【論文閱讀筆記】Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
【作者】周國睿、範穎、崔潤鵬、卞維傑、朱小強、蓋坤 【團隊名稱】阿里巴巴 【收錄】AAAI2018論文 【論文鏈接】https://arxiv.org/abs/1708.04106 深度網絡通常採用複雜的網絡結構來實現精確預測，但對於點擊率預估這樣的在線實時響應系統對響應時間要求非常嚴格。結構複雜，層數很深的深度模型不能很好的滿足嚴苛的響應時間的限制。爲了在滿足響應時間的前提下還能保證很好
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相關文章
- 1. Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net論文閱讀
- 2. 論文閱讀筆記：《ERNIE 2.0: A Continual Pre-training Framework for Language Understanding》
- 3. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 4. 【論文閱讀筆記】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 5. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 6. 論文閱讀：Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
- 7. 【論文閱讀筆記】ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 8. 論文閱讀筆記 ---《Explainable Framework for TextClassification 》
- 9. 論文閱讀：EfficientDet: Scalable and Efficient Object Detection
- 10. 【論文閱讀】Universal Domain Adaptation