深度学习英文单词表

忘是亡心i 2023-08-17 15:16 102阅读 0赞

# 深度学习英文单词表 #

<table> 
 <thead> 
  <tr> 
   <th>英文/缩写</th> 
   <th>汉语</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>Absolute value rectification</td> 
   <td>绝对值整流</td> 
  </tr> 
  <tr> 
   <td>Activation Function</td> 
   <td>激活函数</td> 
  </tr> 
  <tr> 
   <td>Accumulated error backpropagation</td> 
   <td>累积误差反向传播</td> 
  </tr> 
  <tr> 
   <td>Acoustic modeling</td> 
   <td>声学建模</td> 
  </tr> 
  <tr> 
   <td>Acquisition funtion</td> 
   <td>采集函数</td> 
  </tr> 
  <tr> 
   <td>Actor-critic method</td> 
   <td>行为-评判方法</td> 
  </tr> 
  <tr> 
   <td>Adaptive bitrate (ABR) algorithm</td> 
   <td>自适应比特率算法</td> 
  </tr> 
  <tr> 
   <td>Adaptive Resonance Theory/ART</td> 
   <td>自适应谐振理论</td> 
  </tr> 
  <tr> 
   <td>Addictive model</td> 
   <td>加性模型</td> 
  </tr> 
  <tr> 
   <td>Adversarial example</td> 
   <td>对抗样本</td> 
  </tr> 
  <tr> 
   <td>Adversarial Networks</td> 
   <td>对抗网络</td> 
  </tr> 
  <tr> 
   <td>Affine Layer</td> 
   <td>仿射层</td> 
  </tr> 
  <tr> 
   <td>Affinity matrix</td> 
   <td>亲和矩阵</td> 
  </tr> 
  <tr> 
   <td>Agent</td> 
   <td>智能体</td> 
  </tr> 
  <tr> 
   <td>Algorithm</td> 
   <td>算法</td> 
  </tr> 
  <tr> 
   <td>Alpha-beta pruning</td> 
   <td>α-β剪枝</td> 
  </tr> 
  <tr> 
   <td>Alternative splicing dataset</td> 
   <td>选择性剪接数据集</td> 
  </tr> 
  <tr> 
   <td>Analytic gradient</td> 
   <td>解析梯度</td> 
  </tr> 
  <tr> 
   <td>Ancestral Sampling</td> 
   <td>原始采样</td> 
  </tr> 
  <tr> 
   <td>Annealed importance sampling</td> 
   <td>退火重要采样</td> 
  </tr> 
  <tr> 
   <td>Anomaly detection</td> 
   <td>异常检测</td> 
  </tr> 
  <tr> 
   <td>Application-speciﬁc integrated circuit</td> 
   <td>专用集成电路</td> 
  </tr> 
  <tr> 
   <td>Approximate Bayesian computation</td> 
   <td>近似贝叶斯计算</td> 
  </tr> 
  <tr> 
   <td>Approximate inference</td> 
   <td>近似推断</td> 
  </tr> 
  <tr> 
   <td>Approximation</td> 
   <td>近似</td> 
  </tr> 
  <tr> 
   <td>Architecture</td> 
   <td>架构</td> 
  </tr> 
  <tr> 
   <td>Area Under ROC Curve/AUC</td> 
   <td>Roc 曲线下面积</td> 
  </tr> 
  <tr> 
   <td>Artificial General Intelligence/AGI</td> 
   <td>通用人工智能</td> 
  </tr> 
  <tr> 
   <td>Artificial Intelligence/AI</td> 
   <td>人工智能</td> 
  </tr> 
  <tr> 
   <td>Association analysis</td> 
   <td>关联分析</td> 
  </tr> 
  <tr> 
   <td>Asymptotically unbiased</td> 
   <td>渐近无偏</td> 
  </tr> 
  <tr> 
   <td>Asynchoronous Stochastic Gradient Descent</td> 
   <td>异步随机梯度下降</td> 
  </tr> 
  <tr> 
   <td>Attention mechanism</td> 
   <td>注意力机制</td> 
  </tr> 
  <tr> 
   <td>Attribute conditional independence assumption</td> 
   <td>属性条件独立性假设</td> 
  </tr> 
  <tr> 
   <td>Attribute space</td> 
   <td>属性空间</td> 
  </tr> 
  <tr> 
   <td>Attribute value</td> 
   <td>属性值</td> 
  </tr> 
  <tr> 
   <td>Augmented Lagrangian</td> 
   <td>增广拉格朗日法</td> 
  </tr> 
  <tr> 
   <td>Autoencoder</td> 
   <td>自编码器</td> 
  </tr> 
  <tr> 
   <td>Automatic diﬀerentiation</td> 
   <td>自动微分</td> 
  </tr> 
  <tr> 
   <td>Automatic speech recognition/ASR</td> 
   <td>自动语音识别</td> 
  </tr> 
  <tr> 
   <td>Automatic summarization</td> 
   <td>自动摘要</td> 
  </tr> 
  <tr> 
   <td>Auto-regressive network</td> 
   <td>自回归网络</td> 
  </tr> 
  <tr> 
   <td>Average gradient</td> 
   <td>平均梯度</td> 
  </tr> 
  <tr> 
   <td>Average-Pooling</td> 
   <td>平均池化</td> 
  </tr> 
  <tr> 
   <td>Backpropagation/BP</td> 
   <td>反向传播</td> 
  </tr> 
  <tr> 
   <td>Backpropagation Through Time</td> 
   <td>通过时间的反向传播</td> 
  </tr> 
  <tr> 
   <td>Backward induction</td> 
   <td>逆向归纳</td> 
  </tr> 
  <tr> 
   <td>Bag of words/BoW</td> 
   <td>词袋</td> 
  </tr> 
  <tr> 
   <td>Base learner</td> 
   <td>基学习器</td> 
  </tr> 
  <tr> 
   <td>Base learning algorithm</td> 
   <td>基学习算法</td> 
  </tr> 
  <tr> 
   <td>Batch</td> 
   <td>批次</td> 
  </tr> 
  <tr> 
   <td>Batch Normalization/BN</td> 
   <td>批次归一化</td> 
  </tr> 
  <tr> 
   <td>Bayes decision rule</td> 
   <td>贝叶斯判定准则</td> 
  </tr> 
  <tr> 
   <td>Bayes error</td> 
   <td>贝叶斯误差</td> 
  </tr> 
  <tr> 
   <td>Bayes Model Averaging/BMA</td> 
   <td>贝叶斯模型平均</td> 
  </tr> 
  <tr> 
   <td>Bayes optimal classifier</td> 
   <td>贝叶斯最优分类器</td> 
  </tr> 
  <tr> 
   <td>Bayesian decision theory</td> 
   <td>贝叶斯决策论</td> 
  </tr> 
  <tr> 
   <td>Bayesian network</td> 
   <td>贝叶斯网络</td> 
  </tr> 
  <tr> 
   <td>Bayesian optimization</td> 
   <td>贝叶斯优化</td> 
  </tr> 
  <tr> 
   <td>Beam search</td> 
   <td>束搜索</td> 
  </tr> 
  <tr> 
   <td>Bechmark</td> 
   <td>基准</td> 
  </tr> 
  <tr> 
   <td>Belief network</td> 
   <td>信念网络</td> 
  </tr> 
  <tr> 
   <td>Bellman equation</td> 
   <td>贝尔曼方程</td> 
  </tr> 
  <tr> 
   <td>Between-class scatter matrix</td> 
   <td>类间散度矩阵</td> 
  </tr> 
  <tr> 
   <td>Bias</td> 
   <td>偏置 / 偏差</td> 
  </tr> 
  <tr> 
   <td>Biased</td> 
   <td>有偏</td> 
  </tr> 
  <tr> 
   <td>Biased importance sampling</td> 
   <td>有偏重要采样</td> 
  </tr> 
  <tr> 
   <td>Bias-variance decomposition</td> 
   <td>偏差-方差分解</td> 
  </tr> 
  <tr> 
   <td>Bias-Variance Dilemma</td> 
   <td>偏差 - 方差困境</td> 
  </tr> 
  <tr> 
   <td>Bi-directional Long-Short Term Memory/Bi-LSTM</td> 
   <td>双向长短期记忆</td> 
  </tr> 
  <tr> 
   <td>Binary classification</td> 
   <td>二元分类</td> 
  </tr> 
  <tr> 
   <td>Binary relation</td> 
   <td>二元关系</td> 
  </tr> 
  <tr> 
   <td>Binary sparse coding</td> 
   <td>二值稀疏编码</td> 
  </tr> 
  <tr> 
   <td>Binomial distribution</td> 
   <td>二项分布</td> 
  </tr> 
  <tr> 
   <td>Binomial test</td> 
   <td>二项检验</td> 
  </tr> 
  <tr> 
