摘要
整理了一些人工智能的术语和翻译。
Number
| 英文术语 | 中文翻译 | 常用缩写 |
|---|---|---|
| 0-1 loss | 0-1损失函数 |
A
| 英文术语 | 中文翻译 | 常用缩写 |
|---|---|---|
| Absolute Loss Function | 绝对损失函数 | |
| Absolute Value Rectification | 绝对值整流 | |
| Accept-Reject Sampling Method | 接受-拒绝抽样法/接受-拒绝采样法 | |
| Acceptance Distribution | 接受分布 | |
| Access Parameters | 访问参数 | |
| Accumulated Error Backpropagation | 累积误差反向传播 | |
| Accuracy | 准确率 | ACC |
| Acoustic | 声学 | |
| Acoustic Modeling | 声学建模 | |
| Acquisition Function | 采集函数 | |
| Action | 动作 | |
| Action Value Function | 动作价值函数 | |
| Actionism | 行为主义 | |
| Activation | 活性值 | |
| Activation Function | 激活函数 | |
| Active Learning | 主动学习 | |
| Actor | 演员 | |
| Actor-Critic Algorithm | 演员-评论员算法 | |
| Actor-Critic Method | 演员-评论员法 | |
| Adaptive Bitrate Algorithm | 自适应比特率算法 | ABR |
| Adaptive Boosting | 自适应提升 | AdaBoost |
| Adaptive Gradient Algorithm | AdaGrad | |
| Adaptive Moment Estimation Algorithm | Adam算法 | Adam |
| Adaptive Resonance Theory | 自适应谐振理论 | ART |
| Additive Model | 加性模型 | |
| Adversarial | 对抗 | |
| Adversarial Learning | 对抗学习 | |
| Adversarial Example | 对抗样本 | |
| Adversarial Networks | 对抗网络 | |
| Adversarial Training | 对抗训练 | |
| Affine Layer | 仿射层 | |
| Affine Transformation | 仿射变换 | |
| Affinity Matrix | 亲和矩阵 | |
| Agent | 智能体 | |
| Agglomerative | 聚合 | |
| Agnostic PAC Learnable | 不可知PAC可学习 | |
| Algorithm | 算法 | |
| Almost Everywhere | 几乎处处 | |
| Almost Sure | 几乎必然 | |
| Almost Sure Convergence | 几乎必然收敛 | |
| Alpha-Beta Pruning | α-β修剪法 | |
| Alternative Splicing Dataset | 选择性剪接数据集 | |
| Ambiguity | 分歧 | |
| Analytic Gradient | 解析梯度 | |
| Ancestral Sampling | 原始采样 | |
| Annealed Importance Sampling | 退火重要采样 | |
| Anomaly Detection | 异常检测 | |
| Aperiodic | 非周期的 | |
| Aperiodic Graph | 非周期性图 | |
| Application-Specific Integrated Circuit | 专用集成电路 | |
| Approximate Bayesian Computation | 近似贝叶斯计算 | |
| Approximate Dynamic Programming | 近似动态规划 | |
| Approximate Inference | 近似推断 | |
| Approximation | 近似 | |
| Approximation Error | 近似误差 | |
| Architecture | 架构 | |
| Area Under ROC Curve | AUC（ROC曲线下方面积，度量分类模型好坏的标准） | AUC |
| Arithmetic Coding | 算术编码 | |
| Artificial General Intelligence | 通用人工智能 | AGI |
| Artificial Intelligence | 人工智能 | AI |
| Artificial Neural Network | 人工神经网络 | ANN |
| Artificial Neuron | 人工神经元 | |
| Association Analysis | 关联分析 | |
| Associative Memory | 联想记忆 | |
| Associative Memory Model | 联想记忆模型 | |
| Asymptotically Unbiased | 渐近无偏 | |
| Asynchronous Stochastic Gradient Descent | 异步随机梯度下降 | ASGD |
| Asynchronous Stochastic Gradient Algorithm | 异步随机梯度算法 | |
| Asynchronous | 异步 | |
| Atrous Convolution | 空洞卷积 | |
| Attention | 注意力 | |
| Attention Cue | 注意力提示 | |
| Attention Distribution | 注意力分布 | |
| Attention Mechanism | 注意力机制 | |
| Attention Model | 注意力模型 | |
| Attractor | 吸引点 | |
| Attribute | 属性 | |
| Attribute Conditional Independence Assumption | 属性条件独立性假设 | |
| Attribute Space | 属性空间 | |
| Attribute Value | 属性值 | |
| Augmented Lagrangian | 增广拉格朗日法 | |
| Auto-Regressive Network | 自回归网络 | |
| Autoencoder | 自编码器 | AE |
| Automatic Differentiation | 自动微分 | AD |
| Automatic Speech Recognition | 自动语音识别 | ASR |
| Automatic Summarization | 自动摘要 | |
| Autoregressive Generative Model | 自回归生成模型 | |
| Autoregressive Model | 自回归模型 | AR |
| Autoregressive Process | 自回归过程 | |
| Average Gradient | 平均梯度 | |
| Average Pooling Layer | 平均汇聚层 | |
| Average-Pooling | 平均汇聚 | |
| Averaged Perceptron | 平均感知器 | |
| Aberration-Corrected | 像差矫正 | |
| Active Machine Learning | 主动机器学习 | |
| Adaptive Fuzzy Neural Network | 自适应模糊神经网络 | |
| Adaptive Sampling | 自适应采样 | |
| Admet Evaluation | 毒性评估 | |
| Alexnet | AlexNet | |
| Alphago | 阿尔法狗 | |
| AlphaZero | AlphaZero | 阿尔法狗 的升级版本 |
| Adaptive Neuro Fuzzy Inference System | 自适应神经模糊推理系统 | ANFIS |
| Approximate Probabilistic Models | 近似概率模型 | |
| Artificial Neurons | 人工神经元 | |
| Artificial Synapses | 人工突触 | |
| Attention-Based | 基于注意力（机制）的 | |
| Automating Synthetic Planning | 自动化综合规划 | |
| Automation | 自动化 | |
| Autonomous Decision-Making | 自主决策 | |
| Automated Machine Learning | 自动机器学习 | AutoML |
B
| 英文术语 | 中文翻译 | 常用缩写 |
|---|---|---|
| Back Propagation | 反向传播 | BP |
| Back Propagation Algorithm | 反向传播算法 | |
| Back Propagation Through Time | 随时间反向传播 | BPTT |
| Back-Off | 回退 | |
| Backward | 后向 | |
| Backward Induction | 反向归纳 | |
| Backward Search | 反向搜索 | |
| Bag of Words | 词袋 | BOW |
| Bagging | 袋装 | |
| Bandit | 赌博机/老虎机 | |
| Bandpass Filter | 带通滤波器 | |
| Base | 基 | |
| Base Classifier | 基分类器 | |
| Base Learner | 基学习器 | |
| Base Learning Algorithm | 基学习算法 | |
| Base Vector | 基向量 | |
| Baseline | 基准 | |
| Basin of Attraction | 吸引域 | |
| Batch | 批量 | |
| Batch Gradient Descent | 批量梯度下降法 | BGD |
| Batch Learning | 批量学习 | |
| Batch Normalization | 批量规范化 | BN |
| Batch Size | 批量大小 | |
| Baum-Welch Algorithm | Baum-Welch算法 | |
| Bayes Classifier | 贝叶斯分类器 | |
| Bayes Decision Rule | 贝叶斯决策准则 | |
| Bayes Error | 贝叶斯误差 | |
| Bayes Model Averaging | 贝叶斯模型平均 | BMA |
| Bayes Optimal Classifier | 贝叶斯最优分类器 | |
| Bayes Risk | 贝叶斯风险 | |
| Bayes’ Rule | 贝叶斯规则 | |
| Bayes’ Theorem | 贝叶斯定理 | |
| Bayesian Decision Theory | 贝叶斯决策理论 | |
| Bayesian Estimation | 贝叶斯估计 | |
| Bayesian Inference | 贝叶斯推断 | |
| Bayesian Learning | 贝叶斯学习 | |
| Bayesian Linear Regression | 贝叶斯线性回归 | |
| Bayesian Network | 贝叶斯网/贝叶斯网络 | |
| Bayesian Optimization | 贝叶斯优化 | |
