| 1 | 2D Qsar Models | 二维定量构效关系模型 |
| 2 | 3D Cartesian | 三维笛卡尔(坐标) |
| 3 | 3D Conformation | 三维构象 |
| 4 | 3D Grids | 三维(坐标)网格 |
| 5 | 3D Qsar Models | 三维定量构效关系模型 |
| 6 | Aberration-Corrected | 像差矫正 |
| 7 | Accuracy | 准确率 |
| 8 | Activation Function | 激活函数 |
| 9 | Active Learning | 主动学习 |
| 10 | Active Machine Learning | 主动机器学习 |
| 11 | Adaptive Fuzzy Neural Network | 自适应模糊神经网络 |
| 12 | Adaptive Neuro Fuzzy Inference System | 自适应神经模糊推理系统 |
| 13 | Adaptive Sampling | 自适应采样 |
| 14 | Admet Evaluation | 毒性评估 |
| 15 | Alexnet | AlexNet |
| 16 | Alphago | 阿尔法狗 |
| 17 | Approximate Probabilistic Models | 近似概率模型 |
| 18 | Area Under ROC Curve | AUC(ROC曲线下方面积,度量分类模型好坏的标准) |
| 19 | Artificial Intelligence | 人工智能 |
| 20 | Artificial Neural Network | 人工神经网络 |
| 21 | Artificial Neurons | 人工神经元 |
| 22 | Artificial Synapses | 人工突触 |
| 23 | Attention | 注意力 |
| 24 | Attention-Based | 基于注意力(机制)的 |
| 25 | Automating Synthetic Planning | 自动化综合规划 |
| 26 | Automation | 自动化 |
| 27 | Autonomous Decision-Making | 自主决策 |
| 28 | B-Clustering Algorithms | B树聚类算法 |
| 29 | Back Propagation | 反向传播 |
| 30 | Bagging | 袋装 |
| 31 | Balanced Accuracy | 平衡精度 |
| 32 | Bandgap Energy | 带隙能量 |
| 33 | Baseline | 基准 |
| 34 | Baseline Test | 基准测试 |
| 35 | Basin Hopping | 盆地跳跃(算法) |
| 36 | Bayesian Approach | 贝叶斯方法 |
| 37 | Bayesian Induction | 贝叶斯归纳 |
| 38 | Bayesian Inference | 贝叶斯推断 |
| 39 | Bayesian Mcmc Methods | 贝叶斯马尔可夫链蒙特卡洛方法 |
| 40 | Bayesian Methods | 贝叶斯方法 |
| 41 | Bayesian Molecular | 贝叶斯分子(设计方法) |
| 42 | Bayesian Network | 贝叶斯网/贝叶斯网络 |
| 43 | Bayesian Prior | 贝叶斯先验 |
| 44 | Bayesian Program Learning | 贝叶斯程序学习 |
| 45 | Bayesian Regularized Neural Network | 贝叶斯正则化神经网络 |
| 46 | Beam-Scanning | 波束扫描 |
| 47 | Bernoulli Distribution | 伯努利分布 |
| 48 | Best Separates | 最优分离 |
| 49 | Bias | 偏差/偏置 |
| 50 | Biased | 有偏 |
| 51 | Biased Dataset | 有偏数据集 |
| 52 | Bit Collisions | 字节碰撞/冲突 |
| 53 | Black Box | 黑盒子 |
| 54 | Black-Box Attack | 黑盒攻击 |
| 55 | Bonding Environments | 成键环境 |
| 56 | Bonferroni Correction | 邦弗朗尼校正 |
| 57 | Boosting | Boosting(一种模型训练加速方式) |
| 58 | Bootstrap Aggregation | 引导聚合 |
| 59 | Bottom-Up | 自下而上 |
| 60 | Broyden–Fletcher–Goldfarb–Shanno | BFGS(算法) |
| 61 | Buchwald−Hartwig Cross-Coupling | Buchwald–Hartwig 偶联(反应) |
| 62 | C4.5 Algorithm | C4.5 算法 |
| 63 | CASP | 国际蛋白质结构预测竞赛 |
| 64 | Calculation Uncertainties | 计算不确定性 |
| 65 | Canonical Ml Methods | 经典机器学习方法 |
| 66 | Cartesian Distance Vector | 笛卡尔距离向量 |
| 67 | Categorical Data | 分类数据 |
| 68 | Categorization Algorithms | 分类算法 |
| 69 | ChemDataExtractor | 化学数据提取器 |
| 70 | Chi-Squared | 卡方(分布) |
