Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260

Convolutional networks for images, speech, and time series

LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,646

Deep Neural Networks for Acoustic Modeling in Speech Recognition

Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al. (2012) · Cited by 1,839

Connectionist Speech Recognition: A Hybrid Approach

Bourlard, H. A., & Morgan, N. H. (1993) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,115

Deep convolutional neural networks for LVCSR

Sainath, T. N., Mohamed, A., Kingsbury, B. E. D., & Ramabhadran, B. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639347 · Cited by 1,083

Conversational speech transcription using context-dependent deep neural networks

Seide, F. T. B., Li, G., & Yu, D. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-169 · Cited by 877

Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

Seide, F. T. B., Li, G., Xie, C., & Yu, D. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163899 · Cited by 648

Parallel stochastic gradient algorithms for large-scale matrix completion

Recht, B., & Ré, C. (2013) · Mathematical Programming Computation · doi:10.1007/s12532-013-0053-8 · Cited by 378

Introduction to Linear Algebra

DeFranza, J., & Gagliardi, D. (2008) · Cited by 378

Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression

Yuan, M., Ekici, A., Lu, Z., & Monteiro, R. D. C. (2007) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00591.x · Cited by 310

Summary and Future Directions

Yu, D., & Deng, L. (2014) · Signals and communication technology · doi:10.1007/978-1-4471-5779-3_15

Scalable Machine Learning for Visual Data

Yu, X. (2015) · doi:10.7916/d8f47ndb

From Speech Recognition to Language and Multimodal Processing

Deng, L. (2015)

Low-Rank Representation For Enhanced Deep Neural Network Acoustic Models

Luyet, G. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne)

Cluster-based senone selection for the efficient calculation of deep neural network acoustic models

Liu, J., Ling, Z.-H., Wei, S., Hu, G., & Dai, L.-R. (2016) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2016.7918399

An Embedded Deep Learning based Word PredictionPreprint

Yu, S., Kulkarni, N. V., Lee, H., & Kim, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01662

Deep Least Squares Regression for Speaker Adaptation

Kim, Y., Lim, H., Goo, J., & Kim, H. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-783

Learning the number of nodes in DNNs with activation mask

Chen, H., Zhang, S., Hou, J., & Dai, L.-R. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282224

Acoustic Model Compression with Knowledge Transfer

Yi, J., Tao, J., Wen, Z., Li, Y., & Ni, H. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-8111-8_9

Leveraging Second-Order Log-Linear Model for Improved Deep Learning Based ASR Performance

Raj, A., Rath, S. P., & Vepa, J. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1156

Optimal Low Rank Tensor Factorization for Deep Learning

Purohit, A., Abhishek, Rakesh, & Verma, S. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-2372-0_42

ExpandNets: Exploiting Linear Redundancy to Train Small Networks.Preprint

Guo, S., Álvarez, J. M., & Salzmann, M. (2018) · arXiv (Cornell University)

Representative Applications

Lin, Z., & Zhang, H. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812731-5.00005-6

Low-Frequency Character Clustering for End-to-End ASR System

Ito, H., Hagiwara, A., Ichiki, M., Kobayakawa, T. S., Mishima, T., Sato, S., & Kobayashi, A. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659735

Two-Stage Backward Elimination Method for Neural Networks Model Reduction

Tang, X., & Zhang, L. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1267/1/012087

C3-FIow: Compute Compression Co-Design FIow for Deep Neural Networks

Sotoudeh, M., & Baghsorkhi, S. S. (2019)

Structure Characteristic-Aware Pruning Strategy for Convolutional Neural Networks

Zuo, P., Wang, R., Fu, X., Yang, H., Liu, Y., Zhang, L., Zhang, H., & Qian, D. (2019) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2019.00211 · Cited by 1

An Improving Framework of regularization for Network CompressionPreprint

Zhenqian, E., & Weiguo, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05078