| Bayesian Probability | 贝叶斯概率 | |
| Bayesian Statistics | 贝叶斯统计 | |
| Beam Search | 束搜索 | |
| Benchmark | 基准 | |
| Belief Network | 信念网/信念网络 | BN |
| Belief Propagation | 信念传播 | BP |
| Bellman Equation | 贝尔曼方程 | |
| Bellman Optimality Equation | 贝尔曼最优方程 | |
| Bernoulli Distribution | 伯努利分布 | |
| Bernoulli Output Distribution | 伯努利输出分布 | |
| Best-Arm Problem | 最优臂问题 | |
| Beta Distribution | 贝塔分布 | |
| Between-Class Scatter Matrix | 类间散度矩阵 | |
| BFGS | BFGS | |
| Bi-Directional Long-Short Term Memory | 双向长短期记忆 | Bi-LSTM |
| Bi-Partition | 二分法 | |
| Bias | 偏差/偏置 | |
| Bias In Affine Function | 偏置 | |
| Bias In Statistics | 偏差 | |
| Bias Shift | 偏置偏移 | |
| Bias-Variance Decomposition | 偏差 - 方差分解 | |
| Bias-Variance Dilemma | 偏差 - 方差困境 | |
| Biased | 有偏 | |
| Biased Importance Sampling | 有偏重要采样 | |
| Bidirectional Language Model | 双向语言模型 | |
| Bidirectional Recurrent Neural Network | 双向循环神经网络 | Bi-RNN |
| Bigram | 二元语法 | |
| Bilingual Evaluation Understudy | BLEU | |
| Binary Classification | 二分类 | |
| Binary Relation | 二元关系 | |
| Binary Sparse Coding | 二值稀疏编码 | |
| Binomial Distribution | 二项分布 | |
| Binomial Logistic Regression Model | 二项对数几率回归 | |
| Binomial Test | 二项检验 | |
| Biological Plausibility | 生物学合理性 | |
| Bit | 比特 | |
| Block | 块 | |
| Block Coordinate Descent | 块坐标下降 | |
| Block Gibbs Sampling | 块吉布斯采样 | |
| Boilerplate Code | 样板代码 | |
| Boltzmann | 玻尔兹曼 | |
| Boltzmann Distribution | 玻尔兹曼分布 | |
| Boltzmann Factor | 玻尔兹曼因子 | |
| Boltzmann Machine | 玻尔兹曼机 | |
| Boosting | 提升方法 | Boosting（一种模型训练加速方式） |
| Boosting Tree | 提升树 | |
| Bootstrap Aggregating | 装袋算法 | Bagging |
| Bootstrap Sampling | 自助采样法 | |
| Bootstrapping | 自助法/自举法 | |
| Bottleneck Layer | 瓶颈层 | |
| Bottom-Up | 自下而上 | |
| Bounding Boxes | 边界框 | |
| Break-Event Point | 平衡点 | BEP |
| Bridge Sampling | 桥式采样 | |
| Broadcasting | 广播 | |
| Broyden’s Algorithm | Broyden类算法 | |
| Bucketing | 分桶 | |
| Burn-In Period | 预烧期 | |
| Burning-In | 磨合 | |
| B-Clustering Algorithms | B树聚类算法 | |
| Balanced Accuracy | 平衡精度 | |
| Bandgap Energy | 带隙能量 | |
| Baseline Test | 基准测试 | |
| Basin Hopping | 盆地跳跃（算法） | |
| Bayesian Approach | 贝叶斯方法 | |
| Bayesian Induction | 贝叶斯归纳 | |
| Bayesian Mcmc Methods | 贝叶斯马尔可夫链蒙特卡洛方法 | |
| Bayesian Methods | 贝叶斯方法 | |
| Bayesian Molecular | 贝叶斯分子（设计方法） | |
| Bayesian Prior | 贝叶斯先验 | |
| Bayesian Program Learning | 贝叶斯程序学习 | BPL |
| Bayesian Regularized Neural Network | 贝叶斯正则化神经网络 | |
| Beam-Scanning | 波束扫描 | |
| Best Separates | 最优分离 | |
| Biased Dataset | 有偏数据集 | |
| Bit Collisions | 字节碰撞/冲突 | |
| Black Box | 黑盒子 | |
| Black-Box Attack | 黑盒攻击 | |
| Bonding Environments | 成键环境 | |
| Bonferroni Correction | 邦弗朗尼校正 | |