   <td>Bi-partition</td> 
   <td>二分法</td> 
  </tr> 
  <tr> 
   <td>Block coordinate descent</td> 
   <td>块坐标下降</td> 
  </tr> 
  <tr> 
   <td>Block Gibbs Sampling</td> 
   <td>块吉布斯采样</td> 
  </tr> 
  <tr> 
   <td>Boilerplate code</td> 
   <td>样板代码</td> 
  </tr> 
  <tr> 
   <td>Boltzmann distribution</td> 
   <td>玻尔兹曼分布</td> 
  </tr> 
  <tr> 
   <td>Boltzmann machine</td> 
   <td>玻尔兹曼机</td> 
  </tr> 
  <tr> 
   <td>Bootstrap sampling</td> 
   <td>自助采样法／可重复采样／有放回采样</td> 
  </tr> 
  <tr> 
   <td>Bootstrapping</td> 
   <td>自助法</td> 
  </tr> 
  <tr> 
   <td>Bottleneck layer</td> 
   <td>瓶颈层</td> 
  </tr> 
  <tr> 
   <td>Bounding Boxes</td> 
   <td>边界框</td> 
  </tr> 
  <tr> 
   <td>Break-Event Point/BEP</td> 
   <td>平衡点</td> 
  </tr> 
  <tr> 
   <td>Bridge sampling</td> 
   <td>桥式采样</td> 
  </tr> 
  <tr> 
   <td>Broadcasting</td> 
   <td>广播</td> 
  </tr> 
  <tr> 
   <td>Burning-in</td> 
   <td>磨合</td> 
  </tr> 
  <tr> 
   <td>Calculus of variations</td> 
   <td>变分法</td> 
  </tr> 
  <tr> 
   <td>Calibration</td> 
   <td>校准</td> 
  </tr> 
  <tr> 
   <td>Canonical</td> 
   <td>正则的</td> 
  </tr> 
  <tr> 
   <td>Cascade/Coalesced</td> 
   <td>级联</td> 
  </tr> 
  <tr> 
   <td>Cascade-Correlation</td> 
   <td>级联相关</td> 
  </tr> 
  <tr> 
   <td>Categorical attribute</td> 
   <td>分类属性</td> 
  </tr> 
  <tr> 
   <td>Categorical distribution</td> 
   <td>范畴分布、分类分布</td> 
  </tr> 
  <tr> 
   <td>Causal factor</td> 
   <td>因果因子</td> 
  </tr> 
  <tr> 
   <td>Causal modeling</td> 
   <td>因果模型</td> 
  </tr> 
  <tr> 
   <td>Centered difference</td> 
   <td>中心差分</td> 
  </tr> 
  <tr> 
   <td>Central limit theorem</td> 
   <td>中心极限定理</td> 
  </tr> 
  <tr> 
   <td>Chain rule</td> 
   <td>链式法则</td> 
  </tr> 
  <tr> 
   <td>Chordal graph</td> 
   <td>弦图</td> 
  </tr> 
  <tr> 
   <td>Class-conditional probability</td> 
   <td>类条件概率</td> 
  </tr> 
  <tr> 
   <td>Classification and regression tree/CART</td> 
   <td>分类与回归树</td> 
  </tr> 
  <tr> 
   <td>Classifier</td> 
   <td>分类器</td> 
  </tr> 
  <tr> 
   <td>Class-imbalance</td> 
   <td>分类不平衡</td> 
  </tr> 
  <tr> 
   <td>Clip gradient</td> 
   <td>梯度截断</td> 
  </tr> 
  <tr> 
   <td>Clique potential</td> 
   <td>团势能</td> 
  </tr> 
  <tr> 
   <td>Closed-form</td> 
   <td>闭式</td> 
  </tr> 
  <tr> 
   <td>Cluster</td> 
   <td>簇/类/集群</td> 
  </tr> 
  <tr> 
   <td>Cluster analysis</td> 
   <td>聚类分析</td> 
  </tr> 
  <tr> 
   <td>Clustering</td> 
   <td>聚类</td> 
  </tr> 
  <tr> 
   <td>Clustering ensemble</td> 
   <td>聚类集成</td> 
  </tr> 
  <tr> 
   <td>Co-adapting</td> 
   <td>共适应</td> 
  </tr> 
  <tr> 
   <td>Coding matrix</td> 
   <td>编码矩阵</td> 
  </tr> 
  <tr> 
   <td>Collaborative filtering</td> 
   <td>协同过滤</td> 
  </tr> 
  <tr> 
   <td>COLT</td> 
   <td>国际学习理论会议</td> 
  </tr> 
  <tr> 
   <td>Committee-based learning</td> 
   <td>基于委员会的学习</td> 
  </tr> 
  <tr> 
   <td>Competitive learning</td> 
   <td>竞争型学习</td> 
  </tr> 
  <tr> 
   <td>Complete graph</td> 
   <td>完全图</td> 
  </tr> 
  <tr> 
   <td>Component learner</td> 
   <td>组件学习器</td> 
  </tr> 
  <tr> 
   <td>Comprehensibility</td> 
   <td>可解释性</td> 
  </tr> 
  <tr> 
   <td>Computation Cost</td> 
   <td>计算成本</td> 
  </tr> 
  <tr> 
   <td>Computational Linguistics</td> 
   <td>计算语言学</td> 
  </tr> 
  <tr> 
   <td>Computer vision</td> 
   <td>计算机视觉</td> 
  </tr> 
  <tr> 
   <td>Concept drift</td> 
   <td>概念漂移</td> 
  </tr> 
  <tr> 
   <td>Concept Learning System/CLS</td> 
   <td>概念学习系统</td> 
  </tr> 
  <tr> 
   <td>Conditional entropy</td> 
   <td>条件熵</td> 
  </tr> 
  <tr> 
   <td>Conditional mutual information</td> 
   <td>条件互信息</td> 
  </tr> 
  <tr> 
   <td>Conditional Probability Table/CPT</td> 
   <td>条件概率表</td> 
  </tr> 
  <tr> 
   <td>Conditional random field/CRF</td> 
   <td>条件随机场</td> 
  </tr> 
  <tr> 
   <td>Conditional risk</td> 
   <td>条件风险</td> 
  </tr> 
  <tr> 
   <td>Confidence</td> 
   <td>置信度</td> 
  </tr> 
  <tr> 
   <td>Confusion matrix</td> 
   <td>混淆矩阵</td> 
  </tr> 
  <tr> 
   <td>Conjugate directions</td> 
   <td>共轭方向</td> 
  </tr> 
  <tr> 
   <td>Conjugate distribution</td> 
   <td>共轭分布</td> 
  </tr> 
  <tr> 
   <td>Conjugate gradient</td> 
   <td>共轭梯度</td> 
  </tr> 
  <tr> 
   <td>Connection weight</td> 
   <td>连接权</td> 
  </tr> 
  <tr> 
   <td>Connectionism</td> 
   <td>连结主义</td> 
  </tr> 
  <tr> 
   <td>Consistency</td> 
   <td>一致性／相合性</td> 
  </tr> 
  <tr> 
   <td>Consistency convergence</td> 
   <td>一致性收敛</td> 
  </tr> 
  <tr> 
   <td>Contingency table</td> 
   <td>列联表</td> 
  </tr> 
  <tr> 
   <td>Continuation method</td> 
   <td>延拓法</td> 
  </tr> 
  <tr> 
   <td>Continuous attribute</td> 
   <td>连续属性</td> 
  </tr> 
  <tr> 
   <td>Contractive autoencoder</td> 
   <td>收缩自编码器</td> 
  </tr> 
  <tr> 
   <td>Contractive neural network</td> 
   <td>收缩神经网络</td> 
  </tr> 
  <tr> 
   <td>Convex optimization</td> 
   <td>凸优化</td> 
  </tr> 
  <tr> 
   <td>Convergence</td> 
   <td>收敛</td> 
  </tr> 
  <tr> 
   <td>Conversational agent</td> 
   <td>会话智能体</td> 
  </tr> 
  <tr> 
   <td>Convex quadratic programming</td> 
   <td>凸二次规划</td> 
  </tr> 
  <tr> 
   <td>Convexity</td> 
   <td>凸性</td> 
  </tr> 
  <tr> 
   <td>Convolutional Boltzmann Machine</td> 
   <td>卷积玻尔兹曼机</td> 
  </tr> 
  <tr> 
   <td>Convolutional neural network/CNN</td> 
   <td>卷积神经网络</td> 
  </tr> 
  <tr> 
   <td>Co-occurrence</td> 
   <td>同现</td> 
  </tr> 
  <tr> 
   <td>Coordinate descent</td> 
   <td>坐标下降</td> 
  </tr> 
  <tr> 
   <td>Correlation coefficient</td> 
   <td>相关系数</td> 
  </tr> 
  <tr> 
