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Model Compression

1,718 papers

Papers per year

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128
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Papers

Online Multi-Granularity Distillation for GAN Compression ICCV 2021
CARTL: Cooperative Adversarially-Robust Transfer Learning ICML 2021
Versatile Verification of Tree Ensembles ICML 2021
Coded-InvNet for Resilient Prediction Serving Systems ICML 2021
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings ICML 2021
Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference ICML 2021
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators ICML 2021
Accurate Post Training Quantization With Small Calibration Sets ICML 2021
DeepReDuce: ReLU Reduction for Fast Private Inference ICML 2021
BASE Layers: Simplifying Training of Large, Sparse Models ICML 2021
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search ICML 2021
PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data ICML 2021
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset ICML 2021
A Probabilistic Approach to Neural Network Pruning ICML 2021
On the Predictability of Pruning Across Scales ICML 2021
1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed ICML 2021
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework ICML 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks ICML 2021
HAWQ-V3: Dyadic Neural Network Quantization ICML 2021
Federated Model Distillation with Noise-Free Differential Privacy IJCAI 2021
Isotonic Data Augmentation for Knowledge Distillation IJCAI 2021
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation IJCAI 2021
Against Membership Inference Attack: Pruning is All You Need IJCAI 2021
Decomposable-Net: Scalable Low-Rank Compression for Neural Networks IJCAI 2021
Uncertainty-aware Binary Neural Networks IJCAI 2021