Fast LSTM Inference by Dynamic Decomposition on Cloud Systems

You, Y., He, Y., Rajbhandari, S., Wang, W., Hsieh, C., Keutzer, K., & Demmel, J. W. (2019) · Proceedings · doi:10.1109/icdm.2019.00085

Simplified LSTMS for Speech Recognition

Saon, G., Tüske, Z., Audhkhasi, K., Kingsbury, B. E. D., Picheny, M., & Thomas, S. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003898

Understanding Diversity based Pruning of Neural Networks - Statistical Mechanical Analysis.Preprint

Acharyya, R., Zhang, B., Chattoraj, A., Das, S., & Štefankovič, D. (2020) · arXiv (Cornell University)

Modeling information flow through deep convolutional neural networks

Nasiri, B. (2020) · Espace ÉTS (ETS)

High-performance and energy-efficient deep learning for resource-constrained devices

Ren, A. (2020) · Digital Repository Service (Northeastern University) · doi:10.17760/d20383685

Two-stage Strategy for Small-footprint Wake-up-word Speech Recognition System

You, X., Zhao, Y., & Sun, M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207489

Modulating Regularization Frequency for Efficient Compression-Aware Model TrainingPreprint

Lee, D., Kwon, S. J., Kim, B., Yun, J., Park, B., & Jeon, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.01875

Common visual pattern discovery and analysis

Wang, Z. (2021) · doi:10.32657/10356/146898

4-bit Quantization of LSTM-based Speech Recognition ModelsPreprint

Fasoli, A., Chen, C., Serrano, M. J., Sun, X. N., Wang, N., Venkataramani, S., Saon, G., Cui, X., Kingsbury, B. E. D., Zhang, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12074

Deep Clustering and Deep Network Compression

Alqahtani, A. (2021) · Cronfa (Swansea University) · doi:10.23889/suthesis.57269

Imitating What You Need: An Adaptive Framework for Detector Distillation

Sun, R., & Xiong, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87355-4_8

Comparison of SVD and factorized TDNN approaches for speech to textPreprint

Michael, J. J., Goel, N. K., Navneeth, K., Robertson, J., & Mishra, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07027

Towards Zero-Shot Knowledge Distillation for Natural Language ProcessingPreprint

Rashid, A., Lioutas, V., Ghaddar, A., & Rezagholizadeh, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15495 · Cited by 6

Nonlinear Tensor Ring NetworkPreprint

Li, X. P., Liu, Q., & So, H. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06532

Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of MultipliersPreprint

Xu, J., Chen, X., Hu, S., Yu, J., Liu, X., & Meng, H. M. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.14836

A Mixture of Expert Based Deep Neural Network for Improved ASRPreprint

Singh, V. P., Rath, S. P., & Pandey, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01025

Compressão de modelos em transferência de aprendizado de máquina

de Moraes, P. K. S. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.45.2021.tde-09022022-083513

Structured representation in deep neural network systems

Ding, C. (2019) · doi:10.17760/d20323953

Introduction

Wang, Z., & Liu, D. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813659-1.00001-9

On the Compression of Neural Networks Using $\ell_0$-Norm Regularization and Weight Pruning

Oliveira, F. D. D. R., Batista, E. L. O., & Seara, R. (2021) · PubMed · doi:10.48550/arxiv.2109.05075 · Cited by 2

Reduction of DNNs in edge computingPreprint

Garmendia-Orbegozo, A., Núñez-González, J. D., & Anton, M. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1862445/v1

Building Compact and Robust Deep Neural Networks with Toeplitz Matrices

Araujo, A. (2021) · theses.fr (ABES) · doi:10.70675/581a32b0zfa99z457ezab3fzd0f62f6fc8d8 · Cited by 1

Compressing CNN by alternating constraint optimization framework

Liu, P., Liu, W., Shi, C., Zhang, Z., Li, Z., & Liu, J. (2022) · International Conference on Digital Image Processing · doi:10.1117/12.2643734