   <td>Cosine similarity</td> 
   <td>余弦相似度</td> 
  </tr> 
  <tr> 
   <td>Cost curve</td> 
   <td>成本曲线</td> 
  </tr> 
  <tr> 
   <td>Cost Function</td> 
   <td>成本函数</td> 
  </tr> 
  <tr> 
   <td>Cost matrix</td> 
   <td>成本矩阵</td> 
  </tr> 
  <tr> 
   <td>Cost-sensitive</td> 
   <td>成本敏感</td> 
  </tr> 
  <tr> 
   <td>Covariance</td> 
   <td>协方差</td> 
  </tr> 
  <tr> 
   <td>Covariance matrix</td> 
   <td>协方差矩阵</td> 
  </tr> 
  <tr> 
   <td>Cross entropy</td> 
   <td>交叉熵</td> 
  </tr> 
  <tr> 
   <td>Cross validation</td> 
   <td>交叉验证</td> 
  </tr> 
  <tr> 
   <td>Cross-correlation</td> 
   <td>互相关函数</td> 
  </tr> 
  <tr> 
   <td>Crowdsourcing</td> 
   <td>众包</td> 
  </tr> 
  <tr> 
   <td>Cumulative function</td> 
   <td>累积函数</td> 
  </tr> 
  <tr> 
   <td>Curse of dimensionality</td> 
   <td>维度灾难</td> 
  </tr> 
  <tr> 
   <td>Curve-fitting</td> 
   <td>曲线拟合</td> 
  </tr> 
  <tr> 
   <td>Cut point</td> 
   <td>截断点</td> 
  </tr> 
  <tr> 
   <td>Cutting plane algorithm</td> 
   <td>割平面法</td> 
  </tr> 
  <tr> 
   <td>Data generating distribution</td> 
   <td>数据生成分布</td> 
  </tr> 
  <tr> 
   <td>Data mining</td> 
   <td>数据挖掘</td> 
  </tr> 
  <tr> 
   <td>Data parallelism</td> 
   <td>数据并行</td> 
  </tr> 
  <tr> 
   <td>Data set</td> 
   <td>数据集</td> 
  </tr> 
  <tr> 
   <td>Data Wrangling</td> 
   <td>数据整理</td> 
  </tr> 
  <tr> 
   <td>Dataset augmentation</td> 
   <td>数据集增强</td> 
  </tr> 
  <tr> 
   <td>Debugging strategy</td> 
   <td>调试策略</td> 
  </tr> 
  <tr> 
   <td>Decision Boundary</td> 
   <td>决策边界</td> 
  </tr> 
  <tr> 
   <td>Decision stump</td> 
   <td>决策树桩</td> 
  </tr> 
  <tr> 
   <td>Decision tree</td> 
   <td>决策树／判定树</td> 
  </tr> 
  <tr> 
   <td>Deconvolutional Network</td> 
   <td>解卷积网络</td> 
  </tr> 
  <tr> 
   <td>Deduction</td> 
   <td>演绎</td> 
  </tr> 
  <tr> 
   <td>Deep Belief Network</td> 
   <td>深度信念网络</td> 
  </tr> 
  <tr> 
   <td>Deep Boltzmann Machine</td> 
   <td>深度玻尔兹曼机</td> 
  </tr> 
  <tr> 
   <td>Deep circuit</td> 
   <td>深度回路</td> 
  </tr> 
  <tr> 
   <td>Deep Convolutional Generative Adversarial Network/DCGAN</td> 
   <td>深度卷积生成对抗网络</td> 
  </tr> 
  <tr> 
   <td>Deep generative model</td> 
   <td>深度生成模型</td> 
  </tr> 
  <tr> 
   <td>Deep learning</td> 
   <td>深度学习</td> 
  </tr> 
  <tr> 
   <td>Deep Q-Learning</td> 
   <td>深度 Q 学习</td> 
  </tr> 
  <tr> 
   <td>Deep Q-Network</td> 
   <td>深度 Q 网络</td> 
  </tr> 
  <tr> 
   <td>Denoising autoencoder</td> 
   <td>去噪自编码器</td> 
  </tr> 
  <tr> 
   <td>Denoising score matching</td> 
   <td>去噪得分匹配</td> 
  </tr> 
  <tr> 
   <td>Density estimation</td> 
   <td>密度估计</td> 
  </tr> 
  <tr> 
   <td>Density-based clustering</td> 
   <td>密度聚类</td> 
  </tr> 
  <tr> 
   <td>Detailed balance</td> 
   <td>细致平衡</td> 
  </tr> 
  <tr> 
   <td>Determinant</td> 
   <td>行列式</td> 
  </tr> 
  <tr> 
   <td>Deterministic</td> 
   <td>确定性</td> 
  </tr> 
  <tr> 
   <td>Diagonal matrix</td> 
   <td>对角矩阵</td> 
  </tr> 
  <tr> 
   <td>Differentiable neural computer</td> 
   <td>可微分神经计算机</td> 
  </tr> 
  <tr> 
   <td>Diﬀerential entropy</td> 
   <td>微分熵</td> 
  </tr> 
  <tr> 
   <td>Diﬀerential equation</td> 
   <td>微分方程</td> 
  </tr> 
  <tr> 
   <td>Dimensionality reduction algorithm</td> 
   <td>降维算法</td> 
  </tr> 
  <tr> 
   <td>Directed edge</td> 
   <td>有向边</td> 
  </tr> 
  <tr> 
   <td>Directed graphical model</td> 
   <td>有向图模型</td> 
  </tr> 
  <tr> 
   <td>Directional derivative</td> 
   <td>方向导数</td> 
  </tr> 
  <tr> 
   <td>Dirichlet distribution</td> 
   <td>狄利克雷分布</td> 
  </tr> 
  <tr> 
   <td>Disagreement measure</td> 
   <td>不合度量</td> 
  </tr> 
  <tr> 
   <td>Discriminative model</td> 
   <td>判别模型</td> 
  </tr> 
  <tr> 
   <td>Discriminator</td> 
   <td>判别器</td> 
  </tr> 
  <tr> 
   <td>Discriminator network</td> 
   <td>判别器网络</td> 
  </tr> 
  <tr> 
   <td>Distance measure</td> 
   <td>距离度量</td> 
  </tr> 
  <tr> 
   <td>Distance metric learning</td> 
   <td>距离度量学习</td> 
  </tr> 
  <tr> 
   <td>Distribution</td> 
   <td>分布</td> 
  </tr> 
  <tr> 
   <td>Divergence</td> 
   <td>散度</td> 
  </tr> 
  <tr> 
   <td>Diversity measure</td> 
   <td>多样性度量／差异性度量</td> 
  </tr> 
  <tr> 
   <td>Domain adaption</td> 
   <td>领域自适应</td> 
  </tr> 
  <tr> 
   <td>Dominant strategy</td> 
   <td>占优策略</td> 
  </tr> 
  <tr> 
   <td>Double backprop</td> 
   <td>双反向传播</td> 
  </tr> 
  <tr> 
   <td>Doubly block circulant matrix</td> 
   <td>双重分块循环矩阵</td> 
  </tr> 
  <tr> 
   <td>Downsampling</td> 
   <td>下采样</td> 
  </tr> 
  <tr> 
   <td>D-separation/Directed separation</td> 
   <td>有向分离</td> 
  </tr> 
  <tr> 
   <td>Dual problem</td> 
   <td>对偶问题</td> 
  </tr> 
  <tr> 
   <td>Dummy node</td> 
   <td>哑结点</td> 
  </tr> 
  <tr> 
   <td>Dynamic Fusion</td> 
   <td>动态融合</td> 
  </tr> 
  <tr> 
   <td>Dynamic programming</td> 
   <td>动态规划</td> 
  </tr> 
  <tr> 
   <td>Echo state network</td> 
   <td>回声状态网络</td> 
  </tr> 
  <tr> 
   <td>Edge device</td> 
   <td>边缘设备</td> 
  </tr> 
  <tr> 
   <td>Eigendecomposition</td> 
   <td>特征分解</td> 
  </tr> 
  <tr> 
   <td>Eigenvalue</td> 
   <td>特征值</td> 
  </tr> 
  <tr> 
   <td>Eigenvalue decomposition</td> 
   <td>特征值分解</td> 
  </tr> 
  <tr> 
   <td>Eigenvector</td> 
   <td>特征向量</td> 
  </tr> 
  <tr> 
   <td>Element-wise product</td> 
   <td>元素对应乘积</td> 
  </tr> 
  <tr> 
   <td>Ellipsoid method</td> 
   <td>椭球法</td> 
  </tr> 
  <tr> 
   <td>Embedding</td> 
   <td>嵌套</td> 
  </tr> 
  <tr> 
   <td>Emotional analysis</td> 
   <td>情绪分析</td> 
  </tr> 
  <tr> 
   <td>Empirical conditional entropy</td> 
