S-Fold Cross Validation |
S 折交叉验证 |
|
|
Saccade |
扫视 |
|
|
Saddle Point |
鞍点 |
|
|
Saddle-Free Newton Method |
无鞍牛顿法 |
|
|
Saliency Map |
显著图 |
|
|
Saliency-Based Attention |
基于显著性的注意力 |
|
|
Same |
相同 |
|
|
Sample |
样本 |
|
|
Sample Complexity |
样本复杂度 |
|
|
Sample Mean |
样本均值 |
|
|
Sample Space |
样本空间 |
|
|
Sample Variance |
样本方差 |
|
|
Sampling |
采样 |
|
|
Sampling Method |
采样法 |
|
|
Saturate |
饱和 |
|
|
Saturating Function |
饱和函数 |
|
|
Scalar |
标量 |
|
|
Scale Invariance |
尺度不变性 |
|
|
Scatter Matrix |
散布矩阵 |
|
|
Scheduled Sampling |
计划采样 |
|
|
Score |
得分 |
|
|
Score Function |
评分函数 |
|
|
Score Matching |
分数匹配 |
|
|
Second Derivative |
二阶导数 |
|
|
Second Derivative Test |
二阶导数测试 |
|
|
Second Layer |
第二层 |
|
|
Second-Order Method |
二阶方法 |
|
|
Selective Attention |
选择性注意力 |
|
|
Selective Ensemble |
选择性集成 |
|
|
Self Information |
自信息 |
|
|
Self-Attention |
自注意力 |
|
|
Self-Attention Model |
自注意力模型 |
|
|
Self-Contrastive Estimation |
自对比估计 |
|
|
Self-Driving |
自动驾驶 |
|
|
Self-Gated |
自门控 |
|
|
Self-Organizing Map |
自组织映射网 |
SOM |
|
Self-Taught Learning |
自学习 |
|
|
Self-Training |
自训练 |
|
|
Semantic Gap |
语义鸿沟 |
|
|
Semantic Hashing |
语义哈希 |
|
|
Semantic Segmentation |
语义分割 |
|
|
Semantic Similarity |
语义相似度 |
|
|
Semi-Definite Programming |
半正定规划 |
|
|
Semi-Naive Bayes Classifiers |
半朴素贝叶斯分类器 |
|
|
Semi-Restricted Boltzmann Machine |
半受限玻尔兹曼机 |
|
|
Semi-Supervised |
半监督 |
|
|
Semi-Supervised Clustering |
半监督聚类 |
|
|
Semi-Supervised Learning |
半监督学习 |
|
|
Semi-Supervised Support Vector Machine |
半监督支持向量机 |
S3VM |
|
Sentiment Analysis |
情感分析 |
|
|
Separable |
可分离的 |
|
|
Separate |
分离的 |
|
|
Separating Hyperplane |
分离超平面 |
|
|
Separation |
分离 |
|
|
Sequence Labeling |
序列标注 |
|
|
Sequence To Sequence Learning |
序列到序列学习 |
Seq2Seq |
|
Sequence-To-Sequence |
序列到序列 |
Seq2Seq |
|
Sequential Covering |
序贯覆盖 |
|
|
Sequential Minimal Optimization |
序列最小最优化 |
SMO |
|
Sequential Model-Based Optimization |
时序模型优化 |
SMBO |
|
Sequential Partitioning |
顺序分区 |
|
|
Setting |
情景 |
|
|
Shadow Circuit |
浅度回路 |
|
|
Shallow Learning |
浅层学习 |
|
|
Shannon Entropy |
香农熵 |
|
|
Shannons |
香农 |
|
|
Shaping |
塑造 |
|
|
Sharp Minima |
尖锐最小值 |
|
|
Shattering |
打散 |
|
|
Shift Invariance |
平移不变性 |
|
|
Short-Term Memory |
短期记忆 |
|
|
Shortcut Connection |
直连边 |
|
|
Shortlist |
短列表 |
|
|
Siamese Network |
孪生网络 |
|
|
Sigmoid |
Sigmoid(一种激活函数) |
|
统计 |
Sigmoid Belief Network |
Sigmoid信念网络 |
SBN |
|
Sigmoid Curve |
S 形曲线 |
|
|
Sigmoid Function |
Sigmoid函数 |
|
|
Sign Function |
符号函数 |
|
|
Signed Distance |
带符号距离 |
|
|
Similarity |
相似度 |
|
|
Similarity Measure |
相似度度量 |
|
|
Simple Cell |
简单细胞 |
|
|
Simple Recurrent Network |
简单循环网络 |
SRN |
|