| 71 | Classification | 分类 |
| 72 | Classification And Regression Tree | 分类与回归树 |
| 73 | Classification Model | 分类模型 |
| 74 | Cluster | 簇 |
| 75 | Cluster Resolution Feature Selection | 聚类分辨率特征选择 |
| 76 | Cluster-Based Splitting | 基于聚类的分离方法 |
| 77 | Clustering Methods | 聚类方法 |
| 78 | Code Pipeline | 代码流水线 |
| 79 | Coefficient of Determination | 决定系数 |
| 80 | Combined Gradient | 组合梯度(算法) |
| 81 | Complex Data | 复合数据 |
| 82 | Computational Cost | 计算成本 |
| 83 | Computational Optimisation | 计算优化 |
| 84 | Computational Science | 计算科学 |
| 85 | Computational Toxicology | 计算毒理学 |
| 86 | Computer Science | 计算机科学 |
| 87 | Computer Simulations | 计算机模拟 |
| 88 | Computer Vision | 计算机视觉 |
| 89 | Computer-Aided | 计算机辅助 |
| 90 | Confusion Matrix | 混淆矩阵 |
| 91 | Conjugate Gradient | 共轭梯度 |
| 92 | Constraint | 约束 |
| 93 | Core-Loss Spectrum | (电子能量损失谱中的)高能区域 |
| 94 | Correlation | 相关系数 |
| 95 | Cost Function | 代价函数 |
| 96 | Coulomb Matrix | 库仑矩阵 |
| 97 | Coupled-Cluster Predictions | 耦合簇预测 |
| 98 | Covariance | 协方差 |
| 99 | Covariance Matrix | 协方差矩阵 |
| 100 | Cross-Validated Coefficient of Determination | 交叉验证的决定系数 |
| 101 | Cross-Validation | 交叉验证 |
| 102 | Crowd-Sourcing | 众包 |
| 103 | Cut-Points | 切点 |
| 104 | Cutoff Radial Function | 截断径向函数 |
| 105 | DE Algorithm | 差分进化算法 |
| 106 | DFT Calculations | DFT计算 |
| 107 | Data Augmentation | 数据增强 |
| 108 | Data Availability | 数据可用性 |
| 109 | Data Cleaning | 数据清洗 |
| 110 | Data Collection | 数据采集 |
| 111 | Data Considerations | 数据注意事项 |
| 112 | Data Curation | 数据监管 |
| 113 | Data Disparity | 数据差异 |
| 114 | Data Dredging | 数据挖掘 |
| 115 | Data Imputation | 数据填补 |
| 116 | Data Labels | 数据标签 |
| 117 | Data Leakage | 数据泄露 |
| 118 | Data Mining | 数据挖掘 |
| 119 | Data Pre-Processing | 数据预处理 |
| 120 | Data Processing | 数据处理 |
| 121 | Data Quality | 数据质量 |
| 122 | Data Reduction | 数据缩减 |
| 123 | Data Representation | 数据表示 |
| 124 | Data Selection | 数据选择 |
| 125 | Data Set | 数据集 |
| 126 | Data Sources | 数据源 |
| 127 | Data Splitting | 数据拆分 |
| 128 | Data Transformation | 数据转换 |
| 129 | Data-Driven | 数据驱动 |
| 130 | Data-Driven Decision-Making | 数据驱动的决策 |
| 131 | Data-Driven Methods | 数据驱动的方法 |
| 132 | Data-Driven Spectral Analysis | 数据驱动的光谱分析 |
| 133 | Data-Mining | 数据挖掘 |
| 134 | Database | 数据库 |
| 135 | Decision Tree | 决策树 |
| 136 | Deep Learning | 深度学习 |
| 137 | Deep Neural Network | 深度神经网络 |
| 138 | Deep Reinforcement Learning | 深度强化学习 |
| 139 | Deeplift | DeepLift模型 |
| 140 | Dendrogram | 树状图 |
| 141 | Density Functional Theory | 密度泛函理论 |
| 142 | Density-Based Spatial Clustering Of Applications With Noise | DBSCAN密度聚类 |