   <td>经验条件熵</td> 
  </tr> 
  <tr> 
   <td>Empirical entropy</td> 
   <td>经验熵</td> 
  </tr> 
  <tr> 
   <td>Empirical error</td> 
   <td>经验误差</td> 
  </tr> 
  <tr> 
   <td>Empirical risk</td> 
   <td>经验风险</td> 
  </tr> 
  <tr> 
   <td>End-to-End</td> 
   <td>端到端</td> 
  </tr> 
  <tr> 
   <td>Energy-based model</td> 
   <td>基于能量的模型</td> 
  </tr> 
  <tr> 
   <td>Ensemble learning</td> 
   <td>集成学习</td> 
  </tr> 
  <tr> 
   <td>Ensemble pruning</td> 
   <td>集成修剪</td> 
  </tr> 
  <tr> 
   <td>Epochs</td> 
   <td>轮数/周期</td> 
  </tr> 
  <tr> 
   <td>Error Correcting Output Codes/ECOC</td> 
   <td>纠错输出码</td> 
  </tr> 
  <tr> 
   <td>Error rate</td> 
   <td>错误率</td> 
  </tr> 
  <tr> 
   <td>Error-ambiguity decomposition</td> 
   <td>误差-分歧分解</td> 
  </tr> 
  <tr> 
   <td>Euclidean distance</td> 
   <td>欧氏距离</td> 
  </tr> 
  <tr> 
   <td>Euclidean norm</td> 
   <td>欧几里得范数</td> 
  </tr> 
  <tr> 
   <td>Evolutionary computation</td> 
   <td>演化计算</td> 
  </tr> 
  <tr> 
   <td>Exact</td> 
   <td>确切的</td> 
  </tr> 
  <tr> 
   <td>Expectation-Maximization/EM</td> 
   <td>期望最大化</td> 
  </tr> 
  <tr> 
   <td>Expected loss</td> 
   <td>期望损失</td> 
  </tr> 
  <tr> 
   <td>Expert network</td> 
   <td>专家网络</td> 
  </tr> 
  <tr> 
   <td>Explaining away eﬀect</td> 
   <td>相消解释作用</td> 
  </tr> 
  <tr> 
   <td>Exploding Gradient Problem</td> 
   <td>梯度爆炸问题</td> 
  </tr> 
  <tr> 
   <td>Exploitation</td> 
   <td>利用</td> 
  </tr> 
  <tr> 
   <td>Exploration</td> 
   <td>探索</td> 
  </tr> 
  <tr> 
   <td>Exponential loss function</td> 
   <td>指数损失函数</td> 
  </tr> 
  <tr> 
   <td>Extreme Learning Machine/ELM</td> 
   <td>超限学习机</td> 
  </tr> 
  <tr> 
   <td>Factor analysis</td> 
   <td>因子分析</td> 
  </tr> 
  <tr> 
   <td>Factorization</td> 
   <td>因子分解</td> 
  </tr> 
  <tr> 
   <td>Factors of variation</td> 
   <td>变差因素</td> 
  </tr> 
  <tr> 
   <td>False negative</td> 
   <td>假负例</td> 
  </tr> 
  <tr> 
   <td>False positive</td> 
   <td>假正例</td> 
  </tr> 
  <tr> 
   <td>False Positive Rate/FPR</td> 
   <td>假正例率</td> 
  </tr> 
  <tr> 
   <td>Fault-tolerant asynchronous training</td> 
   <td>容错异步训练</td> 
  </tr> 
  <tr> 
   <td>Feature engineering</td> 
   <td>特征工程</td> 
  </tr> 
  <tr> 
   <td>Feature extractor</td> 
   <td>特征提取器</td> 
  </tr> 
  <tr> 
   <td>Feature map</td> 
   <td>特征图</td> 
  </tr> 
  <tr> 
   <td>Feature selection</td> 
   <td>特征选择</td> 
  </tr> 
  <tr> 
   <td>Feature vector</td> 
   <td>特征向量</td> 
  </tr> 
  <tr> 
   <td>Featured Learning</td> 
   <td>特征学习</td> 
  </tr> 
  <tr> 
   <td>Feedforward Neural Networks/FNN</td> 
   <td>前馈神经网络</td> 
  </tr> 
  <tr> 
   <td>Field Programmable Gated Array</td> 
   <td>现场可编程门阵列</td> 
  </tr> 
  <tr> 
   <td>Fine-tuning</td> 
   <td>精调</td> 
  </tr> 
  <tr> 
   <td>Finite difference</td> 
   <td>有限差分</td> 
  </tr> 
  <tr> 
   <td>Fixed point equation</td> 
   <td>不动点方程</td> 
  </tr> 
  <tr> 
   <td>Flipping output</td> 
   <td>翻转法</td> 
  </tr> 
  <tr> 
   <td>Fluctuation</td> 
   <td>震荡</td> 
  </tr> 
  <tr> 
   <td>Folk Theorem</td> 
   <td>无名氏定理</td> 
  </tr> 
  <tr> 
   <td>Forget gate</td> 
   <td>遗忘门</td> 
  </tr> 
  <tr> 
   <td>Forward stagewise algorithm</td> 
   <td>前向分步算法</td> 
  </tr> 
  <tr> 
   <td>Fourier transform</td> 
   <td>傅立叶变换</td> 
  </tr> 
  <tr> 
   <td>Frequentist</td> 
   <td>频率主义学派</td> 
  </tr> 
  <tr> 
   <td>Frequentist probability</td> 
   <td>频率派概率</td> 
  </tr> 
  <tr> 
   <td>Full-rank matrix</td> 
   <td>满秩矩阵</td> 
  </tr> 
  <tr> 
   <td>Functional derivative</td> 
   <td>泛函导数</td> 
  </tr> 
  <tr> 
   <td>Functional neuron</td> 
   <td>功能神经元</td> 
  </tr> 
  <tr> 
   <td>Gain ratio</td> 
   <td>增益率</td> 
  </tr> 
  <tr> 
   <td>Game payoff</td> 
   <td>博弈效用</td> 
  </tr> 
  <tr> 
   <td>Game theory</td> 
   <td>博弈论</td> 
  </tr> 
  <tr> 
   <td>Gated recurrent net/GRN</td> 
   <td>门控循环网络</td> 
  </tr> 
  <tr> 
   <td>Gaussian kernel function</td> 
   <td>高斯核函数</td> 
  </tr> 
  <tr> 
   <td>Gaussian Mixture Model</td> 
   <td>高斯混合模型</td> 
  </tr> 
  <tr> 
   <td>Gaussian Process</td> 
   <td>高斯过程</td> 
  </tr> 
  <tr> 
   <td>General Problem Solving</td> 
   <td>通用问题求解</td> 
  </tr> 
  <tr> 
   <td>Generalization</td> 
   <td>泛化</td> 
  </tr> 
  <tr> 
   <td>Generalization error</td> 
   <td>泛化误差</td> 
  </tr> 
  <tr> 
   <td>Generalization error bound</td> 
   <td>泛化误差上界</td> 
  </tr> 
  <tr> 
   <td>Generalized Lagrange function</td> 
   <td>广义拉格朗日函数</td> 
  </tr> 
  <tr> 
   <td>Generalized linear model</td> 
   <td>广义线性模型</td> 
  </tr> 
  <tr> 
   <td>Generalized pseudolikelihood</td> 
   <td>广义伪似然</td> 
  </tr> 
  <tr> 
   <td>Generalized Rayleigh quotient</td> 
   <td>广义瑞利商</td> 
  </tr> 
  <tr> 
   <td>Generalized score matching</td> 
   <td>广义得分匹配</td> 
  </tr> 
  <tr> 
   <td>Generative Adversarial Networks/GAN</td> 
   <td>生成对抗网络</td> 
  </tr> 
  <tr> 
   <td>Generative Model</td> 
   <td>生成模型</td> 
  </tr> 
  <tr> 
   <td>Generative moment matching network</td> 
   <td>生成矩匹配网络</td> 
  </tr> 
  <tr> 
   <td>Generator</td> 
   <td>生成器</td> 
  </tr> 
  <tr> 
   <td>Genetic Algorithm/GA</td> 
   <td>遗传算法</td> 
  </tr> 
  <tr> 
   <td>Giant magnetoresistance</td> 
   <td>巨磁阻</td> 
  </tr> 
  <tr> 
   <td>Gibbs sampling</td> 
   <td>吉布斯采样</td> 
  </tr> 
  <tr> 
   <td>Gini index</td> 
   <td>基尼指数</td> 
  </tr> 
  <tr> 
   <td>Global contrast normalization</td> 
   <td>全局对比度归一化</td> 
  </tr> 
  <tr> 
   <td>Global minimum</td> 
   <td>全局最小</td> 
  </tr> 
  <tr> 
   <td>Global Optimization</td> 
