JavaShuo
欄目
標籤
[cvpr2015]Improving training of deep neural networks via Singular Value Bounding
時間 2020-12-20
原文
原文鏈接
introduction 【training method】 Singular Value Bounding (SVB):在網絡訓練過程中,通過將權重矩陣的奇異值限制在1附近,保證權值矩陣的正交性。 Bounded Batch Normalization (BBN):用SVB的思想對BN的改進,去除了BN的ill-conditioning(ill-conditioning參考)的風險 算法 樣本:
>>阅读原文<<
相關文章
1.
Xavier——Understanding the difficulty of training deep feedforward neural networks
2.
DANN:Domain-Adversarial Training of Neural Networks
3.
Domain-Adversarial Training of Neural Networks
4.
【Deep Learning】筆記:Understanding the difficulty of training deep feedforward neural networks
5.
Improving Deep Neural Networks
6.
Deep Neural Networks for Object Detection
7.
Recognizing irregular entities in biomedical text via deep neural networks
8.
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
9.
TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
10.
[論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
更多相關文章...
•
XSLT
元素
-
XSLT 教程
•
XSLT
元素
-
XSLT 教程
•
爲了進字節跳動,我精選了29道Java經典算法題,帶詳細講解
•
三篇文章瞭解 TiDB 技術內幕——說存儲
相關標籤/搜索
networks
bounding
singular
neural
training
value
deep
flink training
for...of
for..of
Redis教程
Spring教程
0
分享到微博
分享到微信
分享到QQ
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。
最新文章
1.
NLP《詞彙表示方法(六)ELMO》
2.
必看!RDS 數據庫入門一本通(附網盤鏈接)
3.
阿里雲1C2G虛擬機【99/年】羊毛黨集合啦!
4.
10秒鐘的Cat 6A網線認證儀_DSX2-5000 CH
5.
074《從零開始學Python網絡爬蟲》小記
6.
實例12--會動的地圖
7.
聽薦 | 「談笑風聲」,一次投資圈的嘗試
8.
阿里技術官手寫800多頁PDF總結《精通Java Web整合開發》
9.
設計模式之☞狀態模式實戰
本站公眾號
歡迎關注本站公眾號,獲取更多信息
相關文章
1.
Xavier——Understanding the difficulty of training deep feedforward neural networks
2.
DANN:Domain-Adversarial Training of Neural Networks
3.
Domain-Adversarial Training of Neural Networks
4.
【Deep Learning】筆記:Understanding the difficulty of training deep feedforward neural networks
5.
Improving Deep Neural Networks
6.
Deep Neural Networks for Object Detection
7.
Recognizing irregular entities in biomedical text via deep neural networks
8.
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
9.
TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
10.
[論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
>>更多相關文章<<