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

A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition

Huang, Z., Siniscalchi, S. M., & Lee, C. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.09.018 · Cited by 59

Tensor Decomposition for Compressing Recurrent Neural Network

Tjandra, A., Sakti, S., & Nakamura, S. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489213 · Cited by 58

A Survey on Green Deep LearningPreprint

Xu, J., Zhou, W., Fu, Z., Zhou, H., & Li, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05193 · Cited by 118

Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models

Lee, S., Chung, H., Park, M., Park, J., Ryu, W., & Ye, J. C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00983 · Cited by 69

A Generic Network Compression Framework for Sequential Recommender Systems

Sun, Y., Yuan, F., Yang, M., Wei, G., Zhao, Z., & Liu, D. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401125 · Cited by 55

ACDC: A Structured Efficient Linear LayerPreprint

Moczulski, M., Denil, M., Appleyard, J., & de Freitas, N. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05946 · Cited by 90

Diversity Networks: Neural Network Compression Using Determinantal Point ProcessesPreprint

Mariet, Z., & Sra, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05077 · Cited by 81

A Tucker Deep Computation Model for Mobile Multimedia Feature Learning

Zhang, Q., Yang, L. T., Liu, X., Chen, Z., & Li, P. (2017) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3063593 · Cited by 54

RELAXNet: Residual efficient learning and attention expected fusion network for real-time semantic segmentation

Liu, J., Xu, X., Shi, Y., Deng, C. D., & Shi, M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.12.003 · Cited by 54

Short-Term Electrical Load Demand Forecasting Based on LSTM and RNN Deep Neural Networks

Islam, B. U., & Ahmed, S. F. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/2316474 · Cited by 52

Quantum Algorithms for Feedforward Neural Networks

Allcock, J., Hsieh, C., Kerenidis, I., & Zhang, S. (2020) · ACM Transactions on Quantum Computing · doi:10.1145/3411466 · Cited by 61

Prune Your Model Before Distill It

Park, J., & No, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20083-0_8 · Cited by 57

Low-Complexity Approximate Convolutional Neural Networks

Cintra, R. J., Duffner, S., García, C., & Leite, A. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2815435 · Cited by 50

DynExit: A Dynamic Early-Exit Strategy for Deep Residual Networks

Wang, M., Mo, J., Lin, J. T., Wang, Z., & Du, L. (2019) · SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · doi:10.1109/sips47522.2019.9020551 · Cited by 51

Maximum a posteriori adaptation of network parameters in deep models

Huang, Z., Siniscalchi, S. M., Chen, I., Li, J., Wu, J., & Lee, C. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-285 · Cited by 50

LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression

Mao, Y., Wang, Y., Wu, C., Zhang, C., Wang, Y., Zhang, Q., Yang, Y., Tong, Y., & Bai, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.287 · Cited by 69

Deep Expander Networks: Efficient Deep Networks from Graph Theory

Prabhu, A., Varma, G., & Namboodiri, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_2 · Cited by 52

ExpandNets: Linear Over-parameterization to Train Compact Convolutional NetworksPreprint

Guo, S., Álvarez, J. M., & Salzmann, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10495 · Cited by 49

Proceedings of the First Workshop on Subword and Character Level Models in NLP

(2017) · doi:10.18653/v1/w17-41 · Cited by 48

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network

Zhao, J., Lv, Y., Zhou, Z., & Cao, F. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.06.013 · Cited by 47

GenExp: Multi-objective pruning for deep neural network based on genetic algorithm

Xu, K., Zhang, D., An, J., Liu, L., Liu, L., & Wang, D. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.022 · Cited by 51

Unfolded recurrent neural networks for speech recognition

Saon, G., Soltau, H., Emami, A., & Picheny, M. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-81 · Cited by 47

Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem

Jian, W., He, J., Chen, K., Zhou, Y., & Tang, Z. (2016) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-datacom-cyberscitec.2016.149 · Cited by 46

SubFlow: A Dynamic Induced-Subgraph Strategy Toward Real-Time DNN Inference and Training

Lee, S., & Nirjon, S. (2020) · IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) · doi:10.1109/rtas48715.2020.00-20 · Cited by 49

Distilling Object Detectors with Task Adaptive Regularization

Sun, R., Tang, F., Zhang, X., Xiong, H., & Tian, Q. (2020) · arXiv · doi:10.48550/arxiv.2006.13108 · Cited by 46

Context adaptive deep neural networks for fast acoustic model adaptation

Delcroix, M., Kinoshita, K., Hori, T., & Nakatani, T. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178829 · Cited by 45

Optimising TinyML with quantization and distillation of transformer and mamba models for indoor localisation on edge devices

Suwannaphong, T., Jovan, F., Craddock, I. J., & McConville, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-94205-9 · Cited by 59