| 143 | Descriptor | 描述符 |
| 144 | Dice Similarity | 戴斯相似度 |
| 145 | Differential Evolution | 差分进化 |
| 146 | Dimension Reduction | 降维 |
| 147 | Dimensionality Reduction | 降维 |
| 148 | Dimensionality Reduction Algorithm | 降维算法 |
| 149 | Direct Neural Network Modeling | 正向神经网络建模 |
| 150 | Discrete Manner | 离散方式 |
| 151 | Discrete Quanta | 离散量子 |
| 152 | Discretization | 离散化 |
| 153 | Distillation | 蒸馏 |
| 154 | Dynamic Datasets | 动态数据集 |
| 155 | Dynamic Filter Networks | 动态过滤网络 |
| 156 | Dynamic Sampling | 动态采样 |
| 157 | Dynamics Simulations | 动力学模拟 |
| 158 | Eigenfunction | 特征函数 |
| 159 | Electronegativity | 电负性 |
| 160 | Elman | 埃尔曼 |
| 161 | Empirical Models | 经验模型 |
| 162 | Encoder-Decoder | 编码器-解码器(模型) |
| 163 | Energy Derivatives | 能源衍生品 |
| 164 | Energy Potentials | 能量潜力 |
| 165 | Ensemble Methods | 集成方法 |
| 166 | Entity Normalisation | 实体规范化 |
| 167 | Error Function | 误差函数 |
| 168 | Estimator | 估计/估计量 |
| 169 | Ethical Considerations | 道德考虑 |
| 170 | Euclidean Distances | 欧几里得距离 |
| 171 | Evolutionary Algorithms | 进化算法 |
| 172 | Evolutionary Method | 进化方法 |
| 173 | Exchange–Correlation | 交换关联(的能量/泛函) |
| 174 | Excited-State Potentials | 激发态能量 |
| 175 | Expected Reduction In Distortion | 符合预期的失真减少 |
| 176 | Experimental Validation Data | 实验验证数据 |
| 177 | Expert Systems | 专家系统 |
| 178 | Extended-Connectivity Circular Fingerprint | 扩展连接环形指纹 |
| 179 | Extraction Techniques | 提取技术 |
| 180 | FAIR Data Principles | FAIR数据原则 |
| 181 | Faber-Christensen-Huang-Lilienfeld | Faber-Christensen-Huang-Lilienfeld |
| 182 | Facial Recognition | 面部识别 |
| 183 | False Negatives | 假阴性 |
| 184 | False Positives | 假阳性 |
| 185 | Fchl Representation | Fchl 表示 |
| 186 | Feature Binarization | 特征二值化 |
| 187 | Feature Engineering | 特征工程 |
| 188 | Feature Extraction | 特征抽取 |
| 189 | Feature Selection | 特征选择 |
| 190 | Feature Transform | 特征变换 |
| 191 | Feature Vectors | 特征向量 |
| 192 | Features | 特征 |
| 193 | Feed Back | 反馈 |
| 194 | Feed-Forward Neural Networks | 前馈神经网络 |
| 195 | Feedback Structure | 反馈结构 |
| 196 | Feedforward Neural Network | 前馈神经网络 |
| 197 | Final Evaluation | 最终评估 |
| 198 | Findable | Accessible |
| 199 | First-Principles | 第一性原理 |
| 200 | Flow Rate | 流速 |
| 201 | Forward Cross-Validation | 前向交叉验证 |
| 202 | Forward Prediction | 前向预测 |
| 203 | Forward Reaction Prediction | 前向反应预测 |
| 204 | Fuzzy Logic | 模糊逻辑 |
| 205 | Fuzzy Neural Networks | 模糊神经网络 |
| 206 | Ga-Based Approaches | 基于遗传算法的方法 |
| 207 | Garbage In | Garbage Out |
| 208 | Gas-Phase Networks | 气相网络 |
| 209 | Gaussian Distribution | 高斯分布 |
| 210 | Gaussian Kernel Function | 高斯核函数 |
