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Kazuki Irie
Kazuki Irie
Harvard University, Center for Brain Science
Verified email at fas.harvard.edu - Homepage
Title
Cited by
Cited by
Year
Improved training of end-to-end attention models for speech recognition
A Zeyer, K Irie, R Schlüter, H Ney
Interspeech 2018, 2018
3002018
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention--w/o Data Augmentation
C Lüscher, E Beck, K Irie, M Kitza, W Michel, A Zeyer, R Schlüter, H Ney
Interspeech 2019, 2019
2962019
A Comparison of Transformer and LSTM Encoder Decoder Models for ASR
A Zeyer, P Bahar, K Irie, R Schlüter, H Ney
ASRU 2019, 2019
2272019
Lingvo: a modular and scalable framework for sequence-to-sequence modeling
J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ...
Preprint arXiv:1902.08295, 2019
2022019
Language modeling with deep transformers
K Irie, A Zeyer, R Schlüter, H Ney
Interspeech 2019, 2019
2002019
Linear transformers are secretly fast weight programmers
I Schlag*, K Irie*, J Schmidhuber
ICML 2021, 2021
198*2021
The devil is in the detail: Simple tricks improve systematic generalization of transformers
R Csordás, K Irie, J Schmidhuber
EMNLP 2021, 2021
1142021
LSTM, GRU, highway and a bit of attention: an empirical overview for language modeling in speech recognition
K Irie, Z Tuske, T Alkhouli, R Schluter, H Ney
Interspeech 2016, 2016
1102016
On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
K Irie, R Prabhavalkar, A Kannan, A Bruguier, D Rybach, P Nguyen
Interspeech 2019, 2019
73*2019
Going beyond linear transformers with recurrent fast weight programmers
K Irie*, I Schlag*, R Csordás, J Schmidhuber
NeurIPS 2021, 2021
592021
The RWTH/UPB/FORTH system combination for the 4th CHiME challenge evaluation
T Menne, J Heymann, A Alexandridis, K Irie, A Zeyer, M Kitza, P Golik, ...
CHiME 2016, 2016
522016
The Neural Data Router: Adaptive control flow in Transformers improves systematic generalization
R Csordás, K Irie, J Schmidhuber
ICLR 2022, 2021
482021
The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment
W Zhou, W Michel, K Irie, M Kitza, R Schlüter, H Ney
ICASSP 2020, 2020
472020
Training language models for long-span cross-sentence evaluation
K Irie, A Zeyer, R Schlüter, H Ney
ASRU 2019, 2019
472019
Mindstorms in Natural Language-Based Societies of Mind
M Zhuge*, H Liu*, F Faccio*, DR Ashley*, R Csordás, A Gopalakrishnan, ...
NeurIPS 2023 Workshop on Robustness of Few-shot and Zero-shot Learning in …, 2023
422023
RADMM: Recurrent Adaptive Mixture Model with Applications to Domain Robust Language Modeling
K Irie, S Kumar, M Nirschl, H Liao
ICASSP 2018, 2018
402018
A Modern Self-Referential Weight Matrix That Learns to Modify Itself
K Irie, I Schlag, R Csordás, J Schmidhuber
ICML 2022, 2022
322022
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
K Irie*, R Csordás*, J Schmidhuber
ICML 2022, 2022
282022
On efficient training of word classes and their application to recurrent neural network language models
R Botros, K Irie, M Sundermeyer, H Ney
Interspeech 2016, 2015
212015
Investigation on log-linear interpolation of multi-domain neural network language model
Z Tüske, K Irie, R Schlüter, H Ney
ICASSP 2016, 2016
202016
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