Not All Samples Are Created Equal: Deep Learning with Importance Sampling[J]. arXiv: Learning, 2018.@article{katharopoulos2018not, title={Not All Samples Are Created Equal: Deep Learning with Importance Sampling}, author={Katharopoulos, Angelos and Fleuret, F}, journal={arXiv: Learning},...
作者说这个算法首先是由Are Loss Functions All the Same?提出的, 但是这篇文章只是讲了hinge loss的优势和对其它损失函数的分析. 作者说最相关的文章是Not All Samples Are Created Equal: Deep Learning with Importance Sampling, 这篇文章是从预处理(虽然也是要算loss的)的角度出发的, 理论部分较本文多一些. ...
bibliographic notes section of a book, and the authors seem to be have no understanding of the didactic intention of a textbook (beyond a collation or importance sampling of various topics). In other words, these portions read like a prose description of a bibliography, with equations thrown in...
最近一直沉迷强化里的重要性采样(Importance Sampling, IS),花了不少时间在这上面,先是补概率统计的基础,再是看off-on-policy的东西。 然后看到了,为什么Q-learning没用IS呢?好吧,弄懂了,单步Q-learning不需要IS后,又被多步加不加IS迷惑,简直离谱。被天启大佬推荐了这篇文章,看完后就更迷惑了。 上一篇为什么...
为了初始化DBM的模型参数,本文提出了逐层贪婪预训练RBM堆的算法,但是同论文“A Fast Learning Algorithm for Deep Belief Nets”中方法相比,有一个小小的变化,这是为了消除把自顶向下和自底向上联合在一起所带来的重复计算问题。对于最下层的RBM,我们使输入单元加倍且约束住可视层与隐含层间的权值,见图2右边部分。
Deep Q- learning with Experience Replay 其中非常有趣的技巧包括: 经验回放(Experience Replay )与随机探索;这一部分主要是为了提高样本采样效率,同时降低后续梯度下降中样本的相关性。 目标网络(Target Network),由于TD误差在策略改变过程中也会改变,因此造成神经网络拟合过程的不稳定性,因此构建新的目标网络,在每次...
Not All Samples Are Created Equal: Deep Learning with Importance Sampling Deep neural network training spends most of the computation on examples that are properly han- dled, and could be ignored. We propose to mit- igate this ph... A Katharopoulos 被引量: 8发表: 2018年 Search-Based Peer...
Visual Interpretability for Deep Learning: a Survey [arXiv] Behavior is Everything – Towards Representing Concepts with Sensorimotor Contingencies [paper] [article] [code] IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures [arXiv] [article] [code] DeepType: Multi...
Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure but rather determined from the equi
Deep learning (DL) is a powerful tool for mining features from data, which can theoretically avoid assumptions (e.g., linear events) constraining conventional interpolation methods. Motivated by this and inspired by image-to-image translation, we applied