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  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. Noisy Networks for Exploration.pdf

  2. 关于Noisy Networks for Exploration dqn的原始论文，适合初学者对深度强化学习Noisy Networks for Exploration dqn的认识和了解Published as a conference paper at ICLR 2018 T is assessed by the action-value function Q defined as Q"(.a)=配 ∑ rR(t, at) (1) where E is the expectation ove

  3. 所属分类：讲义

    • 发布日期：2019-09-02
    • 文件大小：5242880
    • 提供者：m0_37384317

  1. larochelle-metalearning.pdf

  2. 英文版。很好的资源，适合机器学习以及人工智能爱好者。A RESEARCH AGENDA Deep learning successes have required a lot of labeled training data s collecting and labeling such data requires significant human labor practically, is that really how we'll solve Al sCientifically, this

  3. 所属分类：机器学习

    • 发布日期：2019-07-29
    • 文件大小：23068672
    • 提供者：xcmax

  1. Attention Is All You Need.pdf

  2. 谷歌提出的Transformer结构tput Probabilities Softmax Linear Add& Norm Feed orwa Add& Norm Add norm Multi-Head Attention Forward N Add Norm I Add norm Masked Multi-Head Multi-Head Attention Atention Encoding ① Encoding ut Output Embedding Embedding Inputs Ish

  3. 所属分类：深度学习

    • 发布日期：2019-07-13
    • 文件大小：2097152
    • 提供者：weixin_41778389

  1. LISTEN ATTEND AND SPELL A NEURAL NETWORK FOR SPEECH RECOGNITION.pdf

  2. 语音识别LAS结构where d and y, are MLP networks. After training, the a; distribution Table 1: WER comparison on the clean and noisy Google voice is typically very sharp and focuses on only a few frames of h; ci car search task. The CLDNN-hMM system is the s

  3. 所属分类：专业指导

    • 发布日期：2019-07-13
    • 文件大小：647168
    • 提供者：weixin_41778389

  1. NIPS2017-Oriol Vinyals-model vs optimazation meta-learning

  2. ppt框架：元学习定义，与无监督学习的对比，与监督学习的对比，元学习模型分类（基于模型，基于度量，基于优化）与例子，总结，展望

  3. 所属分类：深度学习

    • 发布日期：2020-07-21
    • 文件大小：2097152
    • 提供者：liz_Lee