由于依然使用了N-gram模型,因此只能利用局部历史信息,而不能捕捉长距离的依赖关系(long-range dependencies),相对于计数模型,神经网络利用非线性变换(non-linearity)捕捉这n元组之内的关联,比如New York这两个词的强共现关系,但这可能会影响测试集上的泛化。(可以算有利有弊吧?) 参数量会随着n值的增加(即历史信息...
绝大多数的convolutional model都用到的是kernel size比较有限的local convolution,因此无法建模long-range dependencies。最近,structured state-space model (S4) 及其变种在long sequence modeling上的表现非常惊艳,尤其是知名benchmark Long-Range Arena (LRA)吊打Transformer及其线性变种。S4实际上可以看成一个global conv...
c, where c is a numerical constant.\nWhen a<2(1+c) the process y(t), although Markovian, is long-range correlated.\nOur results help in clarifying that even in the context of Markovian processes\nlong-range dependencies are not necessarily associated to the occurrence of\nextreme events. ...
A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dep...
It has a temporal self-attention architecture to model long-range dependencies in the evolution. Moreover, CoEvoGNN optimizes model parameters jointly on two dynamic tasks, attribute inference and link prediction over time. So the model can capture the co-evolutionary patterns of attribute change ...
Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into...
used a combination of DFT and CALPHAD to calculate γ111 in several ternary Ni3Al-based alloys with refractory metal solutes18, and they found compositional dependencies that reasonably agreed with previous studies of the APB energy and yield strength in these alloys. The use of CALPHAD methods ...
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations - ALBERT/modeling.py at master · HuaYZhao/ALBERT
Paper: Efficiently Modeling Long Sequences with Structured State Spaces Motivation and current problem A central problem in sequence modeling is efficiently handling data that contains long-range dependencies (LRDs). 一般要求上万步(16k),现在能做到几千步就不错了。 用special matrix(HIPPO)武装起来的late...
N-grams fail to capture long-range dependencies Problem: what about n-grams that don't appear in the training data? —— rare n-grams can result in a probability of 0, or worse, undefined. ==>Solution: smoothing 3. Smoothing —— we want unseen n-grams to have defined, non-zero pro...