109 citations · 284 across the 11 of their papers we have counts for
6 papers · 1 filter
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
Zihang Dai, Guokun Lai, Yiming Yang +1
With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower…
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
Ruohong Zhang, Yu Hao, Donghan Yu +3
Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the d…
Re-examination of the Role of Latent Variables in Sequence Modeling
Zihang Dai, Guokun Lai, Yiming Yang +1
With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence. However, opposite results are also observed in other…
Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data
Guokun Lai, Bohan Li, Guoqing Zheng +1
How to model distribution of sequential data, including but not limited to speech and human motions, is an important ongoing research problem. It has been demonstrated that model c…
Learning Depthwise Separable Graph Convolution from Data Manifold
Guokun Lai, Hanxiao Liu, Yiming Yang
Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been…
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang +1
Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and…