[1] Task-Driven Convolutional Recurrent Models of the Visual System Aran Nayebi, Daniel Bear, Jonas Kubilius, …
NIPS2018论文及代码集锦(13)(亮点:深度强化学习;三维RNN)
[1] Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models Kurtland Chua, Robe…
NIPS2018论文及代码集锦(12)(亮点: CNN正交正则;循环控制循环网络)
[1] The streaming rollout of deep networks – towards fully model-parallel execution Volker Fischer, Jan …
NIPS2018论文及代码集锦(11)(亮点：注意力模型; GAN; 马尔科夫状态模型)
[1] Unsupervised Attention-guided Image-to-Image Translation Youssef A. Mejjati, Christian Richardt, James Tom…
NIPS2018论文及代码集锦(10)(亮点：训练技巧；强化学习；GAN)
[1] Step Size Matters in Deep Learning Kamil Nar, S. Shankar Sastry University of California, Berkeley …
NIPS2018论文及代码集锦(9)(亮点：多元卷积稀疏编码、循环关系网络)
[1] Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres Oisín…
NIPS2018论文及代码集锦(8)(亮点：实证深度学习、贝叶斯、半监督众包聚类)
[1] Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy, Lance Kaplan, Melih Kandemir…
NIPS2018论文及代码集锦(3)(转载+整理)
[1] KDGAN: Knowledge Distillation with Generative Adversarial Networks Xiaojie Wang, Rui Zhang, Yu Sun, Jianzh…
NIPS2018论文及代码集锦(2)(转载+整理)
[1] Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units Yixi Xu, Xiao Wang Purdue…
NIPS2018论文及代码集锦(1)（转载+整理）
[1] Structure-Aware Convolutional Neural Networks Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, …