Fast learning in Deep Neural Networks

Chandra, B., & Sharma, R. K. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.093 · Cited by 45

Butterfly Transform: An Efficient FFT Based Neural Architecture Design

Alizadeh, K., Prabhu, A., Farhadi, A., & Rastegari, M. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01204 · Cited by 47

Fast and scalable in-memory deep multitask learning via neural weight virtualization

Lee, S., & Nirjon, S. (2020) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3386901.3388947 · Cited by 45

Tensorized Embedding Layers for Efficient Model CompressionPreprint

Hrinchuk, O., Khrulkov, V., Mirvakhabova, L., Orlova, E., & Oseledets, I. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10787 · Cited by 66

Parallel Deep Neural Network Training for Big Data on Blue Gene/Q

Chung, I., Sainath, T. N., Ramabhadran, B., Picheny, M., Gunnels, J. A., Austel, V., Chauhari, U., & Kingsbury, B. E. D. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2626289 · Cited by 43

Singular Values for ReLU Layers

Dittmer, S., King, E. J., & Maaß, P. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2945113 · Cited by 43

Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables

Tüske, Z., Tahir, M. A., Schlüter, R., & Ney, H. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178779 · Cited by 42

Compression of End-to-End Models

Pang, R., Sainath, T. N., Prabhavalkar, R., Gupta, S., Wu, Y., Zhang, S., & Chiu, C. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1025 · Cited by 42

Auto Graph Encoder-Decoder for Neural Network Pruning

Yu, S., Mazaheri, A., & Jannesari, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00630 · Cited by 45

Joint architecture and knowledge distillation in CNN for Chinese text recognition

Wang, Z.-R., & Du, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107722 · Cited by 44

Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation

Kurata, G., & Audhkhasi, K. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1952 · Cited by 39

Efficient online learning with improved LSTM neural networks

Mirza, A. H., Kerpiççi, M., & Kozat, S. S. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102742 · Cited by 41

Literature Review of Deep Network Compression

Alqahtani, A., Xie, X., & Jones, M. W. (2021) · Informatics · doi:10.3390/informatics8040077 · Cited by 42

Adaptive weight compression for memory-efficient neural networks

Ko, J. H., Kim, D.-H., Na, T., Kung, J., & Mukhopadhyay, S. (2017) · Design, Automation, and Test in Europe · doi:10.23919/date.2017.7926982 · Cited by 37

End-to-End Speaker Verification via Curriculum Bipartite Ranking Weighted Binary Cross-Entropy

Bai, Z., Wang, J., Zhang, X.-L., & Chen, J. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3161155 · Cited by 38

Generalization of U-Net Semantic Segmentation for Forest Change Detection in South Korea Using Airborne Imagery

Pyo, J., Han, K.-J., Cho, Y., Kim, D., & Jin, D. (2022) · Forests · doi:10.3390/f13122170 · Cited by 33

Machine learning in spectral domain

Giambagli, L., Buffoni, L., Carletti, T., Nocentini, W., & Fanelli, D. (2021) · Nature Communications · doi:10.1038/s41467-021-21481-0 · Cited by 41

FedHM: Efficient federated learning for heterogeneous models via low-rank factorization

Yao, D., Pan, W., Shi, Y., O'Neill, M. J., Dai, Y., Wan, Y., Zhao, P., Jin, H., & Sun, L. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2025.104333 · Cited by 45

Small-footprint high-performance deep neural network-based speech recognition using split-VQ

Wang, Y., Li, J., & Gong, Y. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178919 · Cited by 37

KRR-CNN: kernels redundancy reduction in convolutional neural networks

Hssayni, E. H., Joudar, N., & Ettaouil, M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06540-3 · Cited by 35

Improving deep neural networks for LVCSR using dropout and shrinking structure

Zhang, S., Bao, Y., Zhou, P., Jiang, H., & Dai, L.-R. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854927 · Cited by 34

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning

Shi, Y. Y., Guo, Z., Su, X., Meng, L., Zhang, M., Sun, J., Wu, C., Zheng, M., Shang, X., Zou, X., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa596 · Cited by 34

Parameterized Hypercomplex Graph Neural Networks for Graph Classification

Le, T., Bertolini, M., Noé, F., & Clevert, D.-A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86365-4_17 · Cited by 34

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot TasksPreprint

Goldblum, M., Reich, S., Fowl, L., Ni, R., Cherepanova, V., & Goldstein, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06753 · Cited by 43

Automatic Speech Recognition With Very Large Conversational Finnish and Estonian Vocabularies

Enarvi, S., Smit, P., Virpioja, S., & Kurimo, M. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2743344 · Cited by 33