| Bootstrap Aggregation | 引导聚合 | bagging |
| Broyden–Fletcher–Goldfarb–Shanno | BFGS（算法） | BFGS |
| Buchwald−Hartwig Cross-Coupling | Buchwald–Hartwig 偶联（反应） | |
| bilevel optimization | 双层规划 |
C
| 英文术语 | 中文翻译 | 常用缩写 | 备注 |
|---|---|---|---|
| Calculus | 微积分 | ||
| Calculus of Variations | 变分法 | ||
| Calibration | 校准 | ||
| Canonical | 正则的 | ||
| Canonical Correlation Analysis | 典型相关分析 | CCA | |
| Capacity | 容量 | ||
| Cartesian Coordinate | 笛卡尔坐标 | ||
| Cascade | 级联 | ||
| Cascade-Correlation | 级联相关 | ||
| Catastrophic Forgetting | 灾难性遗忘 | ||
| Categorical Attribute | 分类属性 | ||
| Categorical Distribution | 类别分布 | ||
| Causal Factor | 因果因子 | ||
| Causal Modeling | 因果模型 | ||
| Cell | 单元 | ||
| Centered Difference | 中心差分 | ||
| Central Limit Theorem | 中心极限定理 | ||
| Chain Rule | 链式法则 | ||
| Channel | 通道 | ||
| Chaos | 混沌 | ||
| Chebyshev Distance | 切比雪夫距离 | ||
| Chord | 弦 | ||
| Chordal Graph | 弦图 | ||
| City Block Distance | 街区距离 | ||
| Class | 类别 | ||
| Class Label | 类标记 | ||
| Class-Conditional Probability | 类条件概率 | ||
| Class-Imbalance | 类别不平衡 | ||
| Classification | 分类 | ||
| Classification And Regression Tree | 分类与回归树 | CART | |
| Classifier | 分类器 | ||
| Clip Gradient | 梯度截断 | ||
| Clipping The Gradient | 截断梯度 | ||
| Clique | 团 | ||
| Clique Potential | 团势能 | ||
| Clockwork RNN | 时钟循环神经网络 | ||
| Closed Form Solution | 闭式解 | ||
| Closed-Form | 闭式 | ||
| Cluster | 簇 | ||
| Cluster Analysis | 聚类分析 | ||
| Cluster Assumption | 聚类假设 | ||
| Clustering | 聚类 | ||
| Clustering Ensemble | 聚类集成 | ||
| Co-Adapting | 共适应 | ||
| Co-Occurrence | 共现 | ||
| Co-Occurrence Frequency | 共现词频 | ||
| Co-Training | 协同训练 | ||
| Code | 编码 | ||
| Codebook Learning | 码书学习 | ||
| Coding Matrix | 编码矩阵 | ||
| Collaborative Filtering | 协同过滤 | ||
| Collapsed Gibbs Sampling | 收缩的吉布斯抽样 | ||
| Collinearity | 共线性 | ||
| COLT | 国际学习理论会议 | ||
| Column | 列 | ||
| Column Space | 列空间 | ||
| Combinatorial Optimization | 组合优化 | ||
| Committee-Based Learning | 基于委员会的学习 | ||
| Common Cause | 共因 | ||
| Common Parent | 同父 | ||
| Compact Singular Value Decomposition | 紧奇异值分解 | ||
| Competitive Learning | 竞争型学习 | ||
| Complementary Slackness | 互补松弛 | ||
| Complete Graph | 完全图 | ||
| Complete Linkage | 完全连接 | ||
| Complete-Data | 完全数据 | ||
| Complex Cell | 复杂细胞 | ||
| Component Learner | 组件学习器 | ||
| Compositionality | 组合性 | ||
| Comprehensibility | 可解释性 | ||
| Computation Cost | 计算代价 | ||
| Computation Graph | 计算图 | ||
| Computational Learning Theory | 计算学习理论 | ||
| Computational Linguistics | 计算语言学 | ||
| Computer Vision | 计算机视觉 | ||
| Concatenate | 连结 | ||
| Concept Class | 概念类 | ||
| Concept Drift | 概念漂移 | ||