   <td>全局优化</td> 
  </tr> 
  <tr> 
   <td>Gradient boosting tree</td> 
   <td>梯度提升树</td> 
  </tr> 
  <tr> 
   <td>Gradient Descent</td> 
   <td>梯度下降</td> 
  </tr> 
  <tr> 
   <td>Gradient energy distribution</td> 
   <td>梯度能量分布</td> 
  </tr> 
  <tr> 
   <td>Graph theory</td> 
   <td>图论</td> 
  </tr> 
  <tr> 
   <td>Grid search</td> 
   <td>网格搜索</td> 
  </tr> 
  <tr> 
   <td>Ground-truth</td> 
   <td>真相／真实</td> 
  </tr> 
  <tr> 
   <td>Hard margin</td> 
   <td>硬间隔</td> 
  </tr> 
  <tr> 
   <td>Hard voting</td> 
   <td>硬投票</td> 
  </tr> 
  <tr> 
   <td>Harmonic mean</td> 
   <td>调和平均</td> 
  </tr> 
  <tr> 
   <td>Hesse matrix</td> 
   <td>海赛矩阵</td> 
  </tr> 
  <tr> 
   <td>Heterogeneous Information Network/HIN</td> 
   <td>异质信息网络</td> 
  </tr> 
  <tr> 
   <td>Hidden dynamic model</td> 
   <td>隐动态模型</td> 
  </tr> 
  <tr> 
   <td>Hidden layer</td> 
   <td>隐藏层</td> 
  </tr> 
  <tr> 
   <td>Hidden Markov Model/HMM</td> 
   <td>隐马尔可夫模型</td> 
  </tr> 
  <tr> 
   <td>Hierarchical clustering</td> 
   <td>层次聚类</td> 
  </tr> 
  <tr> 
   <td>Hilbert space</td> 
   <td>希尔伯特空间</td> 
  </tr> 
  <tr> 
   <td>Hinge loss function</td> 
   <td>合页损失函数</td> 
  </tr> 
  <tr> 
   <td>Hold-out</td> 
   <td>留出法</td> 
  </tr> 
  <tr> 
   <td>Homogeneous</td> 
   <td>同质</td> 
  </tr> 
  <tr> 
   <td>Hybrid computing</td> 
   <td>混合计算</td> 
  </tr> 
  <tr> 
   <td>Hyperparameter</td> 
   <td>超参数</td> 
  </tr> 
  <tr> 
   <td>Hypothesis</td> 
   <td>假设</td> 
  </tr> 
  <tr> 
   <td>Hypothesis test</td> 
   <td>假设检验</td> 
  </tr> 
  <tr> 
   <td>ICML</td> 
   <td>国际机器学习会议</td> 
  </tr> 
  <tr> 
   <td>Identity matrix</td> 
   <td>单位矩阵</td> 
  </tr> 
  <tr> 
   <td>Image restoration</td> 
   <td>图像复原</td> 
  </tr> 
  <tr> 
   <td>Imperfect Information</td> 
   <td>不完美信息</td> 
  </tr> 
  <tr> 
   <td>Improved iterative scaling/IIS</td> 
   <td>改进的迭代尺度法</td> 
  </tr> 
  <tr> 
   <td>Incremental learning</td> 
   <td>增量学习</td> 
  </tr> 
  <tr> 
   <td>Independent and identically distributed/i.i.d.</td> 
   <td>独立同分布</td> 
  </tr> 
  <tr> 
   <td>Independent Component Analysis/ICA</td> 
   <td>独立成分分析</td> 
  </tr> 
  <tr> 
   <td>Independent subspace analysis</td> 
   <td>独立子空间分析</td> 
  </tr> 
  <tr> 
   <td>Indicator function</td> 
   <td>指示函数</td> 
  </tr> 
  <tr> 
   <td>Individual learner</td> 
   <td>个体学习器</td> 
  </tr> 
  <tr> 
   <td>Induction</td> 
   <td>归纳</td> 
  </tr> 
  <tr> 
   <td>Inductive bias</td> 
   <td>归纳偏好</td> 
  </tr> 
  <tr> 
   <td>Inductive learning</td> 
   <td>归纳学习</td> 
  </tr> 
  <tr> 
   <td>Inductive Logic Programming/ILP</td> 
   <td>归纳逻辑程序设计</td> 
  </tr> 
  <tr> 
   <td>Inequality constraint</td> 
   <td>不等式约束</td> 
  </tr> 
  <tr> 
   <td>Inference</td> 
   <td>推断</td> 
  </tr> 
  <tr> 
   <td>Information entropy</td> 
   <td>信息熵</td> 
  </tr> 
  <tr> 
   <td>Information gain</td> 
   <td>信息增益</td> 
  </tr> 
  <tr> 
   <td>Input layer</td> 
   <td>输入层</td> 
  </tr> 
  <tr> 
   <td>Insensitive loss</td> 
   <td>不敏感损失</td> 
  </tr> 
  <tr> 
   <td>Instance segmentation</td> 
   <td>实例分割</td> 
  </tr> 
  <tr> 
   <td>Inter-cluster similarity</td> 
   <td>簇间相似度</td> 
  </tr> 
  <tr> 
   <td>International Conference for Machine Learning/ICML</td> 
   <td>国际机器学习大会</td> 
  </tr> 
  <tr> 
   <td>Intra-cluster similarity</td> 
   <td>簇内相似度</td> 
  </tr> 
  <tr> 
   <td>Intrinsic value</td> 
   <td>固有值</td> 
  </tr> 
  <tr> 
   <td>Invariance</td> 
   <td>不变性</td> 
  </tr> 
  <tr> 
   <td>Invert</td> 
   <td>求逆</td> 
  </tr> 
  <tr> 
   <td>Isometric Mapping/Isomap</td> 
   <td>等度量映射</td> 
  </tr> 
  <tr> 
   <td>Isotonic regression</td> 
   <td>等分回归</td> 
  </tr> 
  <tr> 
   <td>Iterative Dichotomiser</td> 
   <td>迭代二分器</td> 
  </tr> 
  <tr> 
   <td>Jensen-Shannon Divergence/JSD</td> 
   <td>JS 散度</td> 
  </tr> 
  <tr> 
   <td>Kernel method</td> 
   <td>核方法</td> 
  </tr> 
  <tr> 
   <td>Kernel trick</td> 
   <td>核技巧</td> 
  </tr> 
  <tr> 
   <td>Kernelized Linear Discriminant Analysis/KLDA</td> 
   <td>核线性判别分析</td> 
  </tr> 
  <tr> 
   <td>K-fold cross validation</td> 
   <td>k 折交叉验证／k 倍交叉验证</td> 
  </tr> 
  <tr> 
   <td>K-Means Clustering</td> 
   <td>K - 均值聚类</td> 
  </tr> 
  <tr> 
   <td>K-Nearest Neighbours Algorithm/KNN</td> 
   <td>K近邻算法</td> 
  </tr> 
  <tr> 
   <td>Knowledge base</td> 
   <td>知识库</td> 
  </tr> 
  <tr> 
   <td>Knowledge Engineering</td> 
   <td>知识工程</td> 
  </tr> 
  <tr> 
   <td>Knowledge graph</td> 
   <td>知识图谱</td> 
  </tr> 
  <tr> 
   <td>Knowledge Representation</td> 
   <td>知识表征</td> 
  </tr> 
  <tr> 
   <td>Label space</td> 
   <td>标记空间</td> 
  </tr> 
  <tr> 
   <td>Lagrange duality</td> 
   <td>拉格朗日对偶性</td> 
  </tr> 
  <tr> 
   <td>Lagrange multiplier</td> 
   <td>拉格朗日乘子</td> 
  </tr> 
  <tr> 
   <td>Laplace smoothing</td> 
   <td>拉普拉斯平滑</td> 
  </tr> 
  <tr> 
   <td>Laplacian correction</td> 
   <td>拉普拉斯修正</td> 
  </tr> 
  <tr> 
   <td>Latent Dirichlet Allocation/LDA</td> 
   <td>隐狄利克雷分布</td> 
  </tr> 
  <tr> 
   <td>Latent semantic analysis</td> 
   <td>潜在语义分析</td> 
  </tr> 
  <tr> 
   <td>Latent variable</td> 
   <td>隐变量</td> 
  </tr> 
  <tr> 
   <td>Law of large number</td> 
   <td>大数定理</td> 
  </tr> 
  <tr> 
   <td>Layer-wise Adaptive Rate Scaling/LARS</td> 
   <td>层级对应的适应率缩放</td> 
  </tr> 
  <tr> 
   <td>Lazy learning</td> 
   <td>懒惰学习</td> 
  </tr> 
  <tr> 
   <td>Leaky ReLU</td> 
   <td>渗漏整流线性单元</td> 
  </tr> 
  <tr> 
   <td>Learner</td> 
   <td>学习器</td> 
  </tr> 
  <tr> 
   <td>Learning by analogy</td> 
