输出部分可以进行更换:对于输出为定长的字典类型任务,可以替换为LSTM;对于输出为变长的(如组合优化问题),输出使用pointer network OUTPUT SETS 链式法则描述随机变量Y的联合概率,是联合概率最简单的分解,不要求任何严格的限制(比如条件独立);因此理论上,如果模型足够强大,那么任何顺序对模型都可以运行,即不需要产生Y序列...
sequencesetsmattersvinyalsordersutskever PublishedasaconferencepaperatICLR2016 ORDERMATTERS:SEQUENCETOSEQUENCEFORSETS OriolVinyals,SamyBengio,ManjunathKudlur GoogleBrain {vinyals,bengio,keveman}@google ABSTRACT Sequenceshavebecomefirstclasscitizensinsupervisedlearningthankstothe resurgenceofrecurrentneuralnetworks.Many...
Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (se
Order Matters: Sequence to sequence for sets 来自 ui.adsabs.harvard.edu 喜欢 0 阅读量: 192 作者:O Vinyals,S Bengio,M Kudlur 摘要: Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping ...
译问查术语的description, sequence of sets
Andrew TomkinsACMKnowledge Discovery and Data MiningAustin R Benson, Ravi Kumar, and Andrew Tomkins. Sequences of sets. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1148-1157. ACM, 2018....
This is notes of Chapter 2. 2.1 Sets 2.2 Set Operations 2.3 Functions 2.4 Sequences and Summations 2.5 Cardinality of Sets 2.6 Matrices
Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018) Hierarchical Neural Story Generation (Fan et al., 2018) Scaling Neural Machine Translation (Ott et al., 2018) Convolutional Sequence to Sequence Learning (Gehring et al., 2017) ...
The general TCN architecture (as described in [1]) consists of multiple residual blocks, each containing two sets of dilated causal convolution layers with the same dilation factor, followed by normalization, ReLU activation, and spatial dropout layers. The network adds the input of each block ...
Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered ...