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

Filter Pruning via Automatic Pruning Rate Search

Sun, Q., Cao, S. C., & Chen, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26351-4_36 · Cited by 3

Efficient Building Strategy with Knowledge Distillation for Small-Footprint Acoustic Models

Moriya, T., Kanagawa, H., Matsui, K., Fukutomi, T., Shinohara, Y., Yamaguchi, Y., Okamoto, M., & Aono, Y. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639545 · Cited by 3

COPS: Controlled Pruning Before Training Starts

Wimmer, P., Jens, M., & Condurache, A. P. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533582 · Cited by 5

New Strategy Based on RBF Network to Develop a Collaborative Filtering Recommender System

Mohammadi, M., Naree, S. A., & Naseri, M.-A. (2022) · Computing and Informatics · doi:10.31577/cai_2022_3_757 · Cited by 3

Optimizing NARX-RNN Performance to Predict Precious Metal Futures market

Stephanie, Rengasamy, D., Juwono, F. H., Nandong, J., Brennan, A. J., & Gopal, L. (2022) · International Conference on Green Energy, Computing and Sustainable Technology (GECOST) · doi:10.1109/gecost55694.2022.10010534 · Cited by 3

Reverse Self-Distillation Overcoming the Self-Distillation Barrier

Ni, S., Ma, X., Zhu, M., Li, X., & Zhang, Y. (2023) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2023.3288227 · Cited by 4

VarKFaceNet: An Efficient Variable Depthwise Convolution Kernels Neural Network for Lightweight Face Recognition

Ma, Q., Zhang, P., & Cui, M. (2024) · IEEE Access · doi:10.1109/access.2024.3447018 · Cited by 3

Research on multi-base depth neural network speech recognition

Jun, C., Fei, L., Zhang, Y., & Liu, Y. (2017) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac.2017.8054271 · Cited by 3

Retrain-free fully connected layer optimization using matrix factorization

Sun, Y. J., Liu, X., & Liang, L. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297016 · Cited by 3

Translational Bit-by-Bit Multi-bit Quantization for CRNN on Keyword Spotting

Liu, B., Sun, Y., & Liu, B. (2019) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2019.00082 · Cited by 3

Domain Robust, Fast, and Compact Neural Language Models

Gerstenberger, A., Irie, K., Golik, P., Beck, E., & Ney, H. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054399 · Cited by 4

PSM-nets: Compressing Neural Networks with Product of Sparse Matrices

Giffon, L., Ayache, S., Kadri, H., Artières, T., & Sicre, R. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533408 · Cited by 4

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

Exploiting Eigenposteriors for Semi-Supervised Training of DNN Acoustic Models with Sequence Discrimination

Dighe, P., Asaei, A., & Bourlard, H. A. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1784 · Cited by 3

Low-rank and sparse subspace modeling of speech for DNN based acoustic modeling

Dighe, P., Asaei, A., & Bourlard, H. A. (2019) · Speech Communication · doi:10.1016/j.specom.2019.03.004 · Cited by 3

Separable Layers Enable Structured Efficient Linear SubstitutionsPreprint

Gray, G. D. B., Crowley, E. J., & Storkey, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00859 · Cited by 3

Learning from a Lightweight Teacher for Efficient Knowledge DistillationPreprint

Liu, Y., Zhang, W., & Wang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.09163 · Cited by 3

A Fully Tensorized Recurrent Neural NetworkPreprint

Onu, C. C., Miller, J., & Precup, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04196 · Cited by 7

Sequentially-reduced representation of artificial neural network to determine cardiovascular intrinsic frequenciesPreprint

Alavi, R., Wang, Q., Gorji, H., & Pahlevan, N. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.14.480311 · Cited by 3

Adaptive focusing for wideband beamforming in multipath environments

Ganti, A., Martinez, M. R., Hickman, G. W., & Krolik, J. L. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0034789 · Cited by 2

TER-FCN: Tensor Einstein Reduced Fully Connected Network applied to Region-based Convolutional Neural Networks for object detection

Ichi, A. E., Kaddah, W., Elbouz, M., & Badoc, I. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04283-5 · Cited by 2

Robust Cardiac Cine MRI Reconstruction With Spatiotemporal Diffusion Model

Wang, Z., Huang, J., Huang, M., Wang, C., Yang, G., & Qu, X. (2025) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2025.3598421 · Cited by 2

Exploiting sequential Low-Rank Factorization for multilingual DNNS

Sahraeian, R., & Compernolle, D. V. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953113 · Cited by 2

Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model

Park, S., Kim, G., Lee, J., Cha, J., Kim, J.-H., & Lee, H. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.607 · Cited by 4

DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment

Jiang, Z., Chen, T., Chen, X., Cheng, Y., Zhou, L., Yuan, L., Awadallah, A. H., & Wang, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_14 · Cited by 4

TetriX: Flexible Architecture and Optimal Mapping for Tensorized Neural Network Processing

Jie-Fang, Z., Lu, C., & Zhang, Z. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3365936 · Cited by 2

DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization

Ghavami, B., Kamjoo, A., Shannon, L., & Wilton, S. J. E. (2024) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed60706.2024.10528676 · Cited by 2

Spectral Tensor Layers for Communication-Free Distributed Deep Learning

Liu, X.-Y., Wang, X., Yuan, B., & Han, J. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3394861 · Cited by 4

IntentRec: An Advanced Recommender System Leveraging User-Item Intent

Vaidya, A., & Chatterjee, N. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-62277-9_37 · Cited by 2

Partial Filter-Sharing: Improved Parameter-sharing Method for Single Image Super-Resolution Networks

Park, K., & Cho, N. I. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00263 · Cited by 2

A Low-Complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-Beam Beamformers

Aluvihare, H., Sivasankar, S., Li, X., Madanayake, A., & Perera, S. M. (2025) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2025.3602901 · Cited by 2

Regularizing DNN acoustic models with Gaussian stochastic neurons

Zhang, H., Miao, Y., & Metze, F. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178915 · Cited by 2

Tamp

Gong, B., Jou, B., Yu, F., & Chang, S.-F. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2973802 · Cited by 3

Neural Networks with Block Diagonal Inner Product Layers

Nesky, A., & Stout, Q. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01424-7_6 · Cited by 2

Recurrent Neural Network Compression Based on Low-Rank Tensor Representation

Tjandra, A., Sakti, S., & Nakamura, S. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edp7040 · Cited by 2

An Empirical Comparison of Quantization, Pruning and Low-rank Neural Network Compression using the LC Toolkit

Idelbayev, Y., & Carreira-Perpiñán, M. Á. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533730 · Cited by 2

Slimming Convolutional Neural Network Based on Attention Mechanism for Pavement Crack Detection

Huang, W., Zhu, G., Fan, Z., Li, W., Rong, Y., & Cai, Y. (2021) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber53097.2021.9588247 · Cited by 2

Robustness analysis for compact neural networks

Chen, G., Peng, P., & Tian, Y. (2021) · Scientia Sinica Technologica · doi:10.1360/sst-2021-0233 · Cited by 2

Compressing Transformer-Based ASR Model by Task-Driven Loss and Attention-Based Multi-Level Feature Distillation

Lv, Y., Wang, L., Ge, M., Li, S., Ding, C., Pan, L., Wang, Y. G., Dang, J., & Honda, K. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746113 · Cited by 2

On-Device User-Adaptive Next Word Prediction System

Singh, S. (2022) · International Conference on Computer Science, Engineering and Applications (ICCSEA) · doi:10.1109/iccsea54677.2022.9936158 · Cited by 2

Fast data-free model compression via dictionary-pair reconstruction

Gao, Y., Zhang, Z., Zhang, H., Zhao, M., Yang, Y., & Wang, M. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01846-1 · Cited by 2

3D-KCPNet: Efficient 3DCNNs based on tensor mapping theory

Lv, R., Wang, D., Zheng, J., & Yang, Z.-X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126985 · Cited by 2

