{"list":"other","total":53,"rows":[{"id":"1510.00149","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","created":"2015-10-01","cited_by_count":2329},{"id":"1803.03635","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","created":"2018-03-09","cited_by_count":2262},{"id":"2006.05525","title":"Knowledge Distillation: A Survey","created":"2020-06-09","cited_by_count":1603},{"id":"1712.05877","title":"Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference","created":"2017-12-15","cited_by_count":1593},{"id":"2103.13630","title":"A Survey of Quantization Methods for Efficient Neural Network Inference","created":"2021-03-25","cited_by_count":815},{"id":"1807.02811","title":"A Tutorial on Bayesian Optimization","created":"2018-07-08","cited_by_count":744},{"id":"1710.01878","title":"To prune, or not to prune: exploring the efficacy of pruning for model compression","created":"2017-10-05","cited_by_count":603},{"id":"1810.05270","title":"Rethinking the Value of Network Pruning","created":"2018-10-11","cited_by_count":599},{"id":"2003.03033","title":"What is the State of Neural Network Pruning?","created":"2020-03-06","cited_by_count":583},{"id":"1802.03494","title":"AMC: AutoML for Model Compression and Acceleration on Mobile Devices","created":"2018-02-10","cited_by_count":378},{"id":"1710.09282","title":"A Survey of Model Compression and Acceleration for Deep Neural Networks","created":"2017-10-23","cited_by_count":301},{"id":"2101.09671","title":"Pruning and Quantization for Deep Neural Network Acceleration: A Survey","created":"2021-01-24","cited_by_count":263},{"id":"2003.02389","title":"Comparing Rewinding and Fine-tuning in Neural Network Pruning","created":"2020-03-05","cited_by_count":240},{"id":"1903.10258","title":"MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning","created":"2019-03-25","cited_by_count":227},{"id":"1702.03044","title":"Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights","created":"2017-02-10","cited_by_count":217},{"id":"2002.08307","title":"Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning","created":"2020-02-19","cited_by_count":185},{"id":"1909.13144","title":"Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks","created":"2019-09-28","cited_by_count":148},{"id":"1804.06913","title":"Fast inference of deep neural networks in FPGAs for particle physics","created":"2018-04-16","cited_by_count":133},{"id":"1603.01025","title":"Convolutional Neural Networks using Logarithmic Data Representation","created":"2016-03-03","cited_by_count":99},{"id":"2006.10159","title":"Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors","created":"2020-06-15","cited_by_count":80},{"id":"1706.02379","title":"Training Quantized Nets: A Deeper Understanding","created":"2017-06-07","cited_by_count":75},{"id":"1905.13298","title":"DeepShift: Towards Multiplication-Less Neural Networks","created":"2019-05-30","cited_by_count":54},{"id":"2010.12785","title":"ShiftAddNet: A Hardware-Inspired Deep Network","created":"2020-10-24","cited_by_count":52},{"id":"2011.00241","title":"Methods for Pruning Deep Neural Networks","created":"2020-10-31","cited_by_count":49},{"id":"2109.15082","title":"Towards Efficient Post-training Quantization of Pre-trained Language Models","created":"2021-09-30","cited_by_count":39},{"id":"1903.11236","title":"Training Quantized Neural Networks with a Full-precision Auxiliary Module","created":"2019-03-27","cited_by_count":35},{"id":"1806.09228","title":"Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions","created":"2018-06-24","cited_by_count":33},{"id":"2102.11289","title":"Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference","created":"2021-02-22","cited_by_count":31},{"id":"2108.12659","title":"DKM: Differentiable K-Means Clustering Layer for Neural Network Compression","created":"2021-08-28","cited_by_count":31},{"id":"2203.05025","title":"Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks","created":"2022-03-09","cited_by_count":26},{"id":"2103.01542","title":"TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning","created":"2021-03-02","cited_by_count":25},{"id":"2007.07588","title":"Importance of Tuning Hyperparameters of Machine Learning Algorithms","created":"2020-07-15","cited_by_count":20},{"id":"1803.07976","title":"An Overview on Application of Machine Learning Techniques in Optical Networks","created":"2018-03-21","cited_by_count":19},{"id":"2103.08212","title":"Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems","created":"2021-03-15","cited_by_count":19},{"id":"1910.04877","title":"Bit Efficient Quantization for Deep Neural Networks","created":"2019-10-07","cited_by_count":14},{"id":"2112.06126","title":"Neural Network Quantization for Efficient Inference: A Survey","created":"2021-12-08","cited_by_count":14},{"id":"1607.04683","title":"On the efficient representation and execution of deep acoustic models","created":"2016-07-15","cited_by_count":13},{"id":"1710.06234","title":"Nonlinear Interference Mitigation via Deep Neural Networks","created":"2017-10-17","cited_by_count":13},{"id":"2109.02100","title":"Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss","created":"2021-09-05","cited_by_count":12},{"id":"2109.09113","title":"HPTQ: Hardware-Friendly Post Training Quantization","created":"2021-09-19","cited_by_count":10},{"id":"2010.14258","title":"Physics-Based Deep Learning for Fiber-Optic Communication Systems","created":"2020-10-27","cited_by_count":9},{"id":"2001.11802","title":"Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks","created":"2020-01-31","cited_by_count":8},{"id":"2102.02147","title":"Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms","created":"2021-02-03","cited_by_count":7},{"id":"2104.05081","title":"Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems","created":"2021-04-11","cited_by_count":7},{"id":"2110.08271","title":"Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations","created":"2021-10-15","cited_by_count":7},{"id":"2103.03832","title":"Performance and Complexity Analysis of bi-directional Recurrent Neural Network Models vs. Volterra Nonlinear Equalizers in Digital Coherent Systems","created":"2021-03-03","cited_by_count":6},{"id":"2105.01420","title":"Training Quantized Neural Networks to Global Optimality via Semidefinite Programming","created":"2021-05-04","cited_by_count":6},{"id":"2206.05703","title":"PAC-Net: A Model Pruning Approach to Inductive Transfer Learning","created":"2022-06-12","cited_by_count":6},{"id":"2109.14942","title":"Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls","created":"2021-09-30","cited_by_count":5},{"id":"2109.07204","title":"Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization","created":"2021-09-15","cited_by_count":4},{"id":"2202.12689","title":"Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems","created":"2022-02-25","cited_by_count":4},{"id":"1902.06939","title":"Towards Hardware Implementation of Neural Network-based Communication Algorithms","created":"2019-02-19","cited_by_count":3},{"id":"2105.09027","title":"Deep Neural Network Assisted Second-Order Perturbation-Based Nonlinearity Compensation","created":"2021-05-19","cited_by_count":1}],"next_cursor":null}