Simple Recurrent Neural Network |
简单循环神经网络 |
S-RNN |
|
Simplex |
单纯形 |
|
|
Simulated Annealing |
模拟退火 |
|
统计、机器学习 |
Simultaneous Localization And Mapping |
即时定位与地图构建 |
SLAM |
|
Single Component Metropolis-Hastings |
单分量Metropolis-Hastings |
|
|
Single Linkage |
单连接 |
|
|
Singular |
奇异的 |
|
|
Singular Value |
奇异值 |
|
|
Singular Value Decomposition |
奇异值分解 |
SVD |
|
Singular Vector |
奇异向量 |
|
|
Size |
大小 |
|
|
Skip Connection |
跳跃连接 |
|
|
Skip-Gram Model |
跳元模型 |
|
|
Skip-Gram Model With Negative Sampling |
跳元模型加负采样 |
|
|
Slack Variable |
松弛变量 |
|
|
Slow Feature Analysis |
慢特征分析 |
|
|
Slowness Principle |
慢性原则 |
|
|
Smoothing |
平滑 |
|
|
Smoothness Prior |
平滑先验 |
|
|
Soft Attention Mechanism |
软性注意力机制 |
|
|
Soft Clustering |
软聚类 |
|
|
Soft Margin |
软间隔 |
|
|
Soft Margin Maximization |
软间隔最大化 |
|
|
Soft Target |
软目标 |
|
|
Soft Voting |
软投票 |
|
|
Softmax |
Softmax/软最大化 |
|
|
Softmax Function |
Softmax函数/软最大化函数 |
|
统计、机器学习 |
Softmax Regression |
Softmax回归/软最大化回归 |
|
|
Softmax Unit |
Softmax单元/软最大化单元 |
|
|
Softplus |
Softplus |
|
|
Softplus Function |
Softplus函数 |
|
|
Source Domain |
源领域 |
|
|
Span |
张成子空间 |
|
|
Sparse |
稀疏 |
|
|
Sparse Activation |
稀疏激活 |
|
|
Sparse Auto-Encoder |
稀疏自编码器 |
|
|
Sparse Coding |
稀疏编码 |
|
|
Sparse Connectivity |
稀疏连接 |
|
|
Sparse Initialization |
稀疏初始化 |
|
|
Sparse Interactions |
稀疏交互 |
|
|
Sparse Representation |
稀疏表示 |
|
|
Sparse Weights |
稀疏权重 |
|
|
Sparsity |
稀疏性 |
|
|
Specialization |
特化 |
|
|
Spectral Clustering |
谱聚类 |
|
|
Spectral Radius |
谱半径 |
|
|
Speech Recognition |
语音识别 |
|
|
Sphering |
Sphering |
|
|
Spike And Slab |
尖峰和平板 |
|
|
Spike And Slab RBM |
尖峰和平板RBM |
|
|
Spiking Neural Nets |
脉冲神经网络 |
|
|
Splitting Point |
切分点 |
|
|
Splitting Variable |
切分变量 |
|
|
Spurious Modes |
虚假模态 |
|
|
Square |
方阵 |
|
|
Square Loss |
平方损失 |
|
|
Squared Euclidean Distance |
欧氏距离平方 |
|
|
Squared Exponential |
平方指数 |
|
|
Squashing Function |
挤压函数 |
|
|
Stability |
稳定性 |
|
|
Stability-Plasticity Dilemma |
可塑性-稳定性窘境 |
|
|
Stable Base Learner |
稳定基学习器 |
|
|
Stacked Auto-Encoder |
堆叠自编码器 |
SAE |
|
Stacked Deconvolutional Network |
堆叠解卷积网络 |
SDN |
|
Stacked Recurrent Neural Network |
堆叠循环神经网络 |
SRNN |
|
Standard Basis |
标准基 |
|
|
Standard Deviation |
标准差 |
|
|
Standard Error |
标准差 |
|
|
Standard Normal Distribution |
标准正态分布 |
|
|
Standardization |
标准化 |
|
|
State |
状态 |
|
|
State Action Reward State Action |
SARSA算法 |
SARSA |
|
State Sequence |
状态序列 |
|
|
State Space |
状态空间 |
|
|
State Value Function |
状态值函数 |
|
|
State-Action Value Function |
状态-动作值函数 |
|
|
Statement |
声明 |
|
|
Static Computational Graph |
静态计算图 |
|
|
Static Game |
静态博弈 |
|
|
Stationary |
平稳的 |
|
|
Stationary Distribution |
平稳分布 |
|
