zssloth / embedded-neural-network Goto Github PK

thank you for your contribution

I notice that some papers have a bold title
for example:

Learning Compression from Limited Unlabeled Data
AMC: AutoML for Model Compression and Acceleration on Mobile Devices

I just want to confirm do you mean these one should be paid attention to....?

Actually I have tried some algorithm in this area， and think AMC is a milestone.

Thanks for your work, I collect some papers in ICLR 2019 by manually. Can I help you complete this repository?

Poster Presentations:
SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
Rethinking the Value of Network Pruning
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
Dynamic Channel Pruning: Feature Boosting and Suppression
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
Slimmable Neural Networks
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
Dynamic Sparse Graph for Efficient Deep Learning
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Learning Recurrent Binary/Ternary Weights
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
Relaxed Quantization for Discretized Neural Networks
Integer Networks for Data Compression with Latent-Variable Models
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
A Systematic Study of Binary Neural Networks' Optimisation
Analysis of Quantized Models

Oral Presentations:
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks