1、feature scaling

feature scaling發(fā)音

英：　　美：

feature scaling中文意思翻譯

常見(jiàn)釋義：

特征縮放

feature scaling雙語(yǔ)使用場(chǎng)景

1、One feature I would like to add that would be generally useful, but critical for smartphone support is viewport and/or scaling support.───我想增加的一個(gè)非常有用的特性是視角與縮放支持，這對于智能手機來(lái)說(shuō)也非常關(guān)鍵。

2、In this letter, we focus on the feature scaling kernel where each feature individually associates with a scaling factor.───本文的討論集中在特征標度核，在此類(lèi)核函數中，每個(gè)特征都對應于一個(gè)獨立的尺度因子。

3、This is the feature that gives the topology its horizontal scaling.───正是這個(gè)特性使得該拓撲可以水平伸縮。

4、This approach allows for individual feature scaling versus scaling all five features.───該方法允許單個(gè)特性的擴展和所有五個(gè)特性的擴展。

feature scaling相似詞語(yǔ)短語(yǔ)

1、feature───vi.起重要作用；vt.特寫(xiě)；以…為特色；由…主演；n.特色，特征；容貌；特寫(xiě)或專(zhuān)題節目

2、feature feature───特征

3、fractional scaling───分數比例

4、scaling bias───標度偏差

5、scaling question───縮放問(wèn)題

6、scaling calculator───比例計算器

7、scaling factor───比例因數; 換算系數; 計數遞減率; 標度因子；[數]比例因子；[數]換算系數

8、scaling up───按比例放大；按比例增加

9、feature story───n.深度報道；n.新聞特寫(xiě)；專(zhuān)題報道

2、計算機架構頂會(huì )ISCA 2018有哪些新的AI芯片研究成果？

ISCA 是計算機架構領(lǐng)域的頂會(huì )，今年的 ISCA 不久之前在洛杉磯落幕。本次大會(huì )共收到 378 篇投稿，收錄 64 篇論文。在人工智能越來(lái)越火熱的今天，體系架構領(lǐng)域的會(huì )也有越來(lái)越多AI相關(guān)的論文，以下是清華大學(xué)微納電子系涂鋒斌博士在Github上統計的結果：

RANA: Towards Efficient Neural Acceleration with Refresh-Optimized Embedded DRAM. (THU)A Configurable Cloud-Scale DNN Processor for Real-Time AI. (Microsoft)PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine Learning Algorithms. (UIUC)Computation Reuse in DNNs by Exploiting Input Similarity. (UPC)GANAX: A Unified SIMD-MIMD Acceleration for Generative Adversarial Network. (Georiga Tech, IPM, Qualcomm, UCSD, UIUC)SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks.(UCSD, Georgia Tech, Qualcomm)UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition. (UIUC, NVIDIA)An Energy-Efficient Neural Network Accelerator based on Outlier-Aware Low Precision Computation. (Seoul National)Prediction based Execution on Deep Neural Networks. (Florida)Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks. (Georgia Tech, ARM, UCSD)Gist: Efficient Data Encoding for Deep Neural Network Training. (Michigan, Microsoft, Toronto)The Dark Side of DNN Pruning. (UPC)Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks. (Michigan)EVA^2: Exploiting Temporal Redundancy in Live Computer Vision. (Cornell)Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. (Rochester, Georgia Tech, ARM)Feature-Driven and Spatially Folded Digital Neurons for Efficient Spiking Neural Network Simulations. (POSTECH/Berkeley, Seoul National)Space-Time Algebra: A Model for Neocortical Computation. (Wisconsin)Scaling Datacenter Accelerators With Compute-Reuse Architectures. (Princeton)Enabling Scientific Computing on Memristive Accelerators. (Rochester)

其中，第一篇就是清華大學(xué)的論文，也是今年大會(huì )中國唯一被收錄的署名第一完成單位的論文。感興趣的同學(xué)可從以參閱：https://www.jiqizhixin.com/articles/2018-06-12-8