《Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms》

时间 2021-01-11

标签深度学习机器学习计算机视觉自然语言处理栏目 Microsoft Office 繁體版

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出处： ACL2018 1. 贡献本文提出在词向量上进行简单的池化操作在文本分类/匹配任务上就可以得到跟CNN/RNN相当的效果。 2. 方案 1） SWEM-aver：整个句子的信息 ) 2）SWEM-max：突出特征 ) 3）拼接SWEM-aver和SWEM-max 4 SWEM-hier（层次化）最大和平均池化没有考虑词序，这里引入层次化pooling。先作固定窗口的平均pooling，

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1. 论文笔记Baseline Needs More Love:On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
2. baseline needs more love 简单网络vs复杂网络（1）——baseline
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4. Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline）
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6. Missing associated label more...
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8. c3p0 - JDBC3 Connection and Statement Pooling
9. DEEP DOUBLE DESCENT: WHERE BIGGER MODELS AND MORE DATA HURT
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1. 论文笔记Baseline Needs More Love:On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
2. baseline needs more love 简单网络vs复杂网络（1）——baseline
3. Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
4. Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline）
5. Faster, more memory efficient and more ordered dictionaries on PyPy
6. Missing associated label more...
7. Deep Learning Models and Code for Pose Estimation
8. c3p0 - JDBC3 Connection and Statement Pooling
9. DEEP DOUBLE DESCENT: WHERE BIGGER MODELS AND MORE DATA HURT
10. 【ReID】Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional...

>>更多相关文章<<