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Model Compression

1,785 papers

Papers per year

7
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419
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Papers

Robust Quantization: One Model to Rule Them All NIPS 2020
Bayesian Bits: Unifying Quantization and Pruning NIPS 2020
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers NIPS 2020
Pruning neural networks without any data by iteratively conserving synaptic flow NIPS 2020
Rotated Binary Neural Network NIPS 2020
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts NIPS 2020
SCOP: Scientific Control for Reliable Neural Network Pruning NIPS 2020
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning NIPS 2020
Winning the Lottery with Continuous Sparsification NIPS 2020
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training NIPS 2020
Kernel Based Progressive Distillation for Adder Neural Networks NIPS 2020
Understanding and Exploring the Network with Stochastic Architectures NIPS 2020
Calibrating CNNs for Lifelong Learning NIPS 2020
Sparse Weight Activation Training NIPS 2020
Pruning Filter in Filter NIPS 2020
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search NIPS 2020
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks NIPS 2020
Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning AAAI 2020
Indirect Stochastic Gradient Quantization and Its Application in Distributed Deep Learning AAAI 2020
Few Shot Network Compression via Cross Distillation AAAI 2020
Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding AAAI 2020
Information-Theoretic Understanding of Population Risk Improvement with Model Compression AAAI 2020
AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection AAAI 2020
InstaNAS: Instance-Aware Neural Architecture Search AAAI 2020
Distilling Portable Generative Adversarial Networks for Image Translation AAAI 2020