[
    {
        "name": "Accuracy",
        "trans": [
            "准确率"
        ]
    },
    {
        "name": "Activation Function",
        "trans": [
            "激活函数"
        ]
    },
    {
        "name": "Active Learning",
        "trans": [
            "主动学习"
        ]
    },
    {
        "name": "Area Under ROC Curve",
        "trans": [
            "AUC（ROC曲线下方面积，度量分类模型好坏的标准）"
        ]
    },
    {
        "name": "Artificial Intelligence",
        "trans": [
            "人工智能"
        ]
    },
    {
        "name": "Artificial Neural Network",
        "trans": [
            "人工神经网络"
        ]
    },
    {
        "name": "Attention",
        "trans": [
            "注意力"
        ]
    },
    {
        "name": "Back Propagation",
        "trans": [
            "反向传播"
        ]
    },
    {
        "name": "Bagging",
        "trans": [
            "袋装"
        ]
    },
    {
        "name": "Baseline",
        "trans": [
            "基准"
        ]
    },
    {
        "name": "Bayesian Inference",
        "trans": [
            "贝叶斯推断"
        ]
    },
    {
        "name": "Bayesian Network",
        "trans": [
            "贝叶斯网/贝叶斯网络"
        ]
    },
    {
        "name": "Bernoulli Distribution",
        "trans": [
            "伯努利分布"
        ]
    },
    {
        "name": "Bias",
        "trans": [
            "偏差/偏置"
        ]
    },
    {
        "name": "Biased",
        "trans": [
            "有偏"
        ]
    },
    {
        "name": "Boosting",
        "trans": [
            "Boosting（一种模型训练加速方式）"
        ]
    },
    {
        "name": "Bottom-Up",
        "trans": [
            "自下而上"
        ]
    },
    {
        "name": "Classification",
        "trans": [
            "分类"
        ]
    },
    {
        "name": "Classification And Regression Tree",
        "trans": [
            "分类与回归树"
        ]
    },
    {
        "name": "Cluster",
        "trans": [
            "簇"
        ]
    },
    {
        "name": "Computer Vision",
        "trans": [
            "计算机视觉"
        ]
    },
    {
        "name": "Confusion Matrix",
        "trans": [
            "混淆矩阵"
        ]
    },
    {
        "name": "Conjugate Gradient",
        "trans": [
            "共轭梯度"
        ]
    },
    {
        "name": "Correlation",
        "trans": [
            "相关系数"
        ]
    },
    {
        "name": "Cost Function",
        "trans": [
            "代价函数"
        ]
    },
    {
        "name": "Covariance",
        "trans": [
            "协方差"
        ]
    },
    {
        "name": "Covariance Matrix",
        "trans": [
            "协方差矩阵"
        ]
    },
    {
        "name": "Data Augmentation",
        "trans": [
            "数据增强"
        ]
    },
    {
        "name": "Data Mining",
        "trans": [
            "数据挖掘"
        ]
    },
    {
        "name": "Data Set",
        "trans": [
            "数据集"
        ]
    },
    {
        "name": "Decision Tree",
        "trans": [
            "决策树"
        ]
    },
    {
        "name": "Deep Learning",
        "trans": [
            "深度学习"
        ]
    },
    {
        "name": "Deep Neural Network",
        "trans": [
            "深度神经网络"
        ]
    },
    {
        "name": "Deep Reinforcement Learning",
        "trans": [
            "深度强化学习"
        ]
    },
    {
        "name": "Dimension Reduction",
        "trans": [
            "降维"
        ]
    },
    {
        "name": "Dimensionality Reduction Algorithm",
        "trans": [
            "降维算法"
        ]
    },
    {
        "name": "Encoder-Decoder",
        "trans": [
            "编码器-解码器（模型）"
        ]
    },
    {
        "name": "Error Function",
        "trans": [
            "误差函数"
        ]
    },
    {
        "name": "Estimator",
        "trans": [
            "估计/估计量"
        ]
    },
    {
        "name": "Feature Engineering",
        "trans": [
            "特征工程"
        ]
    },
    {
        "name": "Feature Extraction",
        "trans": [
            "特征抽取"
        ]
    },
    {
        "name": "Feature Selection",
        "trans": [
            "特征选择"
        ]
    },
    {
        "name": "Feedforward Neural Network",
        "trans": [
            "前馈神经网络"
        ]
    },
    {
        "name": "Gaussian Distribution",
        "trans": [
            "高斯分布"
        ]
    },
    {
        "name": "Gaussian Kernel Function",
        "trans": [
            "高斯核函数"
        ]
    },
    {
        "name": "Gaussian Mixtures",
        "trans": [
            "高斯混合（模型）"
        ]
    },
    {
        "name": "Gaussian Process",
        "trans": [
            "高斯过程"
        ]
    },
    {
        "name": "Gaussian Process Regression",
        "trans": [
            "高斯过程回归"
        ]
    },
    {
        "name": "Generative Modeling",
        "trans": [
            "生成式建模"
        ]
    },
    {
        "name": "Genetic Algorithm",
        "trans": [
            "遗传算法"
        ]
    },
    {
        "name": "Gradient Descent",
        "trans": [
            "梯度下降"
        ]
    },
    {
        "name": "Grid Search",
        "trans": [
            "网格搜索"
        ]
    },
    {
        "name": "Ground Truth",
        "trans": [
            "真实值"
        ]
    },
    {
        "name": "Hyperplane",
        "trans": [
            "超平面"
        ]
    },
    {
        "name": "Inductive Bias",
        "trans": [
            "归纳偏好"
        ]
    },
    {
        "name": "Information Gain",
        "trans": [
            "信息增益"
        ]
    },
    {
        "name": "Information Gain Ratio",
        "trans": [
            "信息增益比"
        ]
    },
    {
        "name": "Iteration",
        "trans": [
            "迭代"
        ]
    },
    {
        "name": "K-Fold Cross Validation",
        "trans": [
            "k 折交叉验证"
        ]
    },
    {
        "name": "K-Means Clustering",
        "trans": [
            "k-均值聚类"
        ]
    },
    {
        "name": "K-Nearest Neighbor Method",
        "trans": [
            "k-近邻"
        ]
    },
    {
        "name": "Kernel Method",
        "trans": [
            "核方法"
        ]
    },
    {
        "name": "Kernel Trick",
        "trans": [
