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pro vyhledávání: '"Khorrami, Pooya"'
Autor:
Rugina, Ileana, Dangovski, Rumen, Veillette, Mark, Khorrami, Pooya, Cheung, Brian, Simek, Olga, Soljačić, Marin
Recent advances in deep learning, in particular enabled by hardware advances and big data, have provided impressive results across a wide range of computational problems such as computer vision, natural language, or reinforcement learning. Many of th
Externí odkaz:
http://arxiv.org/abs/2112.11929
Autor:
Ramachandran, Prajit, Paine, Tom Le, Khorrami, Pooya, Babaeizadeh, Mohammad, Chang, Shiyu, Zhang, Yang, Hasegawa-Johnson, Mark A., Campbell, Roy H., Huang, Thomas S.
Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial for their success, generation is typically implemented in a na\"{i}ve
Externí odkaz:
http://arxiv.org/abs/1704.06001
Autor:
Paine, Tom Le, Khorrami, Pooya, Chang, Shiyu, Zhang, Yang, Ramachandran, Prajit, Hasegawa-Johnson, Mark A., Huang, Thomas S.
This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet. Compared to a naive implementation that has complexity O(2^L) (L denotes the number of layers in the network), our proposed approach removes redund
Externí odkaz:
http://arxiv.org/abs/1611.09482
Autor:
Han, Wei, Khorrami, Pooya, Paine, Tom Le, Ramachandran, Prajit, Babaeizadeh, Mohammad, Shi, Honghui, Li, Jianan, Yan, Shuicheng, Huang, Thomas S.
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip. Recently, there have been major advances for doing object detection in a single image. These
Externí odkaz:
http://arxiv.org/abs/1602.08465
We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted feat
Externí odkaz:
http://arxiv.org/abs/1602.07377
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, exa
Externí odkaz:
http://arxiv.org/abs/1510.02969
Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large labelled datasets. Unsupervised data has been proposed as another way to im
Externí odkaz:
http://arxiv.org/abs/1412.6597
Autor:
Frisella, Megan, Khorrami, Pooya, Matterer, Jason, Kratkiewicz, Kendra, Torres-Carrasquillo, Pedro
Publikováno v:
Computer Sciences & Mathematics Forum; 2022, Vol. 3, p6, 18p
Akademický článek
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Publikováno v:
2014 IEEE International Conference on Image Processing (ICIP); 2014, p1125-1129, 5p