41 citations · 61 across the 7 of their papers we have counts for
8 papers
Attention that does not Explain Away
Nan Ding, Xinjie Fan, Zhenzhong Lan +2
Models based on the Transformer architecture have achieved better accuracy than the ones based on competing architectures for a large set of tasks. A unique feature of the Transfor…
Multi-stage Pretraining for Abstractive Summarization
Sebastian Goodman, Zhenzhong Lan, Radu Soricut
Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-gene…
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman +3
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases be…
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
Po-Yao Huang, Ye Yuan, Zhenzhong Lan +2
We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge. In this multi-label video classification task, our pipeline achieved 84.6…
Deep Local Video Feature for Action Recognition
Zhenzhong Lan, Yi Zhu, Alexander G. Hauptmann
We investigate the problem of representing an entire video using CNN features for human action recognition. Currently, limited by GPU memory, we have not been able to feed a whole…
The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition
Zhenzhong Lan, Dezhong Yao, Ming Lin +2
Motivated by the success of data-driven convolutional neural networks (CNNs) in object recognition on static images, researchers are working hard towards developing CNN equivalents…