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  1. 从Attention到Memory与Longer-Term Dependencies研究

  2. 《From Attention to Memory and towards Longer-Term Dependencies(NIPS'2015)》by Joshua Bengio http
  3. 所属分类:讲义

    • 发布日期:2016-01-02
    • 文件大小:17825792
    • 提供者:happytofly
  1. Bengio-NIPS2016Workshop-Brains+Bits

  2. Bengio-NIPS2016Workshop-Brains+Bits
  3. 所属分类:其它

    • 发布日期:2017-05-13
    • 文件大小:13631488
    • 提供者:pingguolou
  1. DL-Tutorial-NIPS2015

  2. deep learning NIPS’2015 Tutorial, by Geoff Hinton, Yoshua Bengio & Yann LeCun
  3. 所属分类:深度学习

    • 发布日期:2018-04-17
    • 文件大小:48234496
    • 提供者:qq_42009628
  1. 关于Parzen窗口的NIPS文献

  2. 关于Parzen窗口的NIPS 文献 Manifold Parzen Windows Pascal Vincent and Yoshua Bengio
  3. 所属分类:机器学习

    • 发布日期:2020-02-23
    • 文件大小:143360
    • 提供者:cauchy
  1. Siamese Recurrent Architectures for Learning Sentence Similarity.pdf

  2. 用最简单的模型、最简单的特征工程做出好效果,追求的就是极致性价比。如果有需要,可以在此基础上做一些模型更改和特征工程,提高表现效果。ture for face verification developed by Chopra, Hadsell, and This forces the LSTm to entirely capture the semantic dif- LeCun(2005), which utilizes symmetric Conv Nets where ferences d
  3. 所属分类:深度学习

    • 发布日期:2019-10-14
    • 文件大小:1048576
    • 提供者:wolegequya
  1. Algorithms for hyper-parameter optimization

  2. Algorithms for hyper-parameter optimization.pdf,讲述贝叶斯算法的TPE过程的专业论文The contribution of this work is two novel strategies for approximating f by modeling H: a hier archical Gaussian Process and a tree-structured parzen estimator. These are described in
  3. 所属分类:其它

    • 发布日期:2019-09-03
    • 文件大小:274432
    • 提供者:yangtao_whut
  1. dueldqn.pdf

  2. 关于duelingdqn的原始论文,适合初学者对深度强化学习duelingdqn的认识和了解Dueling Network Architectures for Deep Reinforcement Learning et al.(2016). The results of Schaul et al.(2016) are the 2.1. Deep Q-networks current published state-of-the-art The value functions as descri
  3. 所属分类:讲义

    • 发布日期:2019-09-02
    • 文件大小:688128
    • 提供者:m0_37384317
  1. glow编译器,降低了计算图之间的计算量

  2. 具体描述Glow编译器的基础知识,glow是通过减少计算图的计算量来优化的have implemented a high-level intermediate represen Variable name: save saveLl tation that allows a compiler to reason about and Value: 0.000000e+C0 output: floaK optimize high-level constructs such as tensors and
  3. 所属分类:其它

    • 发布日期:2019-07-27
    • 文件大小:843776
    • 提供者:xiao_mei_mei