            "核技巧"
        ]
    },
    {
        "name": "Label",
        "trans": [
            "标签/标记"
        ]
    },
    {
        "name": "Lazy Learning",
        "trans": [
            "懒惰学习"
        ]
    },
    {
        "name": "Linear Combination",
        "trans": [
            "线性组合"
        ]
    },
    {
        "name": "Linear Discriminant Analysis",
        "trans": [
            "线性判别分析"
        ]
    },
    {
        "name": "Linear Model",
        "trans": [
            "线性模型"
        ]
    },
    {
        "name": "Linear Regression",
        "trans": [
            "线性回归"
        ]
    },
    {
        "name": "Logistic Function",
        "trans": [
            "对数几率函数"
        ]
    },
    {
        "name": "Logistic Regression",
        "trans": [
            "对数几率回归"
        ]
    },
    {
        "name": "Long Short Term Memory",
        "trans": [
            "长短期记忆"
        ]
    },
    {
        "name": "Loss Function",
        "trans": [
            "损失函数"
        ]
    },
    {
        "name": "Machine Learning",
        "trans": [
            "机器学习"
        ]
    },
    {
        "name": "Margin",
        "trans": [
            "间隔"
        ]
    },
    {
        "name": "Meta-Learning",
        "trans": [
            "元学习"
        ]
    },
    {
        "name": "Metric",
        "trans": [
            "指标"
        ]
    },
    {
        "name": "Model Predictive Control",
        "trans": [
            "模型预测控制"
        ]
    },
    {
        "name": "Model Selection",
        "trans": [
            "模型选择"
        ]
    },
    {
        "name": "Multi-Layer Perceptron",
        "trans": [
            "多层感知机"
        ]
    },
    {
        "name": "Multiple Linear Regression",
        "trans": [
            "多元线性回归"
        ]
    },
    {
        "name": "Natural Language Processing",
        "trans": [
            "自然语言处理"
        ]
    },
    {
        "name": "Neural Model",
        "trans": [
            "神经模型"
        ]
    },
    {
        "name": "Neural Network",
        "trans": [
            "神经网络"
        ]
    },
    {
        "name": "Noise",
        "trans": [
            "噪声"
        ]
    },
    {
        "name": "Non-Parametric",
        "trans": [
            "非参数"
        ]
    },
    {
        "name": "Normalization",
        "trans": [
            "规范化"
        ]
    },
    {
        "name": "Occam's Razor",
        "trans": [
            "奥卡姆剃刀"
        ]
    },
    {
        "name": "One-Shot Learning",
        "trans": [
            "单试学习"
        ]
    },
    {
        "name": "Orthogonal",
        "trans": [
            "正交"
        ]
    },
    {
        "name": "Outlier",
        "trans": [
            "异常点"
        ]
    },
    {
        "name": "Output Layer",
        "trans": [
            "输出层"
        ]
    },
    {
        "name": "Overfitting",
        "trans": [
            "过拟合"
        ]
    },
    {
        "name": "Parameter Tuning",
        "trans": [
            "调参"
        ]
    },
    {
        "name": "Parse Tree",
        "trans": [
            "解析树"
        ]
    },
    {
        "name": "Particle Swarm Optimization",
        "trans": [
            "粒子群优化算法"
        ]
    },
    {
        "name": "Pattern Recognition",
        "trans": [
            "模式识别"
        ]
    },
    {
        "name": "Perceptron",
        "trans": [
            "感知机"
        ]
    },
    {
        "name": "Precision",
        "trans": [
            "查准率/准确率"
        ]
    },
    {
        "name": "Principal Component Analysis",
        "trans": [
            "主成分分析"
        ]
    },
    {
        "name": "Prior Knowledge",
        "trans": [
            "先验知识"
        ]
    },
    {
        "name": "Probability Distribution",
        "trans": [
            "概率分布"
        ]
    },
    {
        "name": "Quantum Mechanics",
        "trans": [
            "量子力学"
        ]
    },
    {
        "name": "Radial Basis Function",
        "trans": [
            "径向基函数"
        ]
    },
    {
        "name": "Random Forest",
        "trans": [
            "随机森林"
        ]
    },
    {
        "name": "Random Sampling",
        "trans": [
            "随机采样"
        ]
    },
    {
        "name": "Recall",
        "trans": [
            "查全率/召回率"
        ]
    },
    {
        "name": "Receiver Operating Characteristic",
        "trans": [
            "受试者工作特征"
        ]
    },
    {
        "name": "Rectified Linear Unit",
        "trans": [
            "修正线性单元/整流线性单元"
        ]
    },
    {
        "name": "Recurrent Neural Network",
        "trans": [
            "循环神经网络"
        ]
    },
    {
        "name": "Regression",
        "trans": [
            "回归"
        ]
    },
    {
        "name": "Reinforcement Learning",
        "trans": [
            "强化学习"
        ]
    },
    {
        "name": "Representation Learning",
        "trans": [
            "表示学习"
        ]
    },
    {
        "name": "Robustness",
        "trans": [
            "稳健性"
        ]
    },
    {
        "name": "Sequence-To-Sequence",
        "trans": [
            "序列到序列"
        ]
    },
    {
        "name": "Sigmoid",
        "trans": [
            "Sigmoid（一种激活函数）"
        ]
    },
    {
        "name": "Simulated Annealing",
        "trans": [
            "模拟退火"
        ]
    },
    {
        "name": "Singular",
        "trans": [
            "奇异的"
        ]
    },
    {
        "name": "Softmax Function",
        "trans": [
            "Softmax函数/软最大化函数"
        ]
    },
    {
        "name": "Speech Recognition",
        "trans": [
            "语音识别"
        ]
    },
    {
        "name": "Statistical Learning",
        "trans": [
            "统计学习"
        ]
    },
    {
        "name": "Supervised Learning",
        "trans": [
            "监督学习"
        ]
    },
    {
        "name": "Support Vector",
        "trans": [
            "支持向量"
        ]
    },
    {
        "name": "Support Vector Machine",
        "trans": [
            "支持向量机"
        ]
    },
    {
        "name": "Support Vector Regression",
        "trans": [
            "支持向量回归"
        ]
    },
    {
        "name": "Test Set",