DeepThin: A Self-Compressing Library for Deep Neural NetworksPreprint

Sotoudeh, M., & Baghsorkhi, S. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06944 · Cited by 2

Bulletin of the Polish Academy of Sciences: Technical Sciences

Figurnov, M., Sobolev, A. N., & Vetrov, D. (2018) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpas.2018.125928 · Cited by 2

DARB: A Density-Aware Regular-Block Pruning for Deep Neural NetworksPreprint

Ren, A., Zhang, T., Wang, Y., Lin, S., Dong, P., Chen, Y.-K., Xie, Y., & Wang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08020 · Cited by 2

Hybrid Kronecker Product Decomposition and ApproximationPreprint

Cai, C., Chen, R., & Han, X. G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02955 · Cited by 2

Auto Graph Encoder-Decoder for Model Compression and Network Acceleration.Preprint

Yu, S., Mazaheri, A., & Jannesari, A. (2020) · arXiv (Cornell University) · Cited by 2

Statistical Mechanical Analysis of Neural Network PruningPreprint

Acharyya, R., Chattoraj, A., Zhang, B., Das, S., & Štefankovič, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16617 · Cited by 1

DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language ModelsPreprint

Chen, X., Chen, T., Chen, W., Awadallah, A. H., Wang, Z., & Cheng, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.00160 · Cited by 2

Prkan: Parameter-Reduced Kolmogorov-Arnold NetworksPreprint

Ta, H. T., Quy, T. D., Tran, A. N., Sidorov, G., & Gelbukh, A. F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5137812 · Cited by 2

Neural Differential Radiance Field: Learning the Differential Space Using a Neural Network

Hadadan, S., & Zwicker, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-50069-5_9 · Cited by 1

An Agile Super-Resolution Network via Intelligent Path Selection

Jia, L., Hu, Y., Tian, X., Luo, W., & Ye, Y. (2024) · Mathematics · doi:10.3390/math12071094 · Cited by 1

Novel Post-Training Structure-Agnostic Weight Pruning Technique for Deep Neural Networks

Reyhan, Z. A., Rahnamayan, S., & Bidgoli, A. A. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831075 · Cited by 1

Efficient Real-Time Human Activity Recognition with IoT Sensor Data Using Conditionally Parametrized Convolutions

Lovanshi, M., Tiwari, V., Singh, A., & Ingle, R. (2024) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig64922.2024.10911781 · Cited by 1

MSKFaceNet: A Lightweight Face Recognition Neural Network for Low-Power Devices

Zhang, P., Ma, Q., Li, Y., & Cui, M. (2025) · IEEE Access · doi:10.1109/access.2025.3584814 · Cited by 1

ERGo: Energy-Efficient Hybrid Graph Neural Network Training on Processing-in-Memory Architectures

Dhingra, P., Ugwu, C. E., Doppa, J., & Pande, P. (2025) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3760402 · Cited by 1

Uma Abordagem Híbrida para Sistemas de Recomendação com Base em Avaliações Textuais e Filtragem Colaborativa

Andrade, Y., Vasconcelos, N., Tuler, E., Pereira, A. C. M., & da Rocha, L. C. D. (2025) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia.2025.14831 · Cited by 2

ASQ & POST: A synergistic framework for adaptive and non-uniform quantization

Zhou, W., Yu, Z., Liu, X., Yang, J., Xiao, R., Wang, T., Tang, C., & Lv, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132332 · Cited by 1

Noise Injection as a Structural Diagnostic Tool for Deep Model Reduction

Lee, C.-H., Huang, C.-M., & Lai, W.-L. (2026) · IEEE International Conference on Knowledge Innovation and Invention (ICKII) · doi:10.3390/engproc2025120031 · Cited by 1

Binary SqueezeNet: Enhancing Parameter Efficiency in Binary Neural Networks

Atabey, S., & Akagündüz, E. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00427 · Cited by 1

Sequence discriminative training for low-rank deep neural networks

Tachioka, Y., Watanabe, S., Roux, J. L., & Hershey, J. R. (2014) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2014.7032182 · Cited by 1