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

About pyramid structure in convolutional neural networks

Ullah, I., & Petrosino, A. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727350 · Cited by 34

AddressNet: Shift-Based Primitives for Efficient Convolutional Neural Networks

He, Y., Liu, X., Zhong, H., & Ma, Y. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00134 · Cited by 33

BiQGEMM: Matrix Multiplication with Lookup Table for Binary-Coding-Based Quantized DNNs

Jeon, Y., Park, B., Kwon, S. J., Kim, B., Yun, J., & Lee, D. (2020) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41405.2020.00099 · Cited by 35

A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Gorospe, J., Mulero, R., Arbelaitz, O., Muguerza, J., & Antón, M. Á. (2021) · Sensors · doi:10.3390/s21041031 · Cited by 32

Deep Neural Network Acoustic Models for ASR

Mohamed, A. (2014) · TSpace (University of Toronto) · Cited by 32

Simplifying deep neural networks for neuromorphic architectures

Chung, J., & Shin, T. (2016) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2897937.2898092 · Cited by 31

Compact Feedforward Sequential Memory Networks for Large Vocabulary Continuous Speech Recognition

Zhang, S., Jiang, H., Xiong, S., Wei, S., & Dai, L.-R. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-121 · Cited by 31

Multi-Modality Disease Modeling via Collective Deep Matrix Factorization

Wang, Q., Sun, M., Zhan, L., Thompson, P. M., Ji, S., & Zhou, J. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098164 · Cited by 31

Theory of adaptive SVD regularization for deep neural networks

Bejani, M. M., & Ghatee, M. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.04.021 · Cited by 30

Mr.BiQ: Post-Training Non-Uniform Quantization based on Minimizing the Reconstruction Error

Jeon, Y., Lee, C., Cho, E., & Ro, Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01201 · Cited by 35

An Overview of Neural Network CompressionPreprint

O’Neill, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03669 · Cited by 149

State-Clustering Based Multiple Deep Neural Networks Modeling Approach for Speech Recognition

Zhou, P., Jiang, H., Dai, L.-R., Hu, Y., & Liu, Q. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2392944 · Cited by 30

Block-term tensor neural networks

Ye, J., Li, G., Chen, D., Yang, H., Zhe, S., & Xu, Z. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.05.034 · Cited by 32

Dimensionality reduced training by pruning and freezing parts of a deep neural network: a survey

Wimmer, P., Mehnert, J., & Condurache, A. P. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10489-1 · Cited by 33

Fast face detection on mobile devices by leveraging global and local facial characteristics

Zhang, H., Wang, X., Zhu, J., & Kuo, C. ‐. J. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.05.016 · Cited by 29

Towards Data-Efficient Modeling for Wake Word Spotting

Gao, Y., Mishchenko, Y., Shah, A., Matsoukas, S., & Vitaladevuni, S. N. P. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053313 · Cited by 29

A Fast View Synthesis Implementation Method for Light Field Applications

Gao, W., Zhou, L., & Tao, L. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3459098 · Cited by 29

A comparative study of deep learning models for medical image classification

Dutta, S., Manideep, B., Rai, S., & Vijayarajan, V. (2017) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/263/4/042097 · Cited by 30

HitNet: Hybrid Ternary Recurrent Neural Network

Wang, P., Xie, X., Deng, L., Li, G., Wang, D., & Xie, Y. (2018) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 28

Advancing Acoustic-to-Word CTC Model With Attention and Mixed-Units

Das, A., Li, J., Ye, G., Zhao, R., & Gong, Y. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2933325 · Cited by 27

Towards Bayesian Deep Learning: A SurveyPreprint

Wang, H., & Yeung, D. (2016) · arXiv (Cornell University) · Cited by 28

Efficient Neural Network Compression Inspired by Compressive Sensing

Gao, W., Guo, Y., Ma, S., Li, G., & Kwong, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3186008 · Cited by 29

Channel Compression: Rethinking Information Redundancy Among Channels in CNN Architecture

Liang, J., Zhang, T., & Feng, G. (2020) · IEEE Access · doi:10.1109/access.2020.3015714 · Cited by 27

Structured output layer with auxiliary targets for context-dependent acoustic modelling

Świętojański, P., Bell, P., & Renals, S. J. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-715 · Cited by 26

DNN Model Compression for IoT Domain-Specific Hardware Accelerators

Russo, E., Palesi, M., Monteleone, S., Patti, D., Mineo, A., Ascia, G., & Catania, V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3111723 · Cited by 26

Low-Bit Quantization and Quantization-Aware Training for Small-Footprint Keyword Spotting

Mishchenko, Y., Gören, Y., Sun, M., Beauchene, C., Matsoukas, S., Rybakov, O. J., & Vitaladevuni, S. N. P. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00127 · Cited by 26