        "trans": [
            "测试集"
        ]
    },
    {
        "name": "Threshold",
        "trans": [
            "阈值"
        ]
    },
    {
        "name": "Top-Down",
        "trans": [
            "自顶向下"
        ]
    },
    {
        "name": "Training Sample",
        "trans": [
            "训练样本"
        ]
    },
    {
        "name": "Training Set",
        "trans": [
            "训练集"
        ]
    },
    {
        "name": "Trajectory",
        "trans": [
            "轨迹"
        ]
    },
    {
        "name": "Transfer Learning",
        "trans": [
            "迁移学习"
        ]
    },
    {
        "name": "True Negative",
        "trans": [
            "真负例"
        ]
    },
    {
        "name": "True Positive",
        "trans": [
            "真正例"
        ]
    },
    {
        "name": "True Positive Rate",
        "trans": [
            "真正例率"
        ]
    },
    {
        "name": "Underfitting",
        "trans": [
            "欠拟合"
        ]
    },
    {
        "name": "Unsupervised Learning",
        "trans": [
            "无监督学习"
        ]
    },
    {
        "name": "Validation Set",
        "trans": [
            "验证集"
        ]
    },
    {
        "name": "Variance",
        "trans": [
            "方差"
        ]
    },
    {
        "name": "Variational Autoencoder",
        "trans": [
            "变分自编码器"
        ]
    },
    {
        "name": "Weight",
        "trans": [
            "权重"
        ]
    },
    {
        "name": "Word Embedding",
        "trans": [
            "词嵌入"
        ]
    },
    {
        "name": "2D Qsar Models",
        "trans": [
            "二维定量构效关系模型"
        ]
    },
    {
        "name": "3D Cartesian",
        "trans": [
            "三维笛卡尔（坐标）"
        ]
    },
    {
        "name": "3D Conformation",
        "trans": [
            "三维构象"
        ]
    },
    {
        "name": "3D Grids",
        "trans": [
            "三维（坐标）网格"
        ]
    },
    {
        "name": "3D Qsar Models",
        "trans": [
            "三维定量构效关系模型"
        ]
    },
    {
        "name": "Aberration-Corrected",
        "trans": [
            "像差矫正"
        ]
    },
    {
        "name": "Active Machine Learning",
        "trans": [
            "主动机器学习"
        ]
    },
    {
        "name": "Adaptive Fuzzy Neural Network",
        "trans": [
            "自适应模糊神经网络"
        ]
    },
    {
        "name": "Adaptive Sampling",
        "trans": [
            "自适应采样"
        ]
    },
    {
        "name": "Admet Evaluation",
        "trans": [
            "毒性评估"
        ]
    },
    {
        "name": "Alexnet",
        "trans": [
            "AlexNet"
        ]
    },
    {
        "name": "Alphago",
        "trans": [
            "阿尔法狗"
        ]
    },
    {
        "name": "Adaptive Neuro Fuzzy Inference System",
        "trans": [
            "自适应神经模糊推理系统"
        ]
    },
    {
        "name": "Approximate Probabilistic Models",
        "trans": [
            "近似概率模型"
        ]
    },
    {
        "name": "Artificial Neurons",
        "trans": [
            "人工神经元"
        ]
    },
    {
        "name": "Artificial Synapses",
        "trans": [
            "人工突触"
        ]
    },
    {
        "name": "Attention-Based",
        "trans": [
            "基于注意力（机制）的"
        ]
    },
    {
        "name": "Automating Synthetic Planning",
        "trans": [
            "自动化综合规划"
        ]
    },
    {
        "name": "Automation",
        "trans": [
            "自动化"
        ]
    },
    {
        "name": "Autonomous Decision-Making",
        "trans": [
            "自主决策"
        ]
    },
    {
        "name": "B-Clustering Algorithms",
        "trans": [
            "B树聚类算法"
        ]
    },
    {
        "name": "Balanced Accuracy",
        "trans": [
            "平衡精度"
        ]
    },
    {
        "name": "Bandgap Energy",
        "trans": [
            "带隙能量"
        ]
    },
    {
        "name": "Baseline Test",
        "trans": [
            "基准测试"
        ]
    },
    {
        "name": "Basin Hopping",
        "trans": [
            "盆地跳跃（算法）"
        ]
    },
    {
        "name": "Bayesian Approach",
        "trans": [
            "贝叶斯方法"
        ]
    },
    {
        "name": "Bayesian Induction",
        "trans": [
            "贝叶斯归纳"
        ]
    },
    {
        "name": "Bayesian Mcmc Methods",
        "trans": [
            "贝叶斯马尔可夫链蒙特卡洛方法"
        ]
    },
    {
        "name": "Bayesian Methods",
        "trans": [
            "贝叶斯方法"
        ]
    },
    {
        "name": "Bayesian Molecular",
        "trans": [
            "贝叶斯分子（设计方法）"
        ]
    },
    {
        "name": "Bayesian Prior",
        "trans": [
            "贝叶斯先验"
        ]
    },
    {
        "name": "Bayesian Program Learning",
        "trans": [
            "贝叶斯程序学习"
        ]
    },
    {
        "name": "Bayesian Regularized Neural Network",
        "trans": [
            "贝叶斯正则化神经网络"
        ]
    },
    {
        "name": "Beam-Scanning",
        "trans": [
            "波束扫描"
        ]
    },
    {
        "name": "Best Separates",
        "trans": [
            "最优分离"
        ]
    },
    {
        "name": "Biased Dataset",
        "trans": [
            "有偏数据集"
        ]
    },
    {
        "name": "Bit Collisions",
        "trans": [
            "字节碰撞/冲突"
        ]
    },
    {
        "name": "Black Box",
        "trans": [
            "黑盒子"
        ]
    },
    {
        "name": "Black-Box Attack",
        "trans": [
            "黑盒攻击"
        ]
    },
    {
        "name": "Bonding Environments",
        "trans": [
            "成键环境"
        ]
    },
    {
        "name": "Bonferroni Correction",
        "trans": [
            "邦弗朗尼校正"
        ]
    },
    {
        "name": "Bootstrap Aggregation",
        "trans": [
            "引导聚合"
        ]
    },
    {
        "name": "Broyden–Fletcher–Goldfarb–Shanno",
        "trans": [
            "BFGS（算法）"
        ]
    },
    {
        "name": "Buchwald−Hartwig Cross-Coupling",
        "trans": [
            "Buchwald–Hartwig 偶联（反应）"
        ]
    },
    {
        "name": "C4.5 Algorithm",
        "trans": [
            "C4.5 算法"
        ]
