Fu, Yonggan, Yang, Haichuan, Yuan, Jiayi, Li, Meng, Wan, Cheng, Krishnamoorthi, Raghuraman, Chandra, Vikas, and Lin, Yingyan. DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks. Retrieved from https://par.nsf.gov/biblio/10473384. Thirty-ninth International Conference on Machine Learning (ICML 2022) .

Fu, Yonggan, Yang, Haichuan, Yuan, Jiayi, Li, Meng, Wan, Cheng, Krishnamoorthi, Raghuraman, Chandra, Vikas, & Lin, Yingyan. DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks. Thirty-ninth International Conference on Machine Learning (ICML 2022), (). Retrieved from https://par.nsf.gov/biblio/10473384.

Fu, Yonggan, Yang, Haichuan, Yuan, Jiayi, Li, Meng, Wan, Cheng, Krishnamoorthi, Raghuraman, Chandra, Vikas, and Lin, Yingyan. "DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks". Thirty-ninth International Conference on Machine Learning (ICML 2022) (). Country unknown/Code not available: arXiv. https://par.nsf.gov/biblio/10473384.

@article{osti_10473384,
place = {Country unknown/Code not available}, title = {DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks}, url = {https://par.nsf.gov/biblio/10473384}, abstractNote = {}, journal = {Thirty-ninth International Conference on Machine Learning (ICML 2022)}, publisher = {arXiv}, author = {Fu, Yonggan and Yang, Haichuan and Yuan, Jiayi and Li, Meng and Wan, Cheng and Krishnamoorthi, Raghuraman and Chandra, Vikas and Lin, Yingyan}, }