   <td>类比学习</td> 
  </tr> 
  <tr> 
   <td>Learning rate</td> 
   <td>学习速率</td> 
  </tr> 
  <tr> 
   <td>Learning Vector Quantization/LVQ</td> 
   <td>学习向量量化</td> 
  </tr> 
  <tr> 
   <td>Least squares regression tree</td> 
   <td>最小二乘回归树</td> 
  </tr> 
  <tr> 
   <td>Leave-One-Out/LOO</td> 
   <td>留一法</td> 
  </tr> 
  <tr> 
   <td>Lebesgue-integrable</td> 
   <td>勒贝格可积</td> 
  </tr> 
  <tr> 
   <td>Left eigenvector</td> 
   <td>左特征向量</td> 
  </tr> 
  <tr> 
   <td>Leibniz’s rule</td> 
   <td>莱布尼兹法则</td> 
  </tr> 
  <tr> 
   <td>Linear Discriminant Analysis/LDA</td> 
   <td>线性判别</td> 
  </tr> 
  <tr> 
   <td>Linear model</td> 
   <td>线性模型</td> 
  </tr> 
  <tr> 
   <td>Linear Regression</td> 
   <td>线性回归</td> 
  </tr> 
  <tr> 
   <td>Linear threshold units</td> 
   <td>线性阀值单元</td> 
  </tr> 
  <tr> 
   <td>Link function</td> 
   <td>联系函数</td> 
  </tr> 
  <tr> 
   <td>Local conditional probability distribution</td> 
   <td>局部条件概率分布</td> 
  </tr> 
  <tr> 
   <td>Local contrast normalization</td> 
   <td>局部对比度归一化</td> 
  </tr> 
  <tr> 
   <td>Local curvature</td> 
   <td>局部曲率</td> 
  </tr> 
  <tr> 
   <td>Local Invariances</td> 
   <td>局部不变性</td> 
  </tr> 
  <tr> 
   <td>Local Markov property</td> 
   <td>局部马尔可夫性</td> 
  </tr> 
  <tr> 
   <td>Local minimum</td> 
   <td>局部最小</td> 
  </tr> 
  <tr> 
   <td>Log likelihood</td> 
   <td>对数似然</td> 
  </tr> 
  <tr> 
   <td>Log odds/logit</td> 
   <td>对数几率</td> 
  </tr> 
  <tr> 
   <td>Logistic Regression</td> 
   <td>Logistic 回归</td> 
  </tr> 
  <tr> 
   <td>Log-likelihood</td> 
   <td>对数似然</td> 
  </tr> 
  <tr> 
   <td>Log-linear regression</td> 
   <td>对数线性回归</td> 
  </tr> 
  <tr> 
   <td>Long-Short Term Memory/LSTM</td> 
   <td>长短期记忆</td> 
  </tr> 
  <tr> 
   <td>Long-term dependency</td> 
   <td>长期依赖</td> 
  </tr> 
  <tr> 
   <td>Loopy belief propagation</td> 
   <td>环状信念传播</td> 
  </tr> 
  <tr> 
   <td>Loss function</td> 
   <td>损失函数</td> 
  </tr> 
  <tr> 
   <td>Low rank matrix approximation</td> 
   <td>低秩矩阵近似</td> 
  </tr> 
  <tr> 
   <td>Machine translation/MT</td> 
   <td>机器翻译</td> 
  </tr> 
  <tr> 
   <td>Macron-P</td> 
   <td>宏查准率</td> 
  </tr> 
  <tr> 
   <td>Macron-R</td> 
   <td>宏查全率</td> 
  </tr> 
  <tr> 
   <td>Main diagonal</td> 
   <td>主对角线</td> 
  </tr> 
  <tr> 
   <td>Majority voting</td> 
   <td>绝对多数投票法</td> 
  </tr> 
  <tr> 
   <td>Manifold assumption</td> 
   <td>流形假设</td> 
  </tr> 
  <tr> 
   <td>Manifold learning</td> 
   <td>流形学习</td> 
  </tr> 
  <tr> 
   <td>Manifold tangent classiﬁer</td> 
   <td>流形正切分类器</td> 
  </tr> 
  <tr> 
   <td>Margin theory</td> 
   <td>间隔理论</td> 
  </tr> 
  <tr> 
   <td>Marginal distribution</td> 
   <td>边缘分布</td> 
  </tr> 
  <tr> 
   <td>Marginal independence</td> 
   <td>边缘独立性</td> 
  </tr> 
  <tr> 
   <td>Marginal probability distribution</td> 
   <td>边缘概率分布</td> 
  </tr> 
  <tr> 
   <td>Marginalization</td> 
   <td>边际化</td> 
  </tr> 
  <tr> 
   <td>Markov Chain</td> 
   <td>马尔可夫链</td> 
  </tr> 
  <tr> 
   <td>Markov Chain Monte Carlo/MCMC</td> 
   <td>马尔可夫链蒙特卡罗方法</td> 
  </tr> 
  <tr> 
   <td>Markov Random Field</td> 
   <td>马尔可夫随机场</td> 
  </tr> 
  <tr> 
   <td>Matrix inversion</td> 
   <td>逆矩阵</td> 
  </tr> 
  <tr> 
   <td>Maximal clique</td> 
   <td>最大团</td> 
  </tr> 
  <tr> 
   <td>Maximum A Posteriori</td> 
   <td>最大后验</td> 
  </tr> 
  <tr> 
   <td>Maximum Likelihood Estimation/MLE</td> 
   <td>极大似然估计／极大似然法</td> 
  </tr> 
  <tr> 
   <td>Maximum margin</td> 
   <td>最大间隔</td> 
  </tr> 
  <tr> 
   <td>Maximum weighted spanning tree</td> 
   <td>最大带权生成树</td> 
  </tr> 
  <tr> 
   <td>Max-Pooling</td> 
   <td>最大池化</td> 
  </tr> 
  <tr> 
   <td>Mean product of Student t-distribution</td> 
   <td>学生 t 分布均值乘积</td> 
  </tr> 
  <tr> 
   <td>Mean squared error</td> 
   <td>均方误差</td> 
  </tr> 
  <tr> 
   <td>Mean-covariance restricted Boltzmann machine</td> 
   <td>均值-协方差受限玻尔兹曼机</td> 
  </tr> 
  <tr> 
   <td>Measure theory</td> 
   <td>测度论</td> 
  </tr> 
  <tr> 
   <td>Meta-learner</td> 
   <td>元学习器</td> 
  </tr> 
  <tr> 
   <td>Metric learning</td> 
   <td>度量学习</td> 
  </tr> 
  <tr> 
   <td>Micro-P</td> 
   <td>微查准率</td> 
  </tr> 
  <tr> 
   <td>Micro-R</td> 
   <td>微查全率</td> 
  </tr> 
  <tr> 
   <td>Mini-Batch SGD</td> 
   <td>小批次随机梯度下降</td> 
  </tr> 
  <tr> 
   <td>Minimal Description Length/MDL</td> 
   <td>最小描述长度</td> 
  </tr> 
  <tr> 
   <td>Minimax game</td> 
   <td>极小极大博弈</td> 
  </tr> 
  <tr> 
   <td>Misclassification cost</td> 
   <td>误分类成本</td> 
  </tr> 
  <tr> 
   <td>Mixture density network</td> 
   <td>混合密度网络</td> 
  </tr> 
  <tr> 
   <td>Mixture of experts</td> 
   <td>混合专家</td> 
  </tr> 
  <tr> 
   <td>Model predictive control (MPC)</td> 
   <td>模型预测控制</td> 
  </tr> 
  <tr> 
   <td>Moment matching</td> 
   <td>矩匹配</td> 
  </tr> 
  <tr> 
   <td>Momentum</td> 
   <td>动量</td> 
  </tr> 
  <tr> 
   <td>Monte Carlo Estimate</td> 
   <td>蒙特卡洛估计</td> 
  </tr> 
  <tr> 
   <td>Moore’s Law</td> 
   <td>摩尔定律</td> 
  </tr> 
  <tr> 
   <td>Moral graph</td> 
   <td>道德图／端正图</td> 
  </tr> 
  <tr> 
   <td>Multi-class classification</td> 
   <td>多类别分类</td> 
  </tr> 
  <tr> 
   <td>Multi-document summarization</td> 
   <td>多文档摘要</td> 
  </tr> 
  <tr> 
   <td>Multi-kernel learning</td> 
   <td>多核学习</td> 
  </tr> 
  <tr> 
   <td>Multi-layer feedforward neural networks</td> 
   <td>多层前馈神经网络</td> 
  </tr> 
  <tr> 
   <td>Multilayer Perceptron/MLP</td> 
   <td>多层感知器</td> 
  </tr> 
  <tr> 
   <td>Multimodal learning</td> 
   <td>多模态学习</td> 
  </tr> 
  <tr> 
