Deep Networks
Found 6 free book(s)Learning Transferable Features with Deep Adaptation Networks
proceedings.mlr.pressdeep networks, resulting in statistically unboundedrisk for target tasks (Mansour et al., 2009; Ben-David et al., 2010). Our work is primarily motivated by Yosinski et al. (2014), which comprehensively explores feature transferability of deep convolutional neural networks. The method focuses on a different scenario where the learning tasks are ...
Understanding the difficulty of training deep feedforward ...
proceedings.mlr.pressdeep networks with sigmoids but initialized from unsuper-vised pre-training (e.g. from RBMs) do not suffer from this saturation behavior. Our proposed explanation rests on the hypothesis that the transformation that the lower layers of the randomly initialized network computes initially is
“Deep Fakes” using Generative Adversarial Networks (GAN)
noiselab.ucsd.edutwo GAN networks, and other than the loss in the tradi-tional GAN network, it also included a cycle-consistency loss to ensure any input is mapped to a relatively reasonable output. 2. Physical and Mathematical framework The framework we used in this project is a Cycle-GAN based on deep convolutional GANs. 2.1. Generative Adversarial Networks (GAN)
Spatio-Temporal Graph Convolutional Networks: A Deep ...
www.ijcai.orgSpatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for TrafÞc Forecasting Bing Yu! 1, Haoteng Yin! 2,3, Zhanxing Zhu 3,4 1 School of Mathematical Sciences, Peking University, Beijing, China 2 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 3 Center for Data Science, Peking University, Beijing, China
Sequence to Sequence Learning with Neural Networks
arxiv.orgDeep Neural Networks (DNNs) are extremely powerful machine learning models that achieve ex-cellent performanceon difficult problems such as speech rec ognition[13, 7] and visual object recog-nition [19, 6, 21, 20]. DNNs are powerful because they can perform arbitrary parallel computation for a modest number of steps.
Multifaceted Feature Visualization: Uncovering the ...
arxiv.orgWe can better understand deep neural networks by identifying which features each of their neu-rons have learned to detect. To do so, researchers have created Deep Visualization techniques in-cluding activation maximization, which synthet-ically generates inputs (e.g. images) that maxi-mally activate each neuron. A limitation of cur-