Designing B-spline-based Highly Efficient Neural Networks for IoT Applications on Edge Platforms

Kuzuya, N., & Nagao, T. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945478 · Cited by 1

Anomalous Event Detection and Localization Based on Deep Generative Adversarial Networks for Surveillance Videos

Ganokratanaa, T. (2020) · doi:10.58837/chula.the.2020.155

高层语义分析中的模型蒸馏方法综述

Sun, R., & Xiong, H. (2023) · Journal of Image and Graphics · doi:10.11834/jig.210337

Efficient Algorithms for Artificial Neural Networks and Explainable AI

Eshkiki, H. (2023) · Cronfa (Swansea University) · doi:10.23889/suthesis.63943

Deep Learning Model Optimization

Ren, J., & Xia, D. T. (2023) · Springer eBooks · doi:10.1007/978-981-99-2897-2_8

A Stochastic Rate-Distortion Approach to Supervised Learning Systems

Elshafiy, A., Namazi, M., & Rose, K. H. (2023) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit54713.2023.10206453

An Empirical Study on Neural Networks Pruning: Trimming for Reducing Memory or Workload

Xiao, K., Cai, X., & Xu, K. (2023) · International Conference on Data-driven Optimization of Complex Systems (DOCS) · doi:10.1109/docs60977.2023.10294956

A new approach to neural networks using pseudo-differential operators

Du, H., Molahajloo, S., & Wang, X. (2024) · Journal of Pseudo-Differential Operators and Applications · doi:10.1007/s11868-023-00580-0

Research of Convents Channel Pruning Based on MCP-L1 Regularization

Liu, R., Wang, P., Zhou, L. Y., & Luo, Y. (2023) · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP) · doi:10.1109/aiiip61647.2023.00014

Lightweight Power Engineering Audit Event Recognition Model Based on BERT

Ding, P., Sui, A., & Sun, Y. (2023) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3639631.3639652

Mobile-friendly skin lesion detection using an attention-driven lightweight model

Hu, M., & Yang, X. (2024) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.3006822

Transport Object Detection in Street View Imagery Using Decomposed Convolutional Neural Networks

Bai, Y., Shang, C., Li, Y., Shen, L., Zeng, X., & Shen, Q. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-55568-8_34

On Block g-Circulant Matrices with Discrete Cosine and Sine Transforms for Transformer-Based Translation Machine

Asriani, E., Muchtadi-Alamsyah, I., & Purwarianti, A. (2024) · Mathematics · doi:10.3390/math12111697

A Filter Similarity-Based Early Pruning Methods for Compressing CNNs

Jiang, Z., & Li, K. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-4396-4_4

An Approach to Pruning the Structure of Convolutional Neural Networks without Loss of Generalization Ability

Chen, C., Kroshchanka, A., Golovko, V. A., & Golovko, O. (2024) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661824700056

Hybrid Compression Model Based on Redundancy Quantization and Geometric Median Weight Evaluation in Ddos Attack DetectionPreprint

Li, C., Wu, S., Lou, J., Fang, X., Pan, Y., & Ma, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4892698

On the atout ticket learning problem for neural networks and its application in securing federated learning exchanges

Bouayad, A., Akallouch, M., Mahdaouy, A. E., Alami, H., & Berrada, I. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103891 · Cited by 1

Refining uniform approximation algorithm for low-rank Chebyshev embeddings

Morozov, S., Zheltkov, D. A., & Osinsky, A. (2024) · Russian Journal of Numerical Analysis and Mathematical Modelling · doi:10.1515/rnam-2024-0027

A Feature Map Fusion Self-Distillation Scheme for Image Classification Networks

Qin, Z., Ni, S., Zhu, M., Jia, Y., Liu, S., & Chen, Y. (2025) · Electronics · doi:10.3390/electronics14010182

Data Privacy-Preserving and Communication Efficient Federated Multilinear Compressed Learning