| Concept Learning System | 概念学习系统 | CLS | |
| Concept Shift | 概念偏移 | ||
| Conditional Computation | 条件计算 | ||
| Conditional Entropy | 条件熵 | ||
| Conditional Independence | 条件独立 | ||
| Conditional Language Model | 条件语言模型 | ||
| Conditional Mutual Information | 条件互信息 | ||
| Conditional Probability | 条件概率 | ||
| Conditional Probability Density Function | 条件概率密度函数 | ||
| Conditional Probability Distribution | 条件概率分布 | ||
| Conditional Probability Table | 条件概率表 | CPT | |
| Conditional Random Field | 条件随机场 | CRF | |
| Conditional Risk | 条件风险 | ||
| Conditionally Independent | 条件独立的 | ||
| Conference On Neural Information Processing Systems | 国际神经信息处理系统会议 | NeurIPS | |
| Confidence | 置信度 | ||
| Conflict Resolution | 冲突消解 | ||
| Confusion Matrix | 混淆矩阵 | 机器学习 | |
| Conjugate | 共轭 | ||
| Conjugate Directions | 共轭方向 | ||
| Conjugate Distribution | 共轭分布 | ||
| Conjugate Gradient | 共轭梯度 | 优化，数学 | |
| Conjugate Prior | 共轭先验 | ||
| Connection Weight | 连接权 | ||
| Connectionism | 连接主义 | ||
| Consistency | 一致性 | ||
| Consistency Convergence | 一致性收敛 | ||
| Constrained Optimization | 约束优化 | ||
| Content-Addressable Memory | 基于内容寻址的存储 | CAM | |
| Context Variable | 上下文变量 | ||
| Context Vector | 上下文向量 | ||
| Context Window | 上下文窗口 | ||
| Context Word | 上下文词 | ||
| Context-Specific Independences | 特定上下文独立 | ||
| Contextual Bandit | 上下文赌博机/上下文老虎机 | ||
| Contextualized Representation | 基于上下文的表示 | ||
| Contingency Table | 列联表 | ||
| Continous Bag-Of-Words Model | 连续词袋模型 | CBOW | |
| Continuation Method | 延拓法 | ||
| Continuing Task | 持续式任务 | ||
| Continuous Attribute | 连续属性 | ||
| Continuous Learning | 持续学习 | ||
| Continuous Optimization | 连续优化 | ||
| Contractive | 收缩 | ||
| Contractive Autoencoder | 收缩自编码器 | ||
| Contractive Neural Network | 收缩神经网络 | ||
| Contrastive Divergence | 对比散度 | ||
| Controller | 控制器 | ||
| Convergence | 收敛 | ||
| Conversational Agent | 会话智能体 | ||
| Convex Optimization | 凸优化 | ||
| Convex Quadratic Programming | 凸二次规划 | ||
| Convex Set | 凸集 | ||
| Convexity | 凸性 | ||
| Convolution | 卷积 | ||
| Convolutional Boltzmann Machine | 卷积玻尔兹曼机 | ||
| Convolutional Deep Belief Network | 卷积深度信念网络 | CDBN | |
| Convolutional Kernel | 卷积核 | ||
| Convolutional Network | 卷积网络 | ||
| Convolutional Neural Network | 卷积神经网络 | CNN | |
| Coordinate | 坐标 | ||
| Coordinate Ascent | 坐标上升 | ||
| Coordinate Descent | 坐标下降 | ||
| Coparent | 共父 | ||
| Corpus | 语料库 | ||
| Correlation | 相关系数 | ||
| Correlation Coefficient | 相关系数 | ||
| Cosine | 余弦 | ||
| Cosine Decay | 余弦衰减 | ||
| Cosine Similarity | 余弦相似度 | ||
| Cost | 代价 | ||
| Cost Curve | 代价曲线 | ||
| Cost Function | 代价函数 | ||
| Cost Matrix | 代价矩阵 | ||
| Cost-Sensitive | 代价敏感 | ||
