Timezone: »
Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges. The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells. Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance (even with standard RNN cells) in tasks involving very long-term dependencies. To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures. We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures. The code for our method is publicly available at https://github.com/code-terminator/DilatedRNN.
Author Information
Shiyu Chang (IBM T.J. Watson Research Center)
Yang Zhang (IBM T. J. Watson Research)
Wei Han (University of Illinois at Urbana-Champaign)
Mo Yu (Johns Hopkins University)
Xiaoxiao Guo (IBM Research)
Wei Tan (IBM T. J. Watson Research Center)
Xiaodong Cui (IBM T. J. Watson Research Center)
Michael Witbrock (IBM Research, USA)
Mark Hasegawa-Johnson (University of Illinois)
Professor Mark Hasegawa-Johnson (Fellow of the ASA, 2011, Fellow of the IEEE, 2020) has been on the faculty at the University of Illinois (ECE Department) since 1999. His Ph.D. thesis (MIT, 1996), "Formant and Burst Spectral Measures with Quantitative Error Models for Speech Sound Classification," initiated a lifelong career in the mathematical representation of linguistic knowledge. He is Treasurer of ISCA, Senior Area Editor of the IEEE Transactions on Audio, Speech and Language, a reviewer for the NSF, NIH, EPSRC, NWO, and QNRF, and was plenary speaker at the 2020 IEEE Workshop on Automatic Speech Recognition and Understanding.
Thomas Huang (UIUC)
More from the Same Authors
-
2021 Spotlight: PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition »
Cheng-I Jeff Lai · Yang Zhang · Alexander Liu · Shiyu Chang · Yi-Lun Liao · Yung-Sung Chuang · Kaizhi Qian · Sameer Khurana · David Cox · Jim Glass -
2022 Poster: A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization »
Songtao Lu · Siliang Zeng · Xiaodong Cui · Mark Squillante · Lior Horesh · Brian Kingsbury · Jia Liu · Mingyi Hong -
2021 Poster: Understanding Interlocking Dynamics of Cooperative Rationalization »
Mo Yu · Yang Zhang · Shiyu Chang · Tommi Jaakkola -
2021 Poster: TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up »
Yifan Jiang · Shiyu Chang · Zhangyang Wang -
2021 Poster: PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition »
Cheng-I Jeff Lai · Yang Zhang · Alexander Liu · Shiyu Chang · Yi-Lun Liao · Yung-Sung Chuang · Kaizhi Qian · Sameer Khurana · David Cox · Jim Glass -
2020 : Invited talk - Multimodal Distant Supervision »
Mark Hasegawa-Johnson -
2020 Poster: A Decentralized Parallel Algorithm for Training Generative Adversarial Nets »
Mingrui Liu · Wei Zhang · Youssef Mroueh · Xiaodong Cui · Jarret Ross · Tianbao Yang · Payel Das -
2020 Poster: Ultra-Low Precision 4-bit Training of Deep Neural Networks »
Xiao Sun · Naigang Wang · Chia-Yu Chen · Jiamin Ni · Ankur Agrawal · Xiaodong Cui · Swagath Venkataramani · Kaoutar El Maghraoui · Vijayalakshmi (Viji) Srinivasan · Kailash Gopalakrishnan -
2020 Poster: Training Stronger Baselines for Learning to Optimize »
Tianlong Chen · Weiyi Zhang · Zhou Jingyang · Shiyu Chang · Sijia Liu · Lisa Amini · Zhangyang Wang -
2020 Poster: ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training »
Chia-Yu Chen · Jiamin Ni · Songtao Lu · Xiaodong Cui · Pin-Yu Chen · Xiao Sun · Naigang Wang · Swagath Venkataramani · Vijayalakshmi (Viji) Srinivasan · Wei Zhang · Kailash Gopalakrishnan -
2020 Spotlight: Training Stronger Baselines for Learning to Optimize »
Tianlong Chen · Weiyi Zhang · Zhou Jingyang · Shiyu Chang · Sijia Liu · Lisa Amini · Zhangyang Wang -
2020 Oral: Ultra-Low Precision 4-bit Training of Deep Neural Networks »
Xiao Sun · Naigang Wang · Chia-Yu Chen · Jiamin Ni · Ankur Agrawal · Xiaodong Cui · Swagath Venkataramani · Kaoutar El Maghraoui · Vijayalakshmi (Viji) Srinivasan · Kailash Gopalakrishnan -
2020 Poster: The Lottery Ticket Hypothesis for Pre-trained BERT Networks »
Tianlong Chen · Jonathan Frankle · Shiyu Chang · Sijia Liu · Yang Zhang · Zhangyang Wang · Michael Carbin -
2019 Poster: Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks »
Xiao Sun · Jungwook Choi · Chia-Yu Chen · Naigang Wang · Swagath Venkataramani · Vijayalakshmi (Viji) Srinivasan · Xiaodong Cui · Wei Zhang · Kailash Gopalakrishnan -
2019 Poster: Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries »
Fuwen Tan · Paola Cascante-Bonilla · Xiaoxiao Guo · Hui Wu · Song Feng · Vicente Ordonez -
2019 Poster: Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers »
Guang-He Lee · Yang Yuan · Shiyu Chang · Tommi Jaakkola -
2019 Poster: A Game Theoretic Approach to Class-wise Selective Rationalization »
Shiyu Chang · Yang Zhang · Mo Yu · Tommi Jaakkola -
2018 Poster: Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization »
Sijia Liu · Bhavya Kailkhura · Pin-Yu Chen · Paishun Ting · Shiyu Chang · Lisa Amini -
2018 Poster: Dialog-based Interactive Image Retrieval »
Xiaoxiao Guo · Hui Wu · Yu Cheng · Steven Rennie · Gerald Tesauro · Rogerio Feris -
2018 Poster: Non-Local Recurrent Network for Image Restoration »
Ding Liu · Bihan Wen · Yuchen Fan · Chen Change Loy · Thomas Huang -
2018 Poster: Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks »
Xiaodong Cui · Wei Zhang · Zoltán Tüske · Michael Picheny -
2016 : Panel on "Explainable AI" (Yoshua Bengio, Alessio Lomuscio, Gary Marcus, Stephen Muggleton, Michael Witbrock) »
Yoshua Bengio · Alessio Lomuscio · Gary Marcus · Stephen H Muggleton · Michael Witbrock -
2014 Poster: Accelerated Mini-batch Randomized Block Coordinate Descent Method »
Tuo Zhao · Mo Yu · Yiming Wang · Raman Arora · Han Liu