Xiao, D., Yang, Z., Zhang, M., & Chen, L. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00203

Tensor deep-anomaly: robust tensor classifier for video anomaly detection in surveillance videos

Ichi, A. E., Kaddah, W., Bouz, M. E., & Badoc, I. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20814-1

LT-OAQ: Learnable Threshold Based Outlier-Aware Quantization and its Energy-Efficient Accelerator for Low-Precision On-Chip Training

Xu, Q., Liu, Y., Meng, Y., Chen, Y., Mao, Y., Li, L., & Fu, Y. (2025) · Design, Automation, and Test in Europe · doi:10.23919/date64628.2025.10992890

Low-rank perturbation adjustment (LoPA): An implicit regularization method in image classification

Mahmoud, N. T. A., Antson, H., Chan, W. T., Sulaiman, M., Choi, J., Shimmi, O., & Roy, K. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20941-9

Toward Edge Intelligence *

Xu, L., & Shi, W. (2025) · Wiley eBooks · doi:10.1002/9781394285860.ch4

Quantification stochastic configuration networks with incremental encoding

Wang, W., Li, S., & Fu, W. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.107817

LRAT: Low-Rank Adaptive Transformer for end-to-end speech recognition

Cheng, X., Wu, Y.-Y., & Wang, R. (2025) · Signal, Image and Video Processing · doi:10.1007/s11760-025-04471-3

Compression of Convolutional Neural Networks Employing Tensor Train and High Dimensional Model Representation

Yılmaz, B., & Tuna, S. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101768

RecKross: Recommendations with k-Cross Kernel Net

Vaidya, A., & Chatterjee, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92602-0_24

Kronecker Embedding Compression in FlauBERT Model for Energy Consumption Reduction: A Theoretical and Empirical Analysis

Ciocan, A. V., Courboulay, V., & Ciocan, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-99965-9_28

Structured and Balanced Multicomponent and Multilayer Neural Networks

Shi-jun, Z., Hongkai Zhao, C., Zhong, Y., & Zhou, H. (2025) · SIAM Journal on Scientific Computing · doi:10.1137/24m1675990

ASPSeg: Adaptive Space-Pruning Segmentation for Coronary Angiograms Sequences (Preprint)Preprint

Lin, K.-W., Lee, C.-K., & Lee, C.-R. (2025) · doi:10.2196/preprints.71651

Optimizing TinyML Models for Real-Time Environmental Monitoring: A Comparative Study of Compression Techniques

Shubneet, Yadav, A. R., Chanda, P., Bhowmik, S., Abrar, M. Y. B. T., & Talwandi, N. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-02831-0_3

Filter Pruning and Knowledge Distillation for Efficient Source-Free Unsupervised Domain Adaptation

Gajjar, K. D., Joshi, P., & Sanodiya, R. K. (2025) · International Conference on Contemporary Computing · doi:10.1109/ic366947.2025.11290312

The role of optimization techniques in achieving sustainable artificial intelligence

Hussain, H., & Tamizharasan, P. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-32878-7.00008-0 · Cited by 1

BPE-MTSNet: Balanced Performance and Efficiency in Multivariate Spatiotemporal Time Series Forecasting

Zhao, H., Li, Y., Liu, P., Wang, W., Shi, L., & Cui, Y. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3661975

Krony-PT: GPT-2 Compressed with Kronecker Products

Ayad, M. A. B., Mitrović, J., & Granitzer, M. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11390968

Net2Tab: Tabularizing neural networks with applications to data prefetching

Zhang, P., Gupta, N., Kannan, R., & Prasanna, V. K. (2026) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2026.105265

Contributions to unsupervised learning from massive high-dimensional data streams : structuring, hashing and clustering

Morvan, A. (2018) · theses.fr (ABES) · doi:10.70675/d4e37be4z088dz445bz816fz7fbe9efd3995