    },
    {
        "name": "Calculation Uncertainties",
        "trans": [
            "计算不确定性"
        ]
    },
    {
        "name": "Canonical Ml Methods",
        "trans": [
            "经典机器学习方法"
        ]
    },
    {
        "name": "Cartesian Distance Vector",
        "trans": [
            "笛卡尔距离向量"
        ]
    },
    {
        "name": "CASP",
        "trans": [
            "国际蛋白质结构预测竞赛"
        ]
    },
    {
        "name": "Categorical Data",
        "trans": [
            "分类数据"
        ]
    },
    {
        "name": "Categorization Algorithms",
        "trans": [
            "分类算法"
        ]
    },
    {
        "name": "ChemDataExtractor",
        "trans": [
            "化学数据提取器"
        ]
    },
    {
        "name": "Chi-Squared",
        "trans": [
            "卡方（分布）"
        ]
    },
    {
        "name": "Classification Model",
        "trans": [
            "分类模型"
        ]
    },
    {
        "name": "Cluster Resolution Feature Selection",
        "trans": [
            "聚类分辨率特征选择"
        ]
    },
    {
        "name": "Cluster-Based Splitting",
        "trans": [
            "基于聚类的分离方法"
        ]
    },
    {
        "name": "Clustering Methods",
        "trans": [
            "聚类方法"
        ]
    },
    {
        "name": "Code Pipeline",
        "trans": [
            "代码流水线"
        ]
    },
    {
        "name": "Coefficient of Determination",
        "trans": [
            "决定系数"
        ]
    },
    {
        "name": "Combined Gradient",
        "trans": [
            "组合梯度（算法）"
        ]
    },
    {
        "name": "Complex Data",
        "trans": [
            "复合数据"
        ]
    },
    {
        "name": "Computational Cost",
        "trans": [
            "计算成本"
        ]
    },
    {
        "name": "Computational Optimisation",
        "trans": [
            "计算优化"
        ]
    },
    {
        "name": "Computational Science",
        "trans": [
            "计算科学"
        ]
    },
    {
        "name": "Computational Toxicology",
        "trans": [
            "计算毒理学"
        ]
    },
    {
        "name": "Computer Science",
        "trans": [
            "计算机科学"
        ]
    },
    {
        "name": "Computer Simulations",
        "trans": [
            "计算机模拟"
        ]
    },
    {
        "name": "Computer-Aided",
        "trans": [
            "计算机辅助"
        ]
    },
    {
        "name": "Constraint",
        "trans": [
            "约束"
        ]
    },
    {
        "name": "Core-Loss Spectrum",
        "trans": [
            "（电子能量损失谱中的）高能区域"
        ]
    },
    {
        "name": "Coulomb Matrix",
        "trans": [
            "库仑矩阵"
        ]
    },
    {
        "name": "Coupled-Cluster Predictions",
        "trans": [
            "耦合簇预测"
        ]
    },
    {
        "name": "Cross-Validated Coefficient of Determination",
        "trans": [
            "交叉验证的决定系数"
        ]
    },
    {
        "name": "Cross-Validation",
        "trans": [
            "交叉验证"
        ]
    },
    {
        "name": "Crowd-Sourcing",
        "trans": [
            "众包"
        ]
    },
    {
        "name": "Cut-Points",
        "trans": [
            "切点"
        ]
    },
    {
        "name": "Cutoff Radial Function",
        "trans": [
            "截断径向函数"
        ]
    },
    {
        "name": "Data Availability",
        "trans": [
            "数据可用性"
        ]
    },
    {
        "name": "Data Cleaning",
        "trans": [
            "数据清洗"
        ]
    },
    {
        "name": "Data Collection",
        "trans": [
            "数据采集"
        ]
    },
    {
        "name": "Data Considerations",
        "trans": [
            "数据注意事项"
        ]
    },
    {
        "name": "Data Curation",
        "trans": [
            "数据监管"
        ]
    },
    {
        "name": "Data Disparity",
        "trans": [
            "数据差异"
        ]
    },
    {
        "name": "Data Dredging",
        "trans": [
            "数据挖掘"
        ]
    },
    {
        "name": "Data Imputation",
        "trans": [
            "数据填补"
        ]
    },
    {
        "name": "Data Labels",
        "trans": [
            "数据标签"
        ]
    },
    {
        "name": "Data Leakage",
        "trans": [
            "数据泄露"
        ]
    },
    {
        "name": "Data Pre-Processing",
        "trans": [
            "数据预处理"
        ]
    },
    {
        "name": "Data Processing",
        "trans": [
            "数据处理"
        ]
    },
    {
        "name": "Data Quality",
        "trans": [
            "数据质量"
        ]
    },
    {
        "name": "Data Reduction",
        "trans": [
            "数据缩减"
        ]
    },
    {
        "name": "Data Representation",
        "trans": [
            "数据表示"
        ]
    },
    {
        "name": "Data Selection",
        "trans": [
            "数据选择"
        ]
    },
    {
        "name": "Data Sources",
        "trans": [
            "数据源"
        ]
    },
    {
        "name": "Data Splitting",
        "trans": [
            "数据拆分"
        ]
    },
    {
        "name": "Data Transformation",
        "trans": [
            "数据转换"
        ]
    },
    {
        "name": "Data-Driven",
        "trans": [
            "数据驱动"
        ]
    },
    {
        "name": "Data-Driven Decision-Making",
        "trans": [
            "数据驱动的决策"
        ]
    },
    {
        "name": "Data-Driven Methods",
        "trans": [
            "数据驱动的方法"
        ]
    },
    {
        "name": "Data-Driven Spectral Analysis",
        "trans": [
            "数据驱动的光谱分析"
        ]
    },
    {
        "name": "Data-Mining",
        "trans": [
            "数据挖掘"
        ]
    },
    {
        "name": "Database",
        "trans": [
            "数据库"
        ]
    },
    {
        "name": "DE Algorithm",
        "trans": [
            "差分进化算法"
        ]
    },
    {
        "name": "Deeplift",
        "trans": [
            "DeepLift模型"
        ]
    },
    {
        "name": "Dendrogram",
        "trans": [
            "树状图"
        ]
    },
    {
        "name": "Density Functional Theory",
        "trans": [
            "密度泛函理论"
        ]
    },
    {
        "name": "Density-Based Spatial Clustering Of Applications With Noise",