Pruning 3D Filters For Accelerating 3D ConvNets

Wang, Z., Hong, W., Tan, Y., & Yuan, J. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2950523 · Cited by 25

Enhancing Chinese comprehension and reasoning for large language models: an efficient LoRA fine-tuning and tree of thoughts framework

Chen, S., Wang, W., Chen, X., Zhang, M., Lu, P., Li, X., & Du, Y. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06499-7 · Cited by 27

AI Flow: perspectives, scenarios, and approaches

An, H., Hu, W., Huang, S., Huang, S., Li, R., Liang, Y., Shao, J., Song, Y., Wang, Z., Yuan, C., et al. (2026) · Vicinagearth. · doi:10.1007/s44336-025-00031-y · Cited by 25

Refining Architectures of Deep Convolutional Neural Networks

Shankar, S., Robertson, D., Ioannou, Y., Criminisi, A., & Cipolla, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.243 · Cited by 27

Towards Zero-Shot Knowledge Distillation for Natural Language Processing

Rashid, A., Lioutas, V., Ghaddar, A., & Rezagholizadeh, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.526 · Cited by 29

Collaborative Deep Forest Learning for Recommender Systems

Molaei, S., Havvaei, A., Zare, H., & Jalili, M. (2021) · IEEE Access · doi:10.1109/access.2021.3054818 · Cited by 25

On Low-Rank Directed Acyclic Graphs and Causal Structure Learning

Fang, Z., Zhu, S., Zhang, J., Liu, Y., Chen, Z., & He, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3273353 · Cited by 25

Speaker Adaptation of Hybrid NN/HMM Model for Speech Recognition Based on Singular Value Decomposition

Xue, S., Jiang, H., Dai, L.-R., & Liu, Q. (2015) · Journal of Signal Processing Systems · doi:10.1007/s11265-015-1012-6 · Cited by 24

Run-Time Efficient RNN Compression for Inference on Edge Devices

Thakker, U., Beu, J. G., Gope, D., Dasika, G. S., & Mattina, M. (2019) · 2019 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) · doi:10.1109/emc249363.2019.00013 · Cited by 21

Exploiting low-dimensional structures to enhance DNN based acoustic modeling in speech recognition

Dighe, P., Luyet, G., Asaei, A., & Bourlard, H. A. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472767 · Cited by 24

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial AttackPreprint

Ye, H., Huang, Z., Fang, C., Li, C. J., & Zhang, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.11377 · Cited by 24

Structured adaptive and random spinners for fast machine learning computationsPreprint

Bojarski, M., Choromanska, A., Choromański, K., Fagan, F., Gouy‐Pailler, C., Morvan, A., Sakr, N., Sarlós, T., & Atif, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.06209 · Cited by 24

A Survey on Low-Latency DNN-Based Speech Enhancement

Drgas, S. (2023) · Sensors · doi:10.3390/s23031380 · Cited by 25

Active Subspace of Neural Networks: Structural Analysis and Universal Attacks

Cui, C., Zhang, K., Daulbaev, T., Gusak, J., Oseledets, I. V., & Zhang, Z. (2020) · SIAM Journal on Mathematics of Data Science · doi:10.1137/19m1296070 · Cited by 26

Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination

Hawkins, C., Liu, X., & Zhang, Z. (2022) · SIAM Journal on Mathematics of Data Science · doi:10.1137/21m1391444 · Cited by 29

Machine learning in spectral domain

Giambagli, L., Buffoni, L., Carletti, T., Nocentini, W., & Fanelli, D. (2021) · RePEc: Research Papers in Economics · Cited by 23

A survey of visual neural networks: current trends, challenges and opportunities

Feng, P., & Tang, Z. (2022) · Multimedia Systems · doi:10.1007/s00530-022-01003-8 · Cited by 23

Group $L_{1/2}$ Regularization for Pruning Hidden Layer Nodes of Feedforward Neural Networks

Alemu, H. Z., Zhao, J., Li, F., & Wu, W. H. (2019) · IEEE Access · doi:10.1109/access.2018.2890740 · Cited by 23

QGAN: Quantized Generative Adversarial NetworksPreprint

Wang, P., Wang, D., Ji, Y., Xie, X., Song, H., Liu, X., Lyu, Y., & Xie, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08263 · Cited by 23

Computer Vision Model Compression Techniques for Embedded Systems:A Survey

Lopes, A., dos Santos, F. P., de Oliveira, D. C., Schiezaro, M., & Pedrini, H. (2024) · Computers & Graphics · doi:10.1016/j.cag.2024.104015 · Cited by 25

Fine context, low-rank, softplus deep neural networks for mobile speech recognition

Senior, A., & Lei, X. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6855087 · Cited by 22

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)