|
Stationary Point |
驻点 |
|
|
Statistic Efficiency |
统计效率 |
|
|
Statistical Learning |
统计学习 |
|
|
Statistical Learning Theory |
统计学习理论 |
|
|
Statistical Machine Learning |
统计机器学习 |
|
|
Statistical Relational Learning |
统计关系学习 |
|
|
Statistical Simulation Method |
统计模拟方法 |
|
|
Statistics |
统计量 |
|
|
Status Feature Function |
状态特征函数 |
|
|
Steepest Descent |
最速下降法 |
|
|
Step Decay |
阶梯衰减 |
|
|
Stochastic |
随机 |
|
|
Stochastic Curriculum |
随机课程 |
|
|
Stochastic Dynamical System |
随机动力系统 |
|
|
Stochastic Gradient Ascent |
随机梯度上升 |
|
|
Stochastic Gradient Descent |
随机梯度下降 |
|
|
Stochastic Gradient Descent With Warm Restarts |
带热重启的随机梯度下降 |
SGDR |
|
Stochastic Matrix |
随机矩阵 |
|
|
Stochastic Maximum Likelihood |
随机最大似然 |
|
|
Stochastic Neighbor Embedding |
随机近邻嵌入 |
|
|
Stochastic Neural Network |
随机神经网络 |
SNN |
|
Stochastic Policy |
随机性策略 |
|
|
Stochastic Process |
随机过程 |
|
|
Stop Words |
停用词 |
|
|
Stratified Sampling |
分层采样 |
|
|
Stream |
流 |
|
|
Stride |
步幅 |
|
|
String Kernel Function |
字符串核函数 |
|
|
Strong Classifier |
强分类器 |
|
|
Strong Duality |
强对偶性 |
|
|
Strongly Connected Graph |
强连通图 |
|
|
Strongly Learnable |
强可学习 |
|
|
Structural Risk |
结构风险 |
|
|
Structural Risk Minimization |
结构风险最小化 |
SRM |
|
Structure Learning |
结构学习 |
|
|
Structured Learning |
结构化学习 |
|
|
Structured Probabilistic Model |
结构化概率模型 |
|
|
Structured Variational Inference |
结构化变分推断 |
|
|
Student Network |
学生网络 |
|
|
Sub-Optimal |
次最优 |
|
|
Subatomic |
亚原子 |
|
|
Subsample |
子采样 |
|
|
Subsampling |
下采样 |
|
|
Subsampling Layer |
子采样层 |
|
|
Subset Evaluation |
子集评价 |
|
|
Subset Search |
子集搜索 |
|
|
Subspace |
子空间 |
|
|
Substitution |
置换 |
|
|
Successive Halving |
逐次减半 |
|
|
Sum Rule |
求和法则 |
|
|
Sum-Product |
和积 |
|
|
Sum-Product Network |
和-积网络 |
|
|
Super-Parent |
超父 |
|
|
Supervised |
监督 |
|
|
Supervised Learning |
监督学习 |
|
机器学习 |
Supervised Learning Algorithm |
监督学习算法 |
|
|
Supervised Model |
监督模型 |
|
|
Supervised Pretraining |
监督预训练 |
|
|
Support Vector |
支持向量 |
|
统计、机器学习 |
Support Vector Expansion |
支持向量展式 |
|
|
Support Vector Machine |
支持向量机 |
SVM |
统计、机器学习 |
Support Vector Regression |
支持向量回归 |
SVR |
统计、机器学习 |
Surrogat Loss |
替代损失 |
|
|
Surrogate Function |
替代函数 |
|
|
Surrogate Loss Function |
代理损失函数 |
|
|
Symbol |
符号 |
|
|
Symbolic Differentiation |
符号微分 |
|
|
Symbolic Learning |
符号学习 |
|
|
Symbolic Representation |
符号表示 |
|
|
Symbolism |
符号主义 |
|
|
Symmetric |
对称 |
|
|
Symmetric Matrix |
对称矩阵 |
|
|
Synonymy |
多词一义性 |
|
|
Synset |
同义词集 |
|
|
Synthetic Feature |
合成特征 |
|
|
Scaling |
缩放 |
|
图像处理 |
Simulation |
仿真 |
|
|
Sequence-Function |
序列-功能 |
|
|
Set Prediction |
集合预测 |
|
|
stuff categories |
填充类别 |
|
全景分割中,天空、墙面、地面等不规则的类别 |