| 211 | Gaussian Kernels | 高斯核 |
| 212 | Gaussian Mixtures | 高斯混合(模型) |
| 213 | Gaussian Process | 高斯过程 |
| 214 | Gaussian Process Regression | 高斯过程回归 |
| 215 | Gaussian-Type Structure Descriptors | 高斯型结构描述符 |
| 216 | General Intelligence | 通用智能 |
| 217 | Generalized Gradient Approximation | 广义梯度近似 |
| 218 | Generative Adversarial Networks | 生成对抗网络 |
| 219 | Generative Modeling | 生成式建模 |
| 220 | Genetic Algorithm | 遗传算法 |
| 221 | Gradient Boosting Decision Tree | 梯度提升决策树 |
| 222 | Gradient Descent | 梯度下降 |
| 223 | Gradient-Based | 基于梯度的 |
| 224 | Grain-Surface Networks | 粒面网络 |
| 225 | Graph Convolutional | 图卷积 |
| 226 | Graph Models | 图模型 |
| 227 | Graph Neural Networks | 图神经网络 |
| 228 | Graph-Based | 基于图形 |
| 229 | Graph-Based Models | 基于图的模型 |
| 230 | Graph-Based Neural Networks | 基于图的神经网络 |
| 231 | Graph-Based Representation | 基于图的表示 |
| 232 | Graph-Convolutional Neural Network | 图卷积神经网络 |
| 233 | Graphics Processing Units | 图形处理器 |
| 234 | Gravimetric Polymerization Degree | 比重聚合度 |
| 235 | Grid Search | 网格搜索 |
| 236 | Ground Truth | 真实值 |
| 237 | Hamiltonian Matrix | 哈密顿矩阵 |
| 238 | Hamiltonian Operator | 哈密顿算符 |
| 239 | Heterogeneous Data | 异构数据 |
| 240 | Hidden Layers | 隐藏层 |
| 241 | High Data Throughput | 高数据吞吐量 |
| 242 | High Throughput | 高通量 |
| 243 | High Throughput Screening | 高通量筛选 |
| 244 | High Variance Models | 高方差模型 |
| 245 | High-Dimensional Data | 高维数据 |
| 246 | High-Dimensional NN | 高维神经网络 |
| 247 | High-Dimensional Objects | 高维对象 |
| 248 | High-Throughput | 高通量 |
| 249 | Higher-Dimensional Space | 高维空间 |
| 250 | Higher-Dimensional Spectral Space | 高维光谱空间 |
| 251 | Homogenization | 同质化 |
| 252 | Homomorphic Encryption | 同态加密 |
| 253 | Human Face Recognition | 人脸识别 |
| 254 | Human-Encoded | 人工编码的 |
| 255 | Hybrid Model | 混合模型 |
| 256 | Hybrid Technique | 混合技术 |
| 257 | Hybrid-Neural Model | 混合神经模型 |
| 258 | Hyperparameter Opimization | 超参数优化 |
| 259 | Hyperparameters | 超参数 |
| 260 | Hyperplane | 超平面 |
| 261 | Hyperplanes Separate | 超平面分离 |
| 262 | Id3 Algorithm | Id3 算法 |
| 263 | Image And Speech Recognition | 图像和语音识别 |
| 264 | Image Classification | 图像分类 |
| 265 | Image Classifier | 图像分类器 |
| 266 | Image Recognition | 图像识别 |
| 267 | Inductive Bias | 归纳偏好 |
| 268 | Information Gain | 信息增益 |
| 269 | Information Gain Ratio | 信息增益比 |
| 270 | Informative Priors | 信息先验 |
| 271 | Input-Output Pairs | 输入输出对 |
| 272 | Instance-Based | 基于实例的 |
| 273 | Intelligent Machine | 智能机器 |
| 274 | Intermediate Neurons | 中间神经元 |
| 275 | Internet Of Things | 物联网 |
| 276 | Interpolation Coordinate | 插值坐标 |
| 277 | Interpretability | 可解释性 |
| 278 | Inverse Neural Modeling | 逆神经建模 |
| 279 | Inverse Neural Network Modeling | 逆神经网络建模 |
| 280 | Iteration | 迭代 |
| 281 | Iterative Learning | 迭代学习 |
| 282 | Joint Distribution | 联合分布 |
| 283 | Jordan-Elman Neural Networks | Jordan-Elman 神经网络 |
| 284 | K Clusters | K聚类 |
| 285 | K Nearest Points | K 最近点 |
| 286 | K-1 Folds | K-1 折 |
| 287 | K-Edge (O-K Edge) | K-边缘(O-K 边缘) |
| 288 | K-Fold Cross Validation | k 折交叉验证 |
| 289 | K-Means | K-均值 |
| 290 | K-Means Clustering | k-均值聚类 |
| 291 | K-Nearest Neighbor Method | k-近邻 |
| 292 | KNN Model | K 近邻模型 |
| 293 | Kendall’S Tau | 肯德尔等级相关系数 |
| 294 | Kernel Method | 核方法 |
| 295 | Kernel Ridge Regression | 核岭回归 |
| 296 | Kernel Trick | 核技巧 |
| 297 | Kernels | 内核 |
| 298 | Kinetic Curve | 动力学曲线 |
| 299 | Knowledge Extraction | 知识提取 |
| 300 | Knowledge Gradient | 知识梯度 |
| 301 | L1 And L2 Regularization | L1与L2正则化 |
| 302 | LBP | 局部二值模式 |
| 303 | Label | 标签/标记 |
| 304 | Laboratory Level | 实验室级别 |
| 305 | Language Processing | 语言处理 |
| 306 | Laplacian Prior | 拉普拉斯先验 |
| 307 | Large-Scale Data Storage | 大规模数据存储 |
| 308 | Lasers | 激光器 |
| 309 | Lasso Regression | 拉索回归 |
| 310 | Lazy Learning | 懒惰学习 |
| 311 | Least Absolute Shrinkage And Selection Operator | Lasso回归 |
| 312 | Least Square Support Vector Machine | 最小二乘支持向量机 |
| 313 | Ligand-Field | 配位场 |
| 314 | Linear | 线性的 |
| 315 | Linear Combination | 线性组合 |
| 316 | Linear Dimension Reduction Methods | 线性降维方法 |
| 317 | Linear Discriminant Analysis | 线性判别分析 |
| 318 | Linear Model | 线性模型 |
| 319 | Linear Regression | 线性回归 |
| 320 | Linear Vibronic Coupling Model | 线性振子耦合模型 |
| 321 | Local Recurrent | 本地卷积 |
| 322 | Logic And Heuristics Applied To Synthetic Analysis | LHASA 程序 |
| 323 | Logistic Function | 对数几率函数 |
| 324 | Logistic Regression | 对数几率回归 |
| 325 | Long Short Term Memory | 长短期记忆 |
| 326 | Long-Range Prediction | 长期预测 |
| 327 | Long-Range Prediction Models | 长期预测模型 |
| 328 | Long-Term Planning | 长期规划 |
| 329 | Long-Term Reward | 长期回报 |
| 330 | Loss Function | 损失函数 |
| 331 | MCTS Method | 蒙特卡洛树搜索方法 |
| 332 | ML Algorithm | 机器学习算法 |
| 333 | ML Modelling | 机器学习建模 |
| 334 | ML Potentials | 机器学习势能 |
| 335 | ML-Driven | 机器学习驱动的 |
| 336 | ML-Driven Optimization | 机器学习驱动的最优化 |
| 337 | MLP Neural Model | 多层感知机神经模型 |
| 338 | Machine Learning | 机器学习 |
| 339 | Machine-Readable Data | 机器可读的数据 |
| 340 | Mae | 平均绝对误差 |
| 341 | Mahalanobis Distances | 马氏距离 |
| 342 | Margin | 间隔 |
| 343 | Matrices | 矩阵 |
| 344 | Matthews Correlation Coefficient | 马修斯相关系数 |
| 345 | Maximum Likelihood Methods | 最大似然法 |
| 346 | Maximum Likelihood Procedures | 最大似然估计法 |
| 347 | Mean-Squared Error | 均方误差 |
| 348 | Mechanical Sympathy | 机械同感,软硬件协同编程 |
| 349 | Merging | 合并 |
| 350 | Message Passing Neural Networks | 消息传递神经网络 |
| 351 | Meta-Learning | 元学习 |
| 352 | Metric | 指标 |
| 353 | Microarray Data | 微阵列数据 |
| 354 | Mini Batch | 小批次 |
| 355 | Mining | 挖掘 |
| 356 | Mining Out | 挖掘 |
| 357 | Missing Values | 缺失值 |
| 358 | Model Construction | 模型构建 |
| 359 | Model Evaluation | 模型评估 |
| 360 | Model Performance | 模型性能 |
| 361 | Model Predictive Control | 模型预测控制 |
| 362 | Model Selection | 模型选择 |
| 363 | Model Statistics | 模型统计 |
| 364 | Model Training | 模型训练 |
| 365 | Model Validation | 模型验证 |
| 366 | Model-Based Iterative Reconstruction | 基于模型的迭代重建 |
| 367 | Model-Construction | 模型构建 |
| 368 | Modelling Scenario | 建模场景 |
| 369 | Molecular Graph Theory | 分子图论 |
| 370 | Molecular Modelling | 分子建模 |
| 371 | Monte Carlo Tree Search | 蒙特卡洛树搜索 |
| 372 | Moore’S Law | 摩尔定律 |
| 373 | Multi-Agent Control System | 多智能体控制系统 |
| 374 | Multi-Core Desktop Computer | 多核台式计算机 |
| 375 | Multi-Dimensional Big Data Analysis | 多维度大数据分析 |
| 376 | Multi-Layer Feed-Forward | 多层前馈 |
| 377 | Multi-Layer Perceptron | 多层感知机 |
| 378 | Multi-Objective Genetic Algorithm | 多目标遗传算法 |
| 379 | Multi-Objective Optimization | 多目标优化 |
| 380 | Multi-Reaction Synthesis | 多反应合成 |
| 381 | Multilayer Perceptron | 多层感知机 |
| 382 | Multiple Linear Regression | 多元线性回归 |
| 383 | Multivariate Regression | 多变量回归 |
| 384 | N-Dimensional Space | N维空间 |
| 385 | Naive Bayesian | 朴素贝叶斯 |
| 386 | Naive Bayesian Methods | 朴素贝叶斯方法 |
| 387 | Named Entity Recognition,NER | 命名实体识别 |
| 388 | Natural Language Processing | 自然语言处理 |
| 389 | Nearest Neighbors | 近邻 |
| 390 | Nearest Neighbour Model | 近邻模型 |
| 391 | Negative Predictive Value | 阴性预测值 |
| 392 | Network Architecture | 网络结构 |
| 393 | Network Geometry | 网络几何 |
| 394 | Neural Model | 神经模型 |
| 395 | Neural Network | 神经网络 |
| 396 | Neural Turing Machines | 神经图灵机 |
| 397 | Neural-Network-Based Function | 基于神经网络的函数 |
| 398 | Neurons | 神经元 |
| 399 | Noise | 噪声 |
| 400 | Noise Filters | 噪声过滤器 |
| 401 | Noise-Free | 无噪的 |
| 402 | Non-Linear | 非线性 |
| 403 | Non-Linear Correlation | 非线性相关 |
| 404 | Non-Linearity | 非线性 |
| 405 | Non-Parametric | 非参数 |
| 406 | Non-Parametric Algorithm | 非参数化学习算法 |
| 407 | Non-Safety-Critical Applications | 非安全关键型应用 |
| 408 | Non-Steady-State | 非稳态 |
| 409 | Non-Stochastic | 非随机的 |
| 410 | Non-Template | 非模板 |
| 411 | Non-Template Methods | 非模板方法 |
| 412 | Non-Zero Weight | 非零权重 |
| 413 | Normalization | 规范化 |
| 414 | Nuclear Magnetic Resonance | 核磁共振 |
| 415 | Occam's Razor | 奥卡姆剃刀 |
| 416 | On-The-Fly Optimization | 运行中优化 |
| 417 | One-Hot Vector | 独热向量 |
| 418 | One-Shot Learning | 单试学习 |
| 419 | Open-Source | 开源 |
| 420 | Open-Source Dataset | 开源数据集 |
| 421 | Orthogonal | 正交 |
| 422 | Outlier | 异常点 |
| 423 | Output Layer | 输出层 |
| 424 | Overfitting | 过拟合 |
| 425 | Parameter Tuning | 调参 |
| 426 | Parse Tree | 解析树 |
| 427 | Particle Swarm Optimization | 粒子群优化算法 |
| 428 | Pattern Recognition | 模式识别 |
| 429 | Perceptron | 感知机 |
| 430 | Precision | 查准率/准确率 |
| 431 | Predicted Label | 预测值 |
| 432 | Prediction | 预测 |
| 433 | Prediction Accuracy | 预测准确率 |
| 434 | Predictor | 预测器/决策函数 |
| 435 | Principal Component Analysis | 主成分分析 |
| 436 | Prior Knowledge | 先验知识 |
| 437 | Probability Distribution | 概率分布 |
| 438 | Protein Folding | 蛋白折叠 |
| 439 | Quantum Chemistry | 量子化学 |
| 440 | Quantum Mechanics | 量子力学 |
| 441 | Quantum Theory | 量子理论 |
| 442 | Radial Basis Function | 径向基函数 |
| 443 | Random Forest | 随机森林 |
| 444 | Random Sampling | 随机采样 |
| 445 | Random Selection | 随机选择 |
| 446 | Raw Datasets | 原始数据集 |
| 447 | Recall | 查全率/召回率 |
| 448 | Receiver Operating Characteristic | 受试者工作特征 |
| 449 | Rectified Linear Unit | 修正线性单元/整流线性单元 |
| 450 | Recurrent Neural Network | 循环神经网络 |
| 451 | Regression | 回归 |
| 452 | Reinforcement Learning | 强化学习 |
| 453 | Representation Learning | 表示学习 |
| 454 | Robustness | 稳健性 |
| 455 | Root Mean Square Errors | 均方根 |
| 456 | Scaling | 缩放 |
| 457 | Sequence-Function | 序列-功能 |
| 458 | Sequence-To-Sequence | 序列到序列 |
| 459 | Sigmoid | Sigmoid(一种激活函数) |
| 460 | Simulated Annealing | 模拟退火 |
| 461 | Simulation | 仿真 |
| 462 | Singular | 奇异的 |
| 463 | Softmax Function | Softmax函数/软最大化函数 |
| 464 | Speech Recognition | 语音识别 |
| 465 | Statistical Learning | 统计学习 |
| 466 | Supervised Learning | 监督学习 |
| 467 | Support Vector | 支持向量 |
| 468 | Support Vector Machine | 支持向量机 |
| 469 | Support Vector Regression | 支持向量回归 |
| 470 | Test Set | 测试集 |
| 471 | The Global Minimum | 全局最小值 |
| 472 | Threshold | 阈值 |
| 473 | Top-Down | 自顶向下 |
| 474 | Training Sample | 训练样本 |
| 475 | Training Set | 训练集 |
| 476 | Trajectory | 轨迹 |
| 477 | Transfer Learning | 迁移学习 |
| 478 | True Negative | 真负例 |
| 479 | True Positive | 真正例 |
| 480 | True Positive Rate | 真正例率 |
| 481 | Turing Test | 图灵测试 |
| 482 | Underfitting | 欠拟合 |
| 483 | Unsupervised Learning | 无监督学习 |
| 484 | Validation Set | 验证集 |
| 485 | Variance | 方差 |
| 486 | Variational Autoencoder | 变分自编码器 |
| 487 | Version Control | 版本控制 |
| 488 | Weight | 权重 |
| 489 | Word Embedding | 词嵌入 |
| 490 | Workflow | 工作流 |
| 491 | ms-QSBER-EL Model | 基于人工神经网络组合的结构生物学效应定量关系多尺度模型 |