        "trans": [
            "DBSCAN密度聚类"
        ]
    },
    {
        "name": "Descriptor",
        "trans": [
            "描述符"
        ]
    },
    {
        "name": "DFT Calculations",
        "trans": [
            "DFT计算"
        ]
    },
    {
        "name": "Dice Similarity",
        "trans": [
            "戴斯相似度"
        ]
    },
    {
        "name": "Differential Evolution",
        "trans": [
            "差分进化"
        ]
    },
    {
        "name": "Dimensionality Reduction",
        "trans": [
            "降维"
        ]
    },
    {
        "name": "Direct Neural Network Modeling",
        "trans": [
            "正向神经网络建模"
        ]
    },
    {
        "name": "Discrete Manner",
        "trans": [
            "离散方式"
        ]
    },
    {
        "name": "Discrete Quanta",
        "trans": [
            "离散量子"
        ]
    },
    {
        "name": "Discretization",
        "trans": [
            "离散化"
        ]
    },
    {
        "name": "Distillation",
        "trans": [
            "蒸馏"
        ]
    },
    {
        "name": "Dynamic Datasets",
        "trans": [
            "动态数据集"
        ]
    },
    {
        "name": "Dynamic Filter Networks",
        "trans": [
            "动态过滤网络"
        ]
    },
    {
        "name": "Dynamic Sampling",
        "trans": [
            "动态采样"
        ]
    },
    {
        "name": "Dynamics Simulations",
        "trans": [
            "动力学模拟"
        ]
    },
    {
        "name": "Eigenfunction",
        "trans": [
            "特征函数"
        ]
    },
    {
        "name": "Electronegativity",
        "trans": [
            "电负性"
        ]
    },
    {
        "name": "Elman",
        "trans": [
            "埃尔曼"
        ]
    },
    {
        "name": "Empirical Models",
        "trans": [
            "经验模型"
        ]
    },
    {
        "name": "Energy Derivatives",
        "trans": [
            "能源衍生品"
        ]
    },
    {
        "name": "Energy Potentials",
        "trans": [
            "能量潜力"
        ]
    },
    {
        "name": "Ensemble Methods",
        "trans": [
            "集成方法"
        ]
    },
    {
        "name": "Entity Normalisation",
        "trans": [
            "实体规范化"
        ]
    },
    {
        "name": "Ethical Considerations",
        "trans": [
            "道德考虑"
        ]
    },
    {
        "name": "Euclidean Distances",
        "trans": [
            "欧几里得距离"
        ]
    },
    {
        "name": "Evolutionary Algorithms",
        "trans": [
            "进化算法"
        ]
    },
    {
        "name": "Evolutionary Method",
        "trans": [
            "进化方法"
        ]
    },
    {
        "name": "Exchange–Correlation",
        "trans": [
            "交换关联（的能量/泛函）"
        ]
    },
    {
        "name": "Excited-State Potentials",
        "trans": [
            "激发态能量"
        ]
    },
    {
        "name": "Expected Reduction In Distortion",
        "trans": [
            "符合预期的失真减少"
        ]
    },
    {
        "name": "Experimental Validation Data",
        "trans": [
            "实验验证数据"
        ]
    },
    {
        "name": "Expert Systems",
        "trans": [
            "专家系统"
        ]
    },
    {
        "name": "Extended-Connectivity Circular Fingerprint",
        "trans": [
            "扩展连接环形指纹"
        ]
    },
    {
        "name": "Extraction Techniques",
        "trans": [
            "提取技术"
        ]
    },
    {
        "name": "Faber-Christensen-Huang-Lilienfeld",
        "trans": [
            "Faber-Christensen-Huang-Lilienfeld"
        ]
    },
    {
        "name": "Facial Recognition",
        "trans": [
            "面部识别"
        ]
    },
    {
        "name": "FAIR Data Principles",
        "trans": [
            "FAIR数据原则"
        ]
    },
    {
        "name": "False Negatives",
        "trans": [
            "假阴性"
        ]
    },
    {
        "name": "False Positives",
        "trans": [
            "假阳性"
        ]
    },
    {
        "name": "Fchl Representation",
        "trans": [
            "Fchl 表示"
        ]
    },
    {
        "name": "Feature Binarization",
        "trans": [
            "特征二值化"
        ]
    },
    {
        "name": "Feature Transform",
        "trans": [
            "特征变换"
        ]
    },
    {
        "name": "Feature Vectors",
        "trans": [
            "特征向量"
        ]
    },
    {
        "name": "Features",
        "trans": [
            "特征"
        ]
    },
    {
        "name": "Feed Back",
        "trans": [
            "反馈"
        ]
    },
    {
        "name": "Feed-Forward Neural Networks",
        "trans": [
            "前馈神经网络"
        ]
    },
    {
        "name": "Feedback Structure",
        "trans": [
            "反馈结构"
        ]
    },
    {
        "name": "Final Evaluation",
        "trans": [
            "最终评估"
        ]
    },
    {
        "name": "Findable",
        "trans": [
            "Accessible"
        ]
    },
    {
        "name": "First-Principles",
        "trans": [
            "第一性原理"
        ]
    },
    {
        "name": "Flow Rate",
        "trans": [
            "流速"
        ]
    },
    {
        "name": "Forward Cross-Validation",
        "trans": [
            "前向交叉验证"
        ]
    },
    {
        "name": "Forward Prediction",
        "trans": [
            "前向预测"
        ]
    },
    {
        "name": "Forward Reaction Prediction",
        "trans": [
            "前向反应预测"
        ]
    },
    {
        "name": "Fuzzy Logic",
        "trans": [
            "模糊逻辑"
        ]
    },
    {
        "name": "Fuzzy Neural Networks",
        "trans": [
            "模糊神经网络"
        ]
    },
    {
        "name": "Ga-Based Approaches",
        "trans": [
            "基于遗传算法的方法"
        ]
    },
    {
        "name": "Garbage In",
        "trans": [
            "Garbage Out"
        ]
    },
    {
        "name": "Gas-Phase Networks",
        "trans": [
            "气相网络"
        ]
    },
    {
        "name": "Gaussian Kernels",
        "trans": [
            "高斯核"
        ]
    },
    {
        "name": "Gaussian-Type Structure Descriptors",
        "trans": [
            "高斯型结构描述符"
