人工智能术语翻译（五）
【摘要】 @[toc] 摘要人工智能术语翻译第五部分，包括Q、R、S、T开头的词汇！ Q英文术语中文翻译常用缩写备注Q FunctionQ函数Q-LearningQ学习Q-NetworkQ网络Quadratic Loss Function平方损失函数Quadratic Programming二次规划Quadrature Pair象限对Quantized Neural Network量子化神经网络QNN...
@[toc]
摘要
人工智能术语翻译第五部分，包括Q、R、S、T开头的词汇！
Q
| 英文术语 | 中文翻译 | 常用缩写 | 备注 |
|---|---|---|---|
| Q Function | Q函数 | ||
| Q-Learning | Q学习 | ||
| Q-Network | Q网络 | ||
| Quadratic Loss Function | 平方损失函数 | ||
| Quadratic Programming | 二次规划 | ||
| Quadrature Pair | 象限对 | ||
| Quantized Neural Network | 量子化神经网络 | QNN | |
| Quantum Computer | 量子计算机 | ||
| Quantum Computing | 量子计算 | ||
| Quantum Machine Learning | 量子机器学习 | ||
| Quantum Mechanics | 量子力学 | 物理 | |
| Quasi Newton Method | 拟牛顿法 | ||
| Quasi-Concave | 拟凹 | ||
| Query | 查询 | ||
| Query Vector | 查询向量 | ||
| Query-Key-Value | 查询-键-值 | QKV | |
| Quantum Chemistry | 量子化学 | 化学 | |
| Quantum Theory | 量子理论 | 物理 |
R
| 英文术语 | 中文翻译 | 常用缩写 | 备注 |
|---|---|---|---|
| Radial Basis Function | 径向基函数 | RBF | |
| Random Access Memory | 随机访问存储 | RAM | |
| Random Field | 随机场 | ||
| Random Forest Algorithm | 随机森林算法 | ||
| Random Forest | 随机森林 | RF、RFS | 统计 |
| Random Initialization | 随机初始化 | ||
| Random Sampling | 随机采样 | 统计 | |
| Random Search | 随机搜索 | ||
| Random Subspace | 随机子空间 | ||
| Random Variable | 随机变量 | ||
| Random Walk | 随机游走 | ||
| Range | 值域 | ||
| Rank | 秩 | ||
| Ratio Matching | 比率匹配 | ||
| Raw Feature | 原始特征 | ||
| Re-Balance | 再平衡 | ||
| Re-Sampling | 重采样 | ||
| Re-Weighting | 重赋权 | ||
| Readout Function | 读出函数 | ||
| Real-Time Recurrent Learning | 实时循环学习 | RTRL | |
| Recall | 查全率/召回率 | ||
| Recall-Oriented Understudy For Gisting Evaluation | ROUGE | ||
| Receiver Operating Characteristic | 受试者工作特征 | ROC | |
| Receptive Field | 感受野 | ||
| Recirculation | 再循环 | ||
| Recognition Weight | 认知权重 | ||
| Recommender System | 推荐系统 | ||
| Reconstruction | 重构 | ||
| Reconstruction Error | 重构误差 | ||
| Rectangular Diagonal Matrix | 矩形对角矩阵 | ||
| Rectified Linear | 整流线性 | ||
| Rectified Linear Transformation | 整流线性变换 | ||
| Rectified Linear Unit | 修正线性单元/整流线性单元 | ReLU | CHAPTER 2 |
| Rectifier Network | 整流网络 | ||
| Recurrence | 循环 | ||
| Recurrent Convolutional Network | 循环卷积网络 | ||
| Recurrent Multi-Layer Perceptron | 循环多层感知器 | RMLP | |
| Recurrent Network | 循环网络 | ||
| Recurrent Neural Network | 循环神经网络 | RNN | 机器学习 |
| Recursive Neural Network | 递归神经网络 | RecNN | |
| Reducible | 可约的 | ||
| Redundant Feature | 冗余特征 | ||
| Reference Model | 参考模型 | ||
| Region | 区域 | ||
| Regression | 回归 | 统计 | |
| Regularization | 正则化 | ||
| Regularizer | 正则化项 | ||
| Reinforcement Learning | 强化学习 | RL | 机器学习 |
| Rejection Sampling | 拒绝采样 | ||
| Relation | 关系 | ||
| Relational Database | 关系型数据库 | ||
| Relative Entropy | 相对熵 | ||
| Relevant Feature | 相关特征 | ||
| Reparameterization | 再参数化/重参数化 | ||
| Reparametrization Trick | 重参数化技巧 | ||
| Replay Buffer | 经验池 | ||
| Representation | 表示 | ||
| Representation Learning | 表示学习 | ||
| Representational Capacity | 表示容量 | ||
| Representer Theorem | 表示定理 | ||
| Reproducing Kernel Hilbert Space | 再生核希尔伯特空间 | RKHS | |
| Rescaling | 再缩放 | ||
| Reservoir Computing | 储层计算 | ||
| Reset Gate | 重置门 | ||
| Residual Blocks | 残差块 | ||
| Residual Connection | 残差连接 | ||
| Residual Mapping | 残差映射 | ||
| Residual Network | 残差网络 | ResNet | |
| Residual Unit | 残差单元 | ||
| Residue Function | 残差函数 | ||
| Resolution Quotient | 归结商 | ||
| Restricted Boltzmann Machine | 受限玻尔兹曼机 | RBM | |
| Restricted Isometry Property | 限定等距性 | RIP | |
| Return | 总回报 | ||
| Reverse Correlation | 反向相关 | ||
| Reverse KL Divergence | 逆向KL散度 | ||
| Reverse Mode Accumulation | 反向模式累加 | ||
| Reversible Markov Chain | 可逆马尔可夫链 | ||