Approximations parcimonieuses et méthodes à noyaux pour la compression de modèles d'apprentissage

Giffon, L. (2020) · theses.fr (ABES) · doi:10.70675/7c477a6fz884bz4705z871fz046d746e8042

IDAP++: Advancing Divergence-Aware Pruning with Joint Filter and Layer Optimization

Samarin, A., Nazarenko, A., Kotenko, E., Savelev, A., Toropov, A., Motyko, A. A., & Malykh, V. A. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3786659

Vers une Intelligence Artificielle Numérique Responsable

Ciocan, A. V. (2024) · theses.fr (ABES) · doi:10.70675/56c5e5d9z8984z4a72zba0bzf0e2f7041cc7

E2PL: Effective and Efficient Prompt Learning for Incomplete Multi-view Multi-Label Class Incremental Learning

Chen, J., Wu, Y., Huang, K., Zhao, W., Yangyang, W., Miao, X., Zhu, M., Xi, M., & Cheng, G. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792572

Survey: Model Compression for AI-Based Underwater Mine Countermeasure

Chang, H., Kim, K., Kim, Y., & Park, S. (2026) · Journal of the KNST · doi:10.31818/jknst.2026.3.9.1.181

Neural Network Compression and Knowledge Distillation

Ghojogh, B., & Ghodsi, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-10738-1_20

Scalable, energy-efficient optical-neural architecture for multiplexed deepfake video detection

Kashani, P. G., Chen, S., & Özcan, A. (2026) · eLight · doi:10.1186/s43593-026-00143-y

Inference Acceleration Techniques for Large AI Models: A Survey

Mohamed, F. A., Chadi, M.-A., & Mousannif, H. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538859

Edge AI: Small Language Models on the Go

Włodarz, J. J. (2026) · doi:10.18690/um.fov.5.2026.2

Post-Training Structure-Agnostic Pruning and Quantization for Efficient Deep Neural NetworksPreprint

Reyhan, Z. A., Rahnamayan, S., & Bidgoli, A. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6981422

Object Embedding-Based Knowledge Distillation for Enhanced Visual Question Answering

Gupta, H. D., & Sheng, V. S. (2026) · Neural Processing Letters · doi:10.1007/s11063-026-11871-0

DATT: Dimension-Augmented Tensor-Train Decomposition for Neural Network Compression

Tai, Y.-C., Sie, C.-Y., & Lee, C. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00192

Comparative evaluation of perception-based and non-perception-based level of service estimation at semi-urban uncontrolled intersections

Datta, S. (2026) · Innovative Infrastructure Solutions · doi:10.1007/s41062-026-02952-z

FeDLRT: Federated Dynamical Low-Rank Training

Schotthöfer, S., & Laiu, M. P. (2026) · SIAM Journal on Mathematics of Data Science · doi:10.1137/25m1788142

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

Dao, T., Sohoni, N. S., Gu, A., Eichhorn, M., Blonder, A., Leszczynski, M., Rudra, A., & Ré, C. (2020) · arXiv · doi:10.48550/arxiv.2012.14966 · Cited by 21

Deep neural network based acoustic model parameter reduction using manifold regularized low rank matrix factorization

Chung, H., Kang, J. J., Park, K. Y., Lee, S. J., & Park, J. G. (2016) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2016.7846333 · Cited by 3

Parameter Reduction For Deep Neural Network Based Acoustic Models Using Sparsity Regularized Factorization Neurons

Chung, H., Chung, E., Park, J. G., & Jung, H. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852021 · Cited by 6

Multi-lingual speech recognition with low-rank multi-task deep neural networks

Mohan, A., & Rose, R. C. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178921 · Cited by 33

A study of rank-constrained multilingual DNNS for low-resource ASR

Sahraeian, R., & Compernolle, D. V. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472713 · Cited by 11

Low-Rank Representation For Enhanced Deep Neural Network Acoustic Models

Luyet, G. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne)