【Paper Readind】:Ternary weight networks
Ternary weight networks 2016 NIPS 中科院 Fengfu Li and Bo Zhang 摘要 本文引入了权值为+1、0和-1的三元神经网络(TWNs)。最小化全精度(float或者double)的权重与三元权重之间的欧几里得距离。此外，对基于阈值的三值化函数进行了优化，能够更快速、且更易于计算地得到近似解。TWNs比最近提出的二值网络具有更强的表达能力，并且比后者
相关文章
- 1. Ternary weight networks
- 2. Ternary weight networks论文解读
- 3. Ternary weight networks 论文笔记
- 4. Ternary Neural Networks with Fine-Grained Quantization
- 5. Paper Notes: Graph Attention Networks
- 6. Paper Reading: Slimmable Neural Networks
- 7. Paper Reading《Relation Networks for Object Detection》
- 8. [paper]ADVERSARIAL REPROGRAMMING OF NEURAL NETWORKS
- 9. 【Paper】TextCNN：Convolutional Neural Networks for Sentence Classification
- 10. 三值网络--Trained Ternary Quantization
- 更多相关文章...
- • XSL-FO table-footer 对象 - XSL-FO 教程
- • XSL-FO table-header 对象 - XSL-FO 教程
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Ternary weight networks
- 2. Ternary weight networks论文解读
- 3. Ternary weight networks 论文笔记
- 4. Ternary Neural Networks with Fine-Grained Quantization
- 5. Paper Notes: Graph Attention Networks
- 6. Paper Reading: Slimmable Neural Networks
- 7. Paper Reading《Relation Networks for Object Detection》
- 8. [paper]ADVERSARIAL REPROGRAMMING OF NEURAL NETWORKS
- 9. 【Paper】TextCNN：Convolutional Neural Networks for Sentence Classification
- 10. 三值网络--Trained Ternary Quantization