        ]
    },
    {
        "name": "General Intelligence",
        "trans": [
            "通用智能"
        ]
    },
    {
        "name": "Generalized Gradient Approximation",
        "trans": [
            "广义梯度近似"
        ]
    },
    {
        "name": "Generative Adversarial Networks",
        "trans": [
            "生成对抗网络"
        ]
    },
    {
        "name": "Gradient Boosting Decision Tree",
        "trans": [
            "梯度提升决策树"
        ]
    },
    {
        "name": "Gradient-Based",
        "trans": [
            "基于梯度的"
        ]
    },
    {
        "name": "Grain-Surface Networks",
        "trans": [
            "粒面网络"
        ]
    },
    {
        "name": "Graph Convolutional",
        "trans": [
            "图卷积"
        ]
    },
    {
        "name": "Graph Models",
        "trans": [
            "图模型"
        ]
    },
    {
        "name": "Graph Neural Networks",
        "trans": [
            "图神经网络"
        ]
    },
    {
        "name": "Graph-Based",
        "trans": [
            "基于图形"
        ]
    },
    {
        "name": "Graph-Based Models",
        "trans": [
            "基于图的模型"
        ]
    },
    {
        "name": "Graph-Based Neural Networks",
        "trans": [
            "基于图的神经网络"
        ]
    },
    {
        "name": "Graph-Based Representation",
        "trans": [
            "基于图的表示"
        ]
    },
    {
        "name": "Graph-Convolutional Neural Network",
        "trans": [
            "图卷积神经网络"
        ]
    },
    {
        "name": "Graphics Processing Units",
        "trans": [
            "图形处理器"
        ]
    },
    {
        "name": "Gravimetric Polymerization Degree",
        "trans": [
            "比重聚合度"
        ]
    },
    {
        "name": "Hamiltonian Matrix",
        "trans": [
            "哈密顿矩阵"
        ]
    },
    {
        "name": "Hamiltonian Operator",
        "trans": [
            "哈密顿算符"
        ]
    },
    {
        "name": "Heterogeneous Data",
        "trans": [
            "异构数据"
        ]
    },
    {
        "name": "Hidden Layers",
        "trans": [
            "隐藏层"
        ]
    },
    {
        "name": "High Data Throughput",
        "trans": [
            "高数据吞吐量"
        ]
    },
    {
        "name": "High Throughput",
        "trans": [
            "高通量"
        ]
    },
    {
        "name": "High Throughput Screening",
        "trans": [
            "高通量筛选"
        ]
    },
    {
        "name": "High Variance Models",
        "trans": [
            "高方差模型"
        ]
    },
    {
        "name": "High-Dimensional Data",
        "trans": [
            "高维数据"
        ]
    },
    {
        "name": "High-Dimensional NN",
        "trans": [
            "高维神经网络"
        ]
    },
    {
        "name": "High-Dimensional Objects",
        "trans": [
            "高维对象"
        ]
    },
    {
        "name": "High-Throughput",
        "trans": [
            "高通量"
        ]
    },
    {
        "name": "Higher-Dimensional Space",
        "trans": [
            "高维空间"
        ]
    },
    {
        "name": "Higher-Dimensional Spectral Space",
        "trans": [
            "高维光谱空间"
        ]
    },
    {
        "name": "Homogenization",
        "trans": [
            "同质化"
        ]
    },
    {
        "name": "Homomorphic Encryption",
        "trans": [
            "同态加密"
        ]
    },
    {
        "name": "Human Face Recognition",
        "trans": [
            "人脸识别"
        ]
    },
    {
        "name": "Human-Encoded",
        "trans": [
            "人工编码的"
        ]
    },
    {
        "name": "Hybrid Model",
        "trans": [
            "混合模型"
        ]
    },
    {
        "name": "Hybrid Technique",
        "trans": [
            "混合技术"
        ]
    },
    {
        "name": "Hybrid-Neural Model",
        "trans": [
            "混合神经模型"
        ]
    },
    {
        "name": "Hyperparameter Opimization",
        "trans": [
            "超参数优化"
        ]
    },
    {
        "name": "Hyperparameters",
        "trans": [
            "超参数"
        ]
    },
    {
        "name": "Hyperplanes Separate",
        "trans": [
            "超平面分离"
        ]
    },
    {
        "name": "Id3 Algorithm",
        "trans": [
            "Id3 算法"
        ]
    },
    {
        "name": "Image And Speech Recognition",
        "trans": [
            "图像和语音识别"
        ]
    },
    {
        "name": "Image Classification",
        "trans": [
            "图像分类"
        ]
    },
    {
        "name": "Image Classifier",
        "trans": [
            "图像分类器"
        ]
    },
    {
        "name": "Image Recognition",
        "trans": [
            "图像识别"
        ]
    },
    {
        "name": "Informative Priors",
        "trans": [
            "信息先验"
        ]
    },
    {
        "name": "Input-Output Pairs",
        "trans": [
            "输入输出对"
        ]
    },
    {
        "name": "Instance-Based",
        "trans": [
            "基于实例的"
        ]
    },
    {
        "name": "Intelligent Machine",
        "trans": [
            "智能机器"
        ]
    },
    {
        "name": "Intermediate Neurons",
        "trans": [
            "中间神经元"
        ]
    },
    {
        "name": "Internet Of Things",
        "trans": [
            "物联网"
        ]
    },
    {
        "name": "Interpolation Coordinate",
        "trans": [
            "插值坐标"
        ]
    },
    {
        "name": "Interpretability",
        "trans": [
            "可解释性"
        ]
    },
    {
        "name": "Inverse Neural Modeling",
        "trans": [
            "逆神经建模"
        ]
    },
    {
        "name": "Inverse Neural Network Modeling",
        "trans": [
            "逆神经网络建模"
        ]
    },
    {
        "name": "Iterative Learning",
        "trans": [
            "迭代学习"
        ]
    },
    {
        "name": "Joint Distribution",
        "trans": [
            "联合分布"
        ]
    },
    {
        "name": "Jordan-Elman Neural Networks",
        "trans": [
            "Jordan-Elman 神经网络"
        ]
    },
    {
        "name": "K Clusters",
        "trans": [
            "K聚类"
        ]
    },
    {
        "name": "K Nearest Points",
        "trans": [
            "K 最近点"