   <td>Multinomial distribution</td> 
   <td>多项分布</td> 
  </tr> 
  <tr> 
   <td>Multiple Dimensional Scaling</td> 
   <td>多维缩放</td> 
  </tr> 
  <tr> 
   <td>Multiple linear regression</td> 
   <td>多元线性回归</td> 
  </tr> 
  <tr> 
   <td>Multi-response Linear Regression/MLR</td> 
   <td>多响应线性回归</td> 
  </tr> 
  <tr> 
   <td>Multivariate normal distribution</td> 
   <td>多维正态分布</td> 
  </tr> 
  <tr> 
   <td>Mutual information</td> 
   <td>互信息</td> 
  </tr> 
  <tr> 
   <td>Naive bayes</td> 
   <td>朴素贝叶斯</td> 
  </tr> 
  <tr> 
   <td>Naive Bayes Classifier</td> 
   <td>朴素贝叶斯分类器</td> 
  </tr> 
  <tr> 
   <td>Named entity recognition</td> 
   <td>命名实体识别</td> 
  </tr> 
  <tr> 
   <td>Nash equilibrium</td> 
   <td>纳什均衡</td> 
  </tr> 
  <tr> 
   <td>Nash reversion</td> 
   <td>纳什回归</td> 
  </tr> 
  <tr> 
   <td>Natural language generation/NLG</td> 
   <td>自然语言生成</td> 
  </tr> 
  <tr> 
   <td>Natural language processing</td> 
   <td>自然语言处理</td> 
  </tr> 
  <tr> 
   <td>Nearest-neighbor search</td> 
   <td>最近邻搜索</td> 
  </tr> 
  <tr> 
   <td>Negative class</td> 
   <td>负类</td> 
  </tr> 
  <tr> 
   <td>Negative correlation</td> 
   <td>负相关法</td> 
  </tr> 
  <tr> 
   <td>Negative deﬁnite</td> 
   <td>负定</td> 
  </tr> 
  <tr> 
   <td>Negative Log Likelihood</td> 
   <td>负对数似然</td> 
  </tr> 
  <tr> 
   <td>Negative semideﬁnite</td> 
   <td>半负定</td> 
  </tr> 
  <tr> 
   <td>Neighbourhood Component Analysis/NCA</td> 
   <td>近邻成分分析</td> 
  </tr> 
  <tr> 
   <td>Neural Machine Translation</td> 
   <td>神经机器翻译</td> 
  </tr> 
  <tr> 
   <td>Newton method</td> 
   <td>牛顿法</td> 
  </tr> 
  <tr> 
   <td>Conference on Neural Information Processing Systems/NIPS</td> 
   <td>国际神经信息处理系统会议</td> 
  </tr> 
  <tr> 
   <td>No Free Lunch Theorem/NFL</td> 
   <td>没有免费的午餐定理</td> 
  </tr> 
  <tr> 
   <td>Noise-contrastive estimation</td> 
   <td>噪音对比估计</td> 
  </tr> 
  <tr> 
   <td>Nominal attribute</td> 
   <td>列名属性</td> 
  </tr> 
  <tr> 
   <td>Non-convex optimization</td> 
   <td>非凸优化</td> 
  </tr> 
  <tr> 
   <td>Nonlinear model</td> 
   <td>非线性模型</td> 
  </tr> 
  <tr> 
   <td>Non-linear oscillation</td> 
   <td>非线性振荡</td> 
  </tr> 
  <tr> 
   <td>Non-metric distance</td> 
   <td>非度量距离</td> 
  </tr> 
  <tr> 
   <td>Non-negative matrix factorization</td> 
   <td>非负矩阵分解</td> 
  </tr> 
  <tr> 
   <td>Non-ordinal attribute</td> 
   <td>无序属性</td> 
  </tr> 
  <tr> 
   <td>Non-Saturating Game</td> 
   <td>非饱和博弈</td> 
  </tr> 
  <tr> 
   <td>Norm</td> 
   <td>范数</td> 
  </tr> 
  <tr> 
   <td>Normalization</td> 
   <td>归一化</td> 
  </tr> 
  <tr> 
   <td>Nuclear norm</td> 
   <td>核范数</td> 
  </tr> 
  <tr> 
   <td>Numerical attribute</td> 
   <td>数值属性</td> 
  </tr> 
  <tr> 
   <td>Numerical optimization</td> 
   <td>数值优化</td> 
  </tr> 
  <tr> 
   <td>Objective function</td> 
   <td>目标函数</td> 
  </tr> 
  <tr> 
   <td>Oblique decision tree</td> 
   <td>斜决策树</td> 
  </tr> 
  <tr> 
   <td>Occam’s razor</td> 
   <td>奥卡姆剃刀</td> 
  </tr> 
  <tr> 
   <td>Odds</td> 
   <td>几率</td> 
  </tr> 
  <tr> 
   <td>Offline inference</td> 
   <td>离线推断</td> 
  </tr> 
  <tr> 
   <td>Off-Policy</td> 
   <td>离策略</td> 
  </tr> 
  <tr> 
   <td>Offset vector</td> 
   <td>偏移向量</td> 
  </tr> 
  <tr> 
   <td>One shot learning</td> 
   <td>一次性学习</td> 
  </tr> 
  <tr> 
   <td>One-Dependent Estimator/ODE</td> 
   <td>独依赖估计</td> 
  </tr> 
  <tr> 
   <td>Online inference</td> 
   <td>在线推断</td> 
  </tr> 
  <tr> 
   <td>On-Policy</td> 
   <td>在策略</td> 
  </tr> 
  <tr> 
   <td>Ordinal attribute</td> 
   <td>有序属性</td> 
  </tr> 
  <tr> 
   <td>Orthogonal matrix</td> 
   <td>正交矩阵</td> 
  </tr> 
  <tr> 
   <td>Orthonormal</td> 
   <td>标准正交</td> 
  </tr> 
  <tr> 
   <td>Outlier</td> 
   <td>异常值/离群值 [[5]]</td> 
  </tr> 
  <tr> 
   <td>Out-of-bag estimate</td> 
   <td>包外估计</td> 
  </tr> 
  <tr> 
   <td>Output layer</td> 
   <td>输出层</td> 
  </tr> 
  <tr> 
   <td>Output smearing</td> 
   <td>输出调制法</td> 
  </tr> 
  <tr> 
   <td>Overcomplete</td> 
   <td>过完备</td> 
  </tr> 
  <tr> 
   <td>Overfitting</td> 
   <td>过拟合／过配</td> 
  </tr> 
  <tr> 
   <td>Oversampling</td> 
   <td>过采样</td> 
  </tr> 
  <tr> 
   <td>Paired t-test</td> 
   <td>成对 t 检验</td> 
  </tr> 
  <tr> 
   <td>Pairwise</td> 
   <td>成对型</td> 
  </tr> 
  <tr> 
   <td>Pairwise Markov property</td> 
   <td>成对马尔可夫性</td> 
  </tr> 
  <tr> 
   <td>Parallel tempering</td> 
   <td>并行回火</td> 
  </tr> 
  <tr> 
   <td>Parameter</td> 
   <td>参数</td> 
  </tr> 
  <tr> 
   <td>Parameter estimation</td> 
   <td>参数估计</td> 
  </tr> 
  <tr> 
   <td>Parameter Server</td> 
   <td>参数服务器</td> 
  </tr> 
  <tr> 
   <td>Parameter tuning</td> 
   <td>调参</td> 
  </tr> 
  <tr> 
   <td>Parse tree</td> 
   <td>解析树</td> 
  </tr> 
  <tr> 
   <td>Partial derivative</td> 
   <td>偏导数</td> 
  </tr> 
  <tr> 
   <td>Particle Swarm Optimization/PSO</td> 
   <td>粒子群优化算法</td> 
  </tr> 
  <tr> 
   <td>Part-of-speech tagging</td> 
   <td>词性标注</td> 
  </tr> 
  <tr> 
   <td>Perceptron</td> 
   <td>感知机</td> 
  </tr> 
  <tr> 
   <td>Performance measure</td> 
   <td>性能度量</td> 
  </tr> 
  <tr> 
   <td>Permutation invariant</td> 
   <td>置换不变性</td> 
  </tr> 
  <tr> 
   <td>Perplexity</td> 
   <td>困惑度</td> 
  </tr> 
  <tr> 
   <td>Pictorial structure</td> 
   <td>图形结构</td> 
  </tr> 
  <tr> 
   <td>Plug and Play Generative Network</td> 
   <td>即插即用生成网络</td> 
  </tr> 
  <tr> 
   <td>Plurality voting</td> 
   <td>相对多数投票法</td> 
  </tr> 
  <tr> 
   <td>Polarity detection</td> 
   <td>极性检测</td> 
  </tr> 
  <tr> 
   <td>Polynomial Basis Function</td> 
   <td>多项式基函数</td> 
