( en | de | ru )

 

Disambiguation of "Vemparala, Manoj Rohit"

Author of the publication

copydeleteadd this publication to your clipboard
DSC: Dense-Sparse Convolution for Vectorized Inference of Convolutional Neural Networks.

, , , , and . CVPR Workshops, page 1353-1360. Computer Vision Foundation / IEEE, (2019)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Rohit Sinha

Slavica Manojlovic

Rohit Gupta

Rohit Ghai

Manoj Kakkassery

 

Other publications of authors with the same name

Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs., , , , , , , and . DATE, page 706-711. IEEE, (2022)Hardware Aware Robust Compression of Neural Networks.. Technical University of Munich, Germany, (2023)Adversarial Robust Model Compression Using In-Train Pruning., , , , , , , , , and 2 other author(s). CVPR Workshops, page 66-75. Computer Vision Foundation / IEEE, (2021)ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks., , , , , , and . DAC, page 1-6. IEEE, (2020)AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms., , , , , , , , , and 3 other author(s). DATE, page 238-243. IEEE, (2022)BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks., , , , , , , , , and 1 other author(s). IntelliSys (1), volume 294 of Lecture Notes in Networks and Systems, page 148-167. Springer, (2021)Hardware-Aware Mixed-Precision Neural Networks using In-Train Quantization., , , , , , , , and . BMVC, page 60. BMVA Press, (2021)Pruning as a Binarization Technique., , , , , , , , , and . CVPR Workshops, page 2131-2140. IEEE, (2024)L2PF - Learning to Prune Faster., , , , , , , , and . CVIP (3), volume 1378 of Communications in Computer and Information Science, page 249-261. Springer, (2020)HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology., , , , , , , , , and . ACM Trans. Embed. Comput. Syst., 20 (5s): 66:1-66:25 (2021)