        ]
    },
    {
        "name": "K-1 Folds",
        "trans": [
            "K-1 折"
        ]
    },
    {
        "name": "K-Edge (O-K Edge)",
        "trans": [
            "K-边缘（O-K 边缘）"
        ]
    },
    {
        "name": "K-Means",
        "trans": [
            "K-均值"
        ]
    },
    {
        "name": "Kendall’S Tau",
        "trans": [
            "肯德尔等级相关系数"
        ]
    },
    {
        "name": "Kernel Ridge Regression",
        "trans": [
            "核岭回归"
        ]
    },
    {
        "name": "Kernels",
        "trans": [
            "内核"
        ]
    },
    {
        "name": "Kinetic Curve",
        "trans": [
            "动力学曲线"
        ]
    },
    {
        "name": "KNN Model",
        "trans": [
            "K 近邻模型"
        ]
    },
    {
        "name": "Knowledge Extraction",
        "trans": [
            "知识提取"
        ]
    },
    {
        "name": "Knowledge Gradient",
        "trans": [
            "知识梯度"
        ]
    },
    {
        "name": "L1 And L2 Regularization",
        "trans": [
            "L1与L2正则化"
        ]
    },
    {
        "name": "Laboratory Level",
        "trans": [
            "实验室级别"
        ]
    },
    {
        "name": "Language Processing",
        "trans": [
            "语言处理"
        ]
    },
    {
        "name": "Laplacian Prior",
        "trans": [
            "拉普拉斯先验"
        ]
    },
    {
        "name": "Large-Scale Data Storage",
        "trans": [
            "大规模数据存储"
        ]
    },
    {
        "name": "Lasers",
        "trans": [
            "激光器"
        ]
    },
    {
        "name": "Lasso Regression",
        "trans": [
            "拉索回归"
        ]
    },
    {
        "name": "LBP",
        "trans": [
            "局部二值模式"
        ]
    },
    {
        "name": "Least Absolute Shrinkage And Selection Operator",
        "trans": [
            "Lasso回归"
        ]
    },
    {
        "name": "Least Square Support Vector Machine",
        "trans": [
            "最小二乘支持向量机"
        ]
    },
    {
        "name": "Ligand-Field",
        "trans": [
            "配位场"
        ]
    },
    {
        "name": "Linear",
        "trans": [
            "线性的"
        ]
    },
    {
        "name": "Linear Dimension Reduction Methods",
        "trans": [
            "线性降维方法"
        ]
    },
    {
        "name": "Linear Vibronic Coupling Model",
        "trans": [
            "线性振子耦合模型"
        ]
    },
    {
        "name": "Local Recurrent",
        "trans": [
            "本地卷积"
        ]
    },
    {
        "name": "Logic And Heuristics Applied To Synthetic Analysis",
        "trans": [
            "LHASA 程序"
        ]
    },
    {
        "name": "Long-Range Prediction",
        "trans": [
            "长期预测"
        ]
    },
    {
        "name": "Long-Range Prediction Models",
        "trans": [
            "长期预测模型"
        ]
    },
    {
        "name": "Long-Term Planning",
        "trans": [
            "长期规划"
        ]
    },
    {
        "name": "Long-Term Reward",
        "trans": [
            "长期回报"
        ]
    },
    {
        "name": "Machine-Readable Data",
        "trans": [
            "机器可读的数据"
        ]
    },
    {
        "name": "Mae",
        "trans": [
            "平均绝对误差"
        ]
    },
    {
        "name": "Mahalanobis Distances",
        "trans": [
            "马氏距离"
        ]
    },
    {
        "name": "Matrices",
        "trans": [
            "矩阵"
        ]
    },
    {
        "name": "Matthews Correlation Coefficient",
        "trans": [
            "马修斯相关系数"
        ]
    },
    {
        "name": "Maximum Likelihood Methods",
        "trans": [
            "最大似然法"
        ]
    },
    {
        "name": "Maximum Likelihood Procedures",
        "trans": [
            "最大似然估计法"
        ]
    },
    {
        "name": "MCTS Method",
        "trans": [
            "蒙特卡洛树搜索方法"
        ]
    },
    {
        "name": "Mean-Squared Error",
        "trans": [
            "均方误差"
        ]
    },
    {
        "name": "Mechanical Sympathy",
        "trans": [
            "机械同感，软硬件协同编程"
        ]
    },
    {
        "name": "Merging",
        "trans": [
            "合并"
        ]
    },
    {
        "name": "Message Passing Neural Networks",
        "trans": [
            "消息传递神经网络"
        ]
    },
    {
        "name": "Microarray Data",
        "trans": [
            "微阵列数据"
        ]
    },
    {
        "name": "Mini Batch",
        "trans": [
            "小批次"
        ]
    },
    {
        "name": "Mining",
        "trans": [
            "挖掘"
        ]
    },
    {
        "name": "Mining Out",
        "trans": [
            "挖掘"
        ]
    },
    {
        "name": "Missing Values",
        "trans": [
            "缺失值"
        ]
    },
    {
        "name": "ML Algorithm",
        "trans": [
            "机器学习算法"
        ]
    },
    {
        "name": "ML Modelling",
        "trans": [
            "机器学习建模"
        ]
    },
    {
        "name": "ML Potentials",
        "trans": [
            "机器学习势能"
        ]
    },
    {
        "name": "ML-Driven",
        "trans": [
            "机器学习驱动的"
        ]
    },
    {
        "name": "ML-Driven Optimization",
        "trans": [
            "机器学习驱动的最优化"
        ]
    },
    {
        "name": "MLP Neural Model",
        "trans": [
            "多层感知机神经模型"
        ]
    },
    {
        "name": "Model Construction",
        "trans": [
            "模型构建"
        ]
    },
    {
        "name": "Model Evaluation",
        "trans": [
            "模型评估"
        ]
    },
    {
        "name": "Model Performance",
        "trans": [
            "模型性能"
        ]
    },
    {
        "name": "Model Statistics",
        "trans": [
            "模型统计"
        ]
    },
    {
        "name": "Model Training",
        "trans": [
            "模型训练"
        ]
    },
    {
        "name": "Model Validation",
        "trans": [
            "模型验证"
        ]
    },
    {
        "name": "Model-Based Iterative Reconstruction",
        "trans": [
            "基于模型的迭代重建"
        ]
    },
    {
        "name": "Model-Construction",
        "trans": [
            "模型构建"
        ]
    },
    {
