conftrace_
← Core AI
Artificial Intelligence › Core AI ›

Model Compression

1,952 papers

Papers per year

6
114
489
'15
'20
'25

Papers

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning OSDI 2018
Training Quantized Nets: A Deeper Understanding NIPS 2017
On Blackbox Backpropagation and Jacobian Sensing NIPS 2017
Runtime Neural Pruning NIPS 2017
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon NIPS 2017
Structured Bayesian Pruning via Log-Normal Multiplicative Noise NIPS 2017
Compression-aware Training of Deep Networks NIPS 2017
Reliably Learning the ReLU in Polynomial Time COLT 2017
LCNN: Lookup-Based Convolutional Neural Network CVPR 2017
Unfolding and Shrinking Neural Machine Translation Ensembles EMNLP 2017
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices ICML 2017
Variational Dropout Sparsifies Deep Neural Networks ICML 2017
Node Pruning Based on Entropy of Weights and Node Activity for Small-Footprint Acoustic Model Based on Deep Neural Networks INTERSPEECH 2017
Compressed Time Delay Neural Network for Small-Footprint Keyword Spotting INTERSPEECH 2017
Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization COLING 2016
Blockout: Dynamic Model Selection for Hierarchical Deep Networks CVPR 2016
Model Compression Applied to Small-Footprint Keyword Spotting INTERSPEECH 2016
Compressing Bayes Net CPTs with Persistent Leaky Causes PGM 2016
Tensorizing Neural Networks NIPS 2015
Structured Transforms for Small-Footprint Deep Learning NIPS 2015
Learning both Weights and Connections for Efficient Neural Network NIPS 2015
Deep Fried Convnets ICCV 2015
An Exploration of Parameter Redundancy in Deep Networks With Circulant Projections ICCV 2015
Compressing Neural Networks with the Hashing Trick ICML 2015
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation NIPS 2014