| Covariance | 协方差 | ||
| Covariance Matrix | 协方差矩阵 | ||
| Covariance RBM | 协方差RBM | ||
| Covariate Shift | 协变量偏移 | ||
| Coverage | 覆盖 | ||
| Credit Assignment Problem | 贡献度分配问题 | CAP | |
| Criterion | 准则 | ||
| Critic | 评论员 | ||
| Critic Network | 评价网络 | ||
| Critical Point | 临界点 | ||
| Critical Temperatures | 临界温度 | ||
| Cross Correlation | 互相关 | ||
| Cross Entropy | 交叉熵 | ||
| Cross Validation | 交叉验证 | ||
| Cross-Entropy Loss Function | 交叉熵损失函数 | ||
| Crowdsourcing | 众包 | ||
| Cumulative Distribution Function | 累积分布函数 | CDF | |
| Cumulative Function | 累积函数 | ||
| Curriculum Learning | 课程学习 | ||
| Curse of Dimensionality | 维数灾难 | ||
| Curvature | 曲率 | ||
| Curve-Fitting | 曲线拟合 | ||
| Cut Point | 截断点 | ||
| Cutting Plane Algorithm | 割平面法 | ||
| Cybernetics | 控制论 | ||
| Cyclic Learning Rate | 循环学习率 | ||
| C4.5 Algorithm | C4.5 算法 | 一种决策树算法，数据挖掘 | |
| Calculation Uncertainties | 计算不确定性 | ||
| Canonical Ml Methods | 经典机器学习方法 | ||
| Cartesian Distance Vector | 笛卡尔距离向量 | ||
| CASP | 国际蛋白质结构预测竞赛 | 生物 | |
| Categorical Data | 分类数据 | ||
| Categorization Algorithms | 分类算法 | ||
| ChemDataExtractor | 化学数据提取器 | CDE | |
| Chi-Squared | 卡方（分布） | ||
| Classification Model | 分类模型 | ||
| Cluster Resolution Feature Selection | 聚类分辨率特征选择 | CR-FS | |
| Cluster-Based Splitting | 基于聚类的分离方法 | ||
| Clustering Methods | 聚类方法 | ||
| Code Pipeline | 代码流水线 | ||
| Coefficient of Determination | 决定系数 | r^2 or R^2 | 统计 |
| Combined Gradient | 组合梯度（算法） | 机器学习 | |
| Complex Data | 复合数据 | ||
| Computational Cost | 计算成本 | ||
| Computational Optimisation | 计算优化 | ||
| Computational Science | 计算科学 | ||
| Computational Toxicology | 计算毒理学 | ||
| Computer Science | 计算机科学 | ||
| Computer Simulations | 计算机模拟 | ||
| Computer-Aided | 计算机辅助 | ||
| Constraint | 约束 | ||
| Core-Loss Spectrum | （电子能量损失谱中的）高能区域 | ||
| Coulomb Matrix | 库仑矩阵 | ||
| Coupled-Cluster Predictions | 耦合簇预测 | ||
| Cross-Validated Coefficient of Determination | 交叉验证的决定系数 | q^2 or Q^2 | |
| Cross-Validation | 交叉验证 | CV | |
| Crowd-Sourcing | 众包 | 商业模式 | |
| Cut-Points | 切点 | ||
| Cutoff Radial Function | 截断径向函数 | ||
| Carbon Footprint | 碳排放量 |
D
| 英文术语 | 中文翻译 | 常用缩写 | 备注 |
|---|---|---|---|
| Damping | 衰减 | ||
| Damping Factor | 阻尼因子 | ||
| Data | 数据 | ||
| Data Augmentation | 数据增强 | ||
| Data Generating Distribution | 数据生成分布 | ||
| Data Generating Process | 数据生成过程 | ||
| Data Instance | 数据样本 | ||
| Data Mining | 数据挖掘 | ||
| Data Parallelism | 数据并行 | ||
| Data Point | 数据点 | ||
| Data Preprocessing | 数据预处理 | ||
| Data Set | 数据集 | ||
| Data Wrangling | 数据整理 | ||
| Dataset Augmentation | 数据集增强 | ||
| Davidon-Fletcher-Powell | DFP | ||
| Debugging Strategy | 调试策略 | ||