| Reward | 奖励 | ||
| Reward Function | 奖励函数 | ||
| Ridge Regression | 岭回归 | ||
| Riemann Integral | 黎曼积分 | ||
| Right Eigenvector | 右特征向量 | ||
| Right Singular Vector | 右奇异向量 | ||
| Risk | 风险 | ||
| Risk Function | 风险函数 | ||
| Robustness | 稳健性 | 计算机、机器学习 | |
| Root Node | 根结点 | ||
| Round-Off Error | 舍入误差 | ||
| Row | 行 | ||
| Rule Engine | 规则引擎 | ||
| Rule Learning | 规则学习 | ||
| Random Selection | 随机选择 | 统计 | |
| Raw Datasets | 原始数据集 | 机器学习 | |
| Root Mean Square Errors | 均方根 | RMSE | 统计 |
S
| 英文术语 | 中文翻译 | 常用缩写 | 备注 |
|---|---|---|---|
| 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 |
T
| 英文术语 | 中文翻译 | 常用缩写 | 备注 |
|---|---|---|---|
| T-Distribution Stochastic Neighbour Embedding | T分布随机近邻嵌入 | T-SNE | |
| Tabular Value Function | 表格值函数 | ||
| Tagging | 标注 | ||
| Tangent Distance | 切面距离 | ||
| Tangent Plane | 切平面 | ||
| Tangent Propagation | 正切传播 | ||
| Target | 目标 | ||
| Target Domain | 目标领域 | ||
| Taylor | 泰勒 | ||
| Taylor’s Formula | 泰勒公式 | ||
| Teacher Forcing | 强制教学 | ||
| Teacher Network | 教师网络 | ||
| Temperature | 温度 | ||
| Tempered Transition | 回火转移 | ||
| Tempering | 回火 | ||
| Temporal-Difference Learning | 时序差分学习 | ||
| Tensor | 张量 | ||
| Tensor Processing Units | 张量处理单元 | TPU | |
| Term Frequency-Inverse Document Frequency | 单词频率-逆文本频率 | TF-IDF | |
| Terminal State | 终止状态 | ||
| Test Data | 测试数据 | ||
| Test Error | 测试误差 | ||
| Test Sample | 测试样本 | ||
| Test Set | 测试集 | 机器学习 | |
| The Collider Case | 碰撞情况 | ||
| Threshold | 阈值 | 数学 | |
| Threshold Logic Unit | 阈值逻辑单元 | ||
| Threshold-Moving | 阈值移动 | ||
| Tied Weight | 捆绑权重 | ||
| Tikhonov Regularization | Tikhonov正则化 | ||
| Tiled Convolution | 平铺卷积 | ||
| Time Delay Neural Network | 时延神经网络 | TDNN | |
| Time Homogenous Markov Chain | 时间齐次马尔可夫链 | ||
| Time Step | 时间步 | ||
| Toeplitz Matrix | Toeplitz矩阵 | ||
| Token | 词元 | ||
| Tokenize | 词元化 | ||
| Tokenization | 词元化 | ||
| Tokenizer | 词元分析器 | ||
| Tolerance | 容差 | ||
| Top-Down | 自顶向下 | ||
| Topic | 话题 | ||
| Topic Model | 话题模型 | ||
| Topic Modeling | 话题分析 | ||
| Topic Vector Space | 话题向量空间 | ||
| Topic Vector Space Model | 话题向量空间模型 | ||
| Topic-Document Matrix | 话题-文本矩阵 | ||
| Topographic ICA | 地质ICA | ||
| Total Cost | 总体代价 | ||
| Trace | 迹 | ||
| Tractable | 易处理的 | ||
| Training | 训练 | ||
| Training Data | 训练数据 | ||
| Training Error | 训练误差 | ||
| Training Instance | 训练实例 | ||
| Training Sample | 训练样本 | 机器学习 | |
| Training Set | 训练集 | 机器学习 | |
| Trajectory | 轨迹 | ||
| Transcribe | 转录 | ||
| Transcription System | 转录系统 | ||
| Transductive Learning | 直推学习 | ||
| Transductive Transfer Learning | 直推迁移学习 | ||
| Transfer Learning | 迁移学习 | ||
| Transform | 变换 | ||
| Transformer | Transformer | ||
| Transformer Model | Transformer模型 | ||
| Transition | 转移 | ||
| Transition Kernel | 转移核 | ||
| Transition Matrix | 状态转移矩阵 | ||
| Transition Probability | 转移概率 | ||
| Transpose | 转置 | ||
| Transposed Convolution | 转置卷积 | ||
| Tree-Structured LSTM | 树结构的长短期记忆模型 | ||
| Treebank | 树库 | ||
| Trial | 试验 | ||
| Trial And Error | 试错 | ||
| Triangle Inequality | 三角不等式 | ||
| Triangular Cyclic Learning Rate | 三角循环学习率 | ||
| Triangulate | 三角形化 | ||
| Triangulated Graph | 三角形化图 | ||
| Trigram | 三元语法 | ||
| True Negative | 真负例 | TN | 统计 |
| True Positive | 真正例 | TP | 统计 |
| True Positive Rate | 真正例率 | TPR | 统计 |
| Truncated Singular Value Decomposition | 截断奇异值分解 | ||
| Truncation Error | 截断误差 | ||
| Turing Completeness | 图灵完备 | ||
| Turing Machine | 图灵机 | ||
| Twice-Learning | 二次学习 | ||
| Two-Dimensional Array | 二维数组 | ||
| The Global Minimum | 全局最小值 | 机器学习 | |
| Turing Test | 图灵测试 | AI，CS |
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