Training and Decoding Speedup

Yu, D., & Deng, L. (2014) · Signals and communication technology · doi:10.1007/978-1-4471-5779-3_7 · Cited by 1

Compact kernel models for acoustic modeling via random feature selection

May, A., Collins, M., Hsu, D., & Kingsbury, B. E. D. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472112 · Cited by 1

Sensitivity-Characterised Activity Neurogram (SCAN) for Visualising and Understanding the Inner Workings of Deep Neural Network

Sim, K. C. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016sli0001 · Cited by 1

Compressing Deep Neural Network for Facial Landmarks Detection

Zeng, D., Zhao, F., & Bao, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49685-6_10 · Cited by 1

Speech recognition features based on deep latent Gaussian models

Tjandra, A., Sakti, S., & Nakamura, S. (2017) · Machine learning for signal processing ... · doi:10.1109/mlsp.2017.8168174 · Cited by 1

Unsupervised adaptation of student DNNS learned from teacher RNNS for improved ASR performance

Samarakoon, L., & Mak, B. K.-W. (2017) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2017.8268936 · Cited by 1

Beyond posteriorgram: Bottleneck features for keyword search

Yusuf, B., Gündoğdu, B., & Saraçlar, M. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404534 · Cited by 1

Automatic DNN Node Pruning Using Mixture Distribution-based Group Regularization

Yoshida, T., Moriya, T., Watanabe, K., Shinohara, Y., Yamaguchi, Y., & Aono, Y. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2062 · Cited by 1

Symmetric Quantization Scheme and Approximate Batch Normalization for Quantized Neural Networks Training

Wu, X., Bie, Z., & Lin, X. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1176/3/032024 · Cited by 1

Exploring the efficiency of the combined application of connection pruning and source data preprocessing when training a multilayer perceptron

Galchonkov, O. N., Nevrev, A., Glava, M. G., & Babych, M. (2020) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2020.200819 · Cited by 1

Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers

Lin, T.-Q., Yang, T.-H., Chang, C.-Y., Chen, K.-M., Feng, T.-H., Lee, H.-Y., & Tang, H. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434686 · Cited by 2

Learning Extremely Lightweight and Robust Model with Differentiable Constraints on Sparsity and Condition Number

Wei, X., Xu, Y., Huang, Y., Lv, H., Lan, H. P., Chen, M., & Tang, X. L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19772-7_40 · Cited by 3

Accelerating Computer Vision Tasks on GPUs using Ramanujan Graph Product Framework

Shaik, F. A., Konduru, T., Varma, G., & Kothapalli, K. (2023) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3570991.3571044 · Cited by 1

SLRProp: A Back-Propagation Variant of Sparse Low Rank Method for DNNs Reduction

Garmendia-Orbegozo, A., Núñez-González, J. D., & Anton, M. A. (2023) · Sensors · doi:10.3390/s23052718 · Cited by 1

Improving pedestrian segmentation using region proposal-based CNN semantic segmentation

Lahgazi, M. J., Argoul, P., MAST-EMGCU, U. G. E., & Hakim, A. (2023) · Mathematical Modeling and Computing · doi:10.23939/mmc2023.03.854 · Cited by 1

A Binary Neural Network with Dual Attention for Plant Disease Classification

Ma, P., Zhu, J., & Zhang, G. L. (2023) · Electronics · doi:10.3390/electronics12214431 · Cited by 2

Fast Super-Resolution Network via Dynamic Path Selection

Jia, L., Hu, Y., Tian, X., & Luo, W. (2023) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml60161.2023.10424923 · Cited by 1

Speech Scenario Adaptation and Discourse Topic Recognition on Mobile Smart Terminal

Huang, M., Li, X., Wu, S., & Chen, Y. (2015) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2015.68 · Cited by 1

Parallel Randomized Block Coordinate Descent for Neural Probabilistic Language Model with High-Dimensional Output Targets