| Decision Boundary | 决策边界 | ||
| Decision Function | 决策函数 | ||
| Decision Stump | 决策树桩 | ||
| Decision Surface | 决策平面 | ||
| Decision Tree | 决策树 | DT | |
| Decoder | 解码器 | ||
| Decoding | 解码 | ||
| Decompose | 分解 | ||
| Deconvolution | 反卷积 | ||
| Deconvolutional Network | 反卷积网络 | ||
| Deduction | 演绎 | ||
| Deep Belief Network | 深度信念网络 | DBN | |
| Deep Boltzmann Machine | 深度玻尔兹曼机 | DBM | |
| Deep Circuit | 深度回路 | ||
| Deep Convolutional Generative Adversarial Network | 深度卷积生成对抗网络 | DCGAN | |
| Deep Feedforward Network | 深度前馈网络 | ||
| Deep Generative Model | 深度生成模型 | ||
| Deep Learning | 深度学习 | DL | |
| Deep Model | 深度模型 | ||
| Deep Network | 深度网络 | ||
| Deep Neural Network | 深度神经网络 | DNN | |
| Deep Q-Learning | 深度 Q 学习 | ||
| Deep Q-Network | 深度Q网络 | DQN | |
| Deep Reinforcement Learning | 深度强化学习 | ||
| Deep Sequence Model | 深度序列模型 | ||
| Default Rule | 默认规则 | ||
| Definite Integral | 定积分 | ||
| Degree Of Belief | 信任度 | ||
| Delta-Bar-Delta | Delta-Bar-Delta | ||
| Denoising | 去噪 | ||
| Denoising Autoencoder | 去噪自编码器 | ||
| Denoising Score Matching | 去躁分数匹配 | ||
| Denominator Layout | 分母布局 | ||
| Dense | 稠密 | ||
| Density Estimation | 密度估计 | ||
| Density-Based Clustering | 密度聚类 | ||
| Dependency | 依赖 | ||
| Depth | 深度 | ||
| Derivative | 导数 | ||
| Description | 描述 | ||
| Design Matrix | 设计矩阵 | ||
| Detailed Balance | 细致平衡 | ||
| Detailed Balance Equation | 细致平衡方程 | ||
| Detector Stage | 探测级 | ||
| Determinant | 行列式 | ||
| Deterministic | 确定性 | ||
| Deterministic Model | 确定性模型 | ||
| Deterministic Policy | 确定性策略 | ||
| Development Set | 开发集 | ||
| Diagonal Matrix | 对角矩阵 | ||
| Diameter | 直径 | ||
| Dictionary | 字典 | ||
| Dictionary Learning | 字典学习 | ||
| Differentiable Function | 可微函数 | ||
| Differentiable Neural Computer | 可微分神经计算机 | ||
| Differential Entropy | 微分熵 | ||
| Differential Equation | 微分方程 | ||
| Differentiation | 微分 | ||
| Dilated Convolution | 膨胀卷积 | ||
| Dimension | 维度 | ||
| Dimension Reduction | 降维 | ||
| Dimensionality Reduction Algorithm | 降维算法 | ||
| Dirac Delta Function | Dirac Delta函数 | ||
| Dirac Distribution | Dirac分布 | ||
| Directed | 有向 | ||
| Directed Acyclic Graph | 有向非循环图 | DAG | |
| Directed Edge | 有向边 | ||
| Directed Graph | 有向图 | ||
| Directed Graphical Model | 有向图模型 | ||
| Directed Model | 有向模型 | ||
| Directed Separation | 有向分离 | ||
| Directional Derivative | 方向导数 | ||
| Dirichlet Distribution | 狄利克雷分布 | ||
| Disagreement Measure | 不合度量 | ||
| Disagreement-Based Methods | 基于分歧的方法 | ||
| Discount Factor | 衰减系数 | ||
| Discounted Return | 折扣回报 | ||
| Discrete Optimization | 离散优化 | ||
| Discriminant Function | 判别函数 | ||
| Discriminative Approach | 判别方法 | ||
| Discriminative Model | 判别式模型 | ||