        "name": "Modelling Scenario",
        "trans": [
            "建模场景"
        ]
    },
    {
        "name": "Molecular Graph Theory",
        "trans": [
            "分子图论"
        ]
    },
    {
        "name": "Molecular Modelling",
        "trans": [
            "分子建模"
        ]
    },
    {
        "name": "Monte Carlo Tree Search",
        "trans": [
            "蒙特卡洛树搜索"
        ]
    },
    {
        "name": "Moore’S Law",
        "trans": [
            "摩尔定律"
        ]
    },
    {
        "name": "ms-QSBER-EL Model",
        "trans": [
            "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"
        ]
    },
    {
        "name": "Multi-Agent Control System",
        "trans": [
            "多智能体控制系统"
        ]
    },
    {
        "name": "Multi-Core Desktop Computer",
        "trans": [
            "多核台式计算机"
        ]
    },
    {
        "name": "Multi-Dimensional Big Data Analysis",
        "trans": [
            "多维度大数据分析"
        ]
    },
    {
        "name": "Multi-Layer Feed-Forward",
        "trans": [
            "多层前馈"
        ]
    },
    {
        "name": "Multi-Objective Genetic Algorithm",
        "trans": [
            "多目标遗传算法"
        ]
    },
    {
        "name": "Multi-Objective Optimization",
        "trans": [
            "多目标优化"
        ]
    },
    {
        "name": "Multi-Reaction Synthesis",
        "trans": [
            "多反应合成"
        ]
    },
    {
        "name": "Multilayer Perceptron",
        "trans": [
            "多层感知机"
        ]
    },
    {
        "name": "Multivariate Regression",
        "trans": [
            "多变量回归"
        ]
    },
    {
        "name": "N-Dimensional Space",
        "trans": [
            "N维空间"
        ]
    },
    {
        "name": "Naive Bayesian",
        "trans": [
            "朴素贝叶斯"
        ]
    },
    {
        "name": "Naive Bayesian Methods",
        "trans": [
            "朴素贝叶斯方法"
        ]
    },
    {
        "name": "Named Entity Recognition，NER",
        "trans": [
            "命名实体识别"
        ]
    },
    {
        "name": "Nearest Neighbors",
        "trans": [
            "近邻"
        ]
    },
    {
        "name": "Nearest Neighbour Model",
        "trans": [
            "近邻模型"
        ]
    },
    {
        "name": "Negative Predictive Value",
        "trans": [
            "阴性预测值"
        ]
    },
    {
        "name": "Network Architecture",
        "trans": [
            "网络结构"
        ]
    },
    {
        "name": "Network Geometry",
        "trans": [
            "网络几何"
        ]
    },
    {
        "name": "Neural Turing Machines",
        "trans": [
            "神经图灵机"
        ]
    },
    {
        "name": "Neural-Network-Based Function",
        "trans": [
            "基于神经网络的函数"
        ]
    },
    {
        "name": "Neurons",
        "trans": [
            "神经元"
        ]
    },
    {
        "name": "Nuclear Magnetic Resonance",
        "trans": [
            "核磁共振"
        ]
    },
    {
        "name": "Noise Filters",
        "trans": [
            "噪声过滤器"
        ]
    },
    {
        "name": "Noise-Free",
        "trans": [
            "无噪的"
        ]
    },
    {
        "name": "Non-Linear",
        "trans": [
            "非线性"
        ]
    },
    {
        "name": "Non-Linear Correlation",
        "trans": [
            "非线性相关"
        ]
    },
    {
        "name": "Non-Linearity",
        "trans": [
            "非线性"
        ]
    },
    {
        "name": "Non-Parametric Algorithm",
        "trans": [
            "非参数化学习算法"
        ]
    },
    {
        "name": "Non-Safety-Critical Applications",
        "trans": [
            "非安全关键型应用"
        ]
    },
    {
        "name": "Non-Steady-State",
        "trans": [
            "非稳态"
        ]
    },
    {
        "name": "Non-Stochastic",
        "trans": [
            "非随机的"
        ]
    },
    {
        "name": "Non-Template",
        "trans": [
            "非模板"
        ]
    },
    {
        "name": "Non-Template Methods",
        "trans": [
            "非模板方法"
        ]
    },
    {
        "name": "Non-Zero Weight",
        "trans": [
            "非零权重"
        ]
    },
    {
        "name": "On-The-Fly Optimization",
        "trans": [
            "运行中优化"
        ]
    },
    {
        "name": "One-Hot Vector",
        "trans": [
            "独热向量"
        ]
    },
    {
        "name": "Open-Source",
        "trans": [
            "开源"
        ]
    },
    {
        "name": "Open-Source Dataset",
        "trans": [
            "开源数据集"
        ]
    },
    {
        "name": "Predicted Label",
        "trans": [
            "预测值"
        ]
    },
    {
        "name": "Prediction",
        "trans": [
            "预测"
        ]
    },
    {
        "name": "Prediction Accuracy",
        "trans": [
            "预测准确率"
        ]
    },
    {
        "name": "Predictor",
        "trans": [
            "预测器/决策函数"
        ]
    },
    {
        "name": "Protein Folding",
        "trans": [
            "蛋白折叠"
        ]
    },
    {
        "name": "Quantum Chemistry",
        "trans": [
            "量子化学"
        ]
    },
    {
        "name": "Quantum Theory",
        "trans": [
            "量子理论"
        ]
    },
    {
        "name": "Random Selection",
        "trans": [
            "随机选择"
        ]
    },
    {
        "name": "Raw Datasets",
        "trans": [
            "原始数据集"
        ]
    },
    {
        "name": "Root Mean Square Errors",
        "trans": [
            "均方根"
        ]
    },
    {
        "name": "Scaling",
        "trans": [
            "缩放"
        ]
    },
    {
        "name": "Simulation",
        "trans": [
            "仿真"
        ]
    },
    {
        "name": "The Global Minimum",
        "trans": [
            "全局最小值"
        ]
    },
    {
        "name": "Turing Test",
        "trans": [
            "图灵测试"
        ]
    },
    {
        "name": "Version Control",
        "trans": [
            "版本控制"
        ]
    },
    {
        "name": "Workflow",
        "trans": [
            "工作流"
        ]
    },
    {
        "name": "Sequence-Function",
        "trans": [
            "序列-功能"
        ]
    }
]