Liu, X., Yan, J., Wang, X., & Hongyuan Zha, B. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_28 · Cited by 1

Improving the Decoding Efficiency of Deep Neural Network Acoustic Models by Cluster-Based Senone Selection

Liu, J., Ling, Z.-H., Wei, S., Hu, G., & Dai, L.-R. (2017) · Journal of Signal Processing Systems · doi:10.1007/s11265-017-1288-9 · Cited by 1

A L1-constrained DNN Acceleration Algorithm with ADMM

Liu, L., Wang, S., Wan, L., & Sun, D. (2018) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic.2018.8706287 · Cited by 1

MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model

Ma, Y., Chen, P. H., & Hsieh, C. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1529 · Cited by 1

Neural networks with block diagonal inner product layers: a look at neural network architecture through the lens of random matrices

Nesky, A., & Stout, Q. F. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04531-z · Cited by 1

Neuromorphic Systems Design by Matching Inductive Biases to Hardware Constraints

Müller, L. K., Stark, P., Offrein, B. J., & Abel, S. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00437 · Cited by 1

Investigation of Node Pruning Criteria for Neural Networks Model Compression with Non-Linear Function and Non-Uniform Network Topology

Nakadai, K., Fukumoto, Y., & Takeda, R. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383593 · Cited by 1

Model compression for on-device inference

Shi, Y., Yang, K., Yang, Z., & Zhou, Y. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823817-2.00015-2 · Cited by 2

Knowledge Distillation for Small-footprint Highway NetworksPreprint

Lu, L., Guo, M., & Renals, S. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.00892 · Cited by 1

Fast CUR Approximation of Average Matrix and ExtensionsPreprint

Pan, V. Y., Svadlenka, J., & Zhao, L. (2016) · arXiv (Cornell University) · Cited by 1

Learning SMaLL PredictorsPreprint

Garg, V., Dekel, O., & Xiao, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.02388 · Cited by 1

In-training Matrix Factorization for Parameter-frugal Neural Machine TranslationPreprint

Kaden, Z., Scao, T. L., & Olivier, R. (2019) · arXiv.org · doi:10.48550/arxiv.1910.06393 · Cited by 1

Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of ParametersPreprint

Nicodemo, N., Naithani, G., Drossos, K., Virtanen, T. I., & Saletti, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00527 · Cited by 1

Joint Architecture and Knowledge Distillation in Convolutional Neural Network for Offline Handwritten Chinese Text Recognition.Preprint

Wang, Z.-R., & Du, J. (2019) · arXiv (Cornell University) · Cited by 1

Towards Data-efficient Modeling for Wake Word SpottingPreprint

Gao, Y., Mishchenko, Y., Shah, A., Matsoukas, S., & Vitaladevuni, S. N. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06659 · Cited by 1

Q-Rater: Non-Convex Optimization for Post-Training Uniform QuantizationPreprint

Kim, B., Lee, D., Ro, Y., Jeon, Y., Kwon, S. J., Park, B., & Oh, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.01868 · Cited by 1

On the Compression of Neural Networks Using 𝓁 0 -Norm Regularization and Weight Pruning.Preprint

Oliveira, F. D. D. R., Batista, E. L. O., & Seara, R. (2021) · arXiv (Cornell University) · Cited by 1

Broadening the scope of gaussian processes for large-scale learning

Cutajar, K. (2019) · theses.fr (ABES) · doi:10.70675/2dfdf9e0zc30cz4e1fz8fa5zf02d3692856e · Cited by 2

Precision Neural Network Quantization via Learnable Adaptive Modules

Zhou, W., Yu, Z., Liu, X., Yang, J., Xiao, R., Wang, T., Tang, C., & Lv, J. (2025) · arXiv · doi:10.48550/arxiv.2504.17263

Investigation of mixture splitting concept for training linear bottlenecks of deep neural network acoustic models

Tahir, M. A., Wiesler, S., Schlüter, R., & Ney, H. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178845

A distributed architecture for fast SGD sequence discriminative training of DNN acoustic models

Saon, G. (2014) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078571

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)