
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
A flexible, extensible software framework for model compression based on the LC algorithm. CoRR (2020)
Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Structured Multi-Hashing for Model Compression. CoRR (2019)
Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev
"Learning-Compression" Algorithms for Neural Net Pruning. CVPR (2018)
Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev
Model compression as constrained optimization, with application to neural nets. Part II: quantization. CoRR (2017)