| Discriminative RBM | 判别RBM | ||
| Discriminator | 判别器 | ||
| Discriminator Network | 判别网络 | ||
| Distance | 距离 | ||
| Distance Measure | 距离度量 | ||
| Distance Metric Learning | 距离度量学习 | ||
| Distributed Representation | 分布式表示 | ||
| Distribution | 分布 | ||
| Diverge | 发散 | ||
| Divergence | 散度 | ||
| Diversity | 多样性 | ||
| Diversity Measure | 多样性度量/差异性度量 | ||
| Divide-And-Conquer | 分而治之 | ||
| Divisive | 分裂 | ||
| Domain | 领域 | ||
| Domain Adaptation | 领域自适应 | ||
| Dominant Eigenvalue | 主特征值 | ||
| Dominant Eigenvector | 主特征向量 | ||
| Dominant Strategy | 占优策略 | ||
| Dot Product | 点积 | ||
| Double Backprop | 双反向传播 | ||
| Doubly Block Circulant Matrix | 双重分块循环矩阵 | ||
| Down Sampling | 下采样 | ||
| Downstream Task | 下游任务 | ||
| Dropout | 暂退法 | ||
| Dropout Boosting | 暂退Boosting | ||
| Dropout Mask | 暂退掩码 | ||
| Dropout Method | 暂退法 | ||
| Dual Algorithm | 对偶算法 | ||
| Dual Problem | 对偶问题 | ||
| Dummy Node | 哑结点 | ||
| Dying ReLU Problem | 死亡ReLU问题 | ||
| Dynamic Bayesian Network | 动态贝叶斯网络 | ||
| Dynamic Computational Graph | 动态计算图 | ||
| Dynamic Fusion | 动态融合 | ||
| Dynamic Programming | 动态规划 | ||
| Dynamic Structure | 动态结构 | ||
| Dynamical System | 动力系统 | ||
| Data Availability | 数据可用性 | ||
| Data Cleaning | 数据清洗 | ||
| Data Collection | 数据采集 | ||
| Data Considerations | 数据注意事项 | ||
| Data Curation | 数据监管 | ||
| Data Disparity | 数据差异 | ||
| Data Dredging | 数据挖掘 | ||
| Data Imputation | 数据填补 | ||
| Data Labels | 数据标签 | ||
| Data Leakage | 数据泄露 | ||
| Data Pre-Processing | 数据预处理 | ||
| Data Processing | 数据处理 | ||
| Data Quality | 数据质量 | ||
| Data Reduction | 数据缩减 | ||
| Data Representation | 数据表示 | ||
| Data Selection | 数据选择 | ||
| Data Sources | 数据源 | ||
| Data Splitting | 数据拆分 | ||
| Data Transformation | 数据转换 | ||
| Data-Driven | 数据驱动 | ||
| Data-Driven Decision-Making | 数据驱动的决策 | ||
| Data-Driven Methods | 数据驱动的方法 | ||
| Data-Driven Spectral Analysis | 数据驱动的光谱分析 | ||
| Data-Mining | 数据挖掘 | ||
| Database | 数据库 | ||
| DE Algorithm | 差分进化算法 | ||
| Deeplift | DeepLift模型 | ||
| Dendrogram | 树状图 | ||
| Density Functional Theory | 密度泛函理论 | DFT | |
| Density-Based Spatial Clustering Of Applications With Noise | DBSCAN密度聚类 | DBSCAN | |
| Descriptor | 描述符 | ||
| DFT Calculations | DFT计算 | ||
| Dice Similarity | 戴斯相似度 | ||
| Differential Evolution | 差分进化 | DE | |
| Dimensionality Reduction | 降维 | ||
| Direct Neural Network Modeling | 正向神经网络建模 | ||
| Discrete Manner | 离散方式 | ||
| Discrete Quanta | 离散量子 | ||
| Discretization | 离散化 | ||
| Distillation | 蒸馏 | ||
| Dynamic Datasets | 动态数据集 | ||
| Dynamic Filter Networks | 动态过滤网络 | ||
| Dynamic Sampling | 动态采样 | ||
| Dynamics Simulations | 动力学模拟 |