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 |