Alignment-based的转录本定量-RSEM 在之前的一篇博文Alignment-free的转录本比对工具-Salmon提到了用Alignment-free的Salmon来基于转录本水平进行表达丰度的定量。我最早接触转录本的定量则是Alignment-based transcript quantification,也就是先比对后定量。我最先是在无参转录组中用了RSEM,因为无参转录组需要对reads进行拼...
Alignment-Based LearningLearning, Alignmentbased
Alignment-Based Metrics for Trace Comparison 31 A: m c a cmam B: m c a c b c m b m CMP: = = = _ _ = = ≠ = Fig. 1. Constructed alignment of sequence A and B Although the dynamic programming approach is functional, the quadratic time complexity leads to long alignment times....
Secondly, through maximizing the kernel alignment value between linear combination kernel and ideal kernel, both weights in the two defined kernels are learned in this process simultaneously, and the learned weights of labels can be employed as the degree of labeling importance regarded as a kind ...
论文解读:On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation 机器翻译是自然语言处理的任务之一。基于transformer和multi-head attention在机器翻译中的应用十分广泛。注意力机制在神经机器翻译(NMT)模型中通常扮演着统计机器翻译(SMT)中的对齐机制(Alignment Mechanism),通过注意力...
DeepAlign: Alignment-based Process Anomaly Correction Using Recurrent Neural Networks - tnolle/deepalign
Bootstrapping Structure into Language: Alignment-Based Learning This thesis introduces a new unsupervised learning framework, called Alignment-Based Learning, which is based on the alignment of sentences and Harris's (1... MMV Zaanen - 《University of Leeds》 被引量: 144发表: 2002年 Implementing...
2、GLOBAL ALIGNMENT BY WASSERSTEIN DISTANCE 因为专家和模仿者之间动态性的不同,导致有些状态转移可能也有很大的偏离,VAE可能无法将其拉回来,因此还需要在全局的the state visitation distribution上加上约束,使两者尽可能靠近(这么说的话,其实之前的方式GAIL的方式都只是全局上做的约束?)作者指出仅使用这个全局约束不...
Our approach includes the idea of interference alignment as one of its key ingredients. For graphs of a bounded degree, our algorithm has linear complexity in terms of the number of vertices, and polynomial complexity in terms of the number of edges. 2. We prove that our algorithm achieves ...
我们先介绍 Word-by-Context Alignment base model ,它可以reweight词的权重,使得重要的词获得更大的权重。现在让我们考虑2个连着线的点A,B,然后各自的text sequence为 t_a,t_b ,长度为 M_a,M_b 。所以A点的textual word embedding可以表示为 X_a \in R^{d*M_a}(d是word embedding的长度),B点同理...