  </tr> 
  <tr> 
   <td>Polynomial kernel function</td> 
   <td>多项式核函数</td> 
  </tr> 
  <tr> 
   <td>Pooling</td> 
   <td>池化</td> 
  </tr> 
  <tr> 
   <td>Positive class</td> 
   <td>正类</td> 
  </tr> 
  <tr> 
   <td>Positive definite matrix</td> 
   <td>正定矩阵</td> 
  </tr> 
  <tr> 
   <td>Posterior inference</td> 
   <td>后验推断</td> 
  </tr> 
  <tr> 
   <td>Posterior probability</td> 
   <td>后验概率</td> 
  </tr> 
  <tr> 
   <td>Post-hoc test</td> 
   <td>后续检验</td> 
  </tr> 
  <tr> 
   <td>Post-pruning</td> 
   <td>后剪枝</td> 
  </tr> 
  <tr> 
   <td>potential function</td> 
   <td>势函数</td> 
  </tr> 
  <tr> 
   <td>Power method</td> 
   <td>幂方法</td> 
  </tr> 
  <tr> 
   <td>Precision</td> 
   <td>查准率／精确率 [[5]]</td> 
  </tr> 
  <tr> 
   <td>Prepruning</td> 
   <td>预剪枝</td> 
  </tr> 
  <tr> 
   <td>Principal component analysis/PCA</td> 
   <td>主成分分析</td> 
  </tr> 
  <tr> 
   <td>Principle of multiple explanations</td> 
   <td>多释原则</td> 
  </tr> 
  <tr> 
   <td>Prior knowledge</td> 
   <td>先验知识</td> 
  </tr> 
  <tr> 
   <td>Probability Graphical Model</td> 
   <td>概率图模型</td> 
  </tr> 
  <tr> 
   <td>Proximal Gradient Descent/PGD</td> 
   <td>近端梯度下降</td> 
  </tr> 
  <tr> 
   <td>Pruning</td> 
   <td>剪枝</td> 
  </tr> 
  <tr> 
   <td>Pseudo-label</td> 
   <td>伪标记</td> 
  </tr> 
  <tr> 
   <td>Quadratic Programming</td> 
   <td>二次规划</td> 
  </tr> 
  <tr> 
   <td>Quantized Neural Network/QNN</td> 
   <td>量子化神经网络</td> 
  </tr> 
  <tr> 
   <td>Quantum computer</td> 
   <td>量子计算机</td> 
  </tr> 
  <tr> 
   <td>Quantum Computing</td> 
   <td>量子计算</td> 
  </tr> 
  <tr> 
   <td>Quantum machine learning</td> 
   <td>量子机器学习</td> 
  </tr> 
  <tr> 
   <td>Quasi Newton method</td> 
   <td>拟牛顿法</td> 
  </tr> 
  <tr> 
   <td>Quasi-concave</td> 
   <td>拟凹</td> 
  </tr> 
  <tr> 
   <td>Radial Basis Function/RBF</td> 
   <td>径向基函数</td> 
  </tr> 
  <tr> 
   <td>Random Forest Algorithm</td> 
   <td>随机森林算法</td> 
  </tr> 
  <tr> 
   <td>Random walk</td> 
   <td>随机漫步</td> 
  </tr> 
  <tr> 
   <td>Recall</td> 
   <td>召回率／查全率</td> 
  </tr> 
  <tr> 
   <td>Receiver Operating Characteristic/ROC</td> 
   <td>受试者工作特征</td> 
  </tr> 
  <tr> 
   <td>Rectified Linear Unit/ReLU</td> 
   <td>线性修正单元</td> 
  </tr> 
  <tr> 
   <td>Recurrent Neural Network</td> 
   <td>循环神经网络</td> 
  </tr> 
  <tr> 
   <td>Recursive neural network</td> 
   <td>递归神经网络</td> 
  </tr> 
  <tr> 
   <td>Reference model</td> 
   <td>参考模型</td> 
  </tr> 
  <tr> 
   <td>Regression</td> 
   <td>回归</td> 
  </tr> 
  <tr> 
   <td>Regularization</td> 
   <td>正则化</td> 
  </tr> 
  <tr> 
   <td>Regularizer</td> 
   <td>正则化项</td> 
  </tr> 
  <tr> 
   <td>Reinforcement learning/RL</td> 
   <td>强化学习</td> 
  </tr> 
  <tr> 
   <td>Relative entropy</td> 
   <td>相对熵</td> 
  </tr> 
  <tr> 
   <td>Reparametrization</td> 
   <td>重参数化</td> 
  </tr> 
  <tr> 
   <td>Representation learning</td> 
   <td>表征学习</td> 
  </tr> 
  <tr> 
   <td>Representer theorem</td> 
   <td>表示定理</td> 
  </tr> 
  <tr> 
   <td>Reproducing Kernel Hilbert Space/RKHS</td> 
   <td>再生核希尔伯特空间</td> 
  </tr> 
  <tr> 
   <td>Re-sampling</td> 
   <td>重采样法</td> 
  </tr> 
  <tr> 
   <td>Rescaling</td> 
   <td>再缩放</td> 
  </tr> 
  <tr> 
   <td>Reservoir computing</td> 
   <td>储层计算</td> 
  </tr> 
  <tr> 
   <td>Residual Blocks</td> 
   <td>残差块</td> 
  </tr> 
  <tr> 
   <td>Residual Mapping</td> 
   <td>残差映射</td> 
  </tr> 
  <tr> 
   <td>Residual Network</td> 
   <td>残差网络</td> 
  </tr> 
  <tr> 
   <td>Restricted Boltzmann Machine/RBM</td> 
   <td>受限玻尔兹曼机</td> 
  </tr> 
  <tr> 
   <td>Restricted Isometry Property/RIP</td> 
   <td>限定等距性</td> 
  </tr> 
  <tr> 
   <td>Reverse mode accumulation</td> 
   <td>反向模式累加</td> 
  </tr> 
  <tr> 
   <td>Re-weighting</td> 
   <td>重赋权法</td> 
  </tr> 
  <tr> 
   <td>Ridge regression</td> 
   <td>岭回归</td> 
  </tr> 
  <tr> 
   <td>Robustness</td> 
   <td>稳健性/鲁棒性</td> 
  </tr> 
  <tr> 
   <td>Root node</td> 
   <td>根结点</td> 
  </tr> 
  <tr> 
   <td>Rule Engine</td> 
   <td>规则引擎</td> 
  </tr> 
  <tr> 
   <td>Rule learning</td> 
   <td>规则学习</td> 
  </tr> 
  <tr> 
   <td>Saddle point</td> 
   <td>鞍点</td> 
  </tr> 
  <tr> 
   <td>Saddle-free Newton method</td> 
   <td>无鞍牛顿法</td> 
  </tr> 
  <tr> 
   <td>Saliency map</td> 
   <td>显著图</td> 
  </tr> 
  <tr> 
   <td>Sample space</td> 
   <td>样本空间</td> 
  </tr> 
  <tr> 
   <td>Sampling</td> 
   <td>采样</td> 
  </tr> 
  <tr> 
   <td>Score function</td> 
   <td>评分函数</td> 
  </tr> 
  <tr> 
   <td>Second derivative</td> 
   <td>二阶导数</td> 
  </tr> 
  <tr> 
   <td>Second-order method</td> 
   <td>二阶方法</td> 
  </tr> 
  <tr> 
   <td>Self-contrastive estimation</td> 
   <td>自对比估计</td> 
  </tr> 
  <tr> 
   <td>Self-Driving</td> 
   <td>自动驾驶</td> 
  </tr> 
  <tr> 
   <td>Self-Organizing Map/SOM</td> 
   <td>自组织映射</td> 
  </tr> 
  <tr> 
   <td>Semantic hashing</td> 
   <td>语义哈希</td> 
  </tr> 
  <tr> 
   <td>Semantic segmentation</td> 
   <td>语义分割</td> 
  </tr> 
  <tr> 
   <td>Semantic similarity</td> 
   <td>语义相似度</td> 
  </tr> 
  <tr> 
   <td>Semi-Definite Programming</td> 
   <td>半正定规划</td> 
  </tr> 
  <tr> 
   <td>Semi-naive Bayes classifiers</td> 
   <td>半朴素贝叶斯分类器</td> 
  </tr> 
  <tr> 
   <td>Semi-restricted Boltzmann Machine</td> 
   <td>半受限波尔兹曼机</td> 
  </tr> 
  <tr> 
   <td>Semi-Supervised Learning</td> 
   <td>半监督学习</td> 
  </tr> 
  <tr> 
   <td>semi-Supervised Support Vector Machine</td> 
   <td>半监督支持向量机</td> 
  </tr> 
  <tr> 
   <td>Sentiment analysis</td> 
   <td>情感分析</td> 
  </tr> 
  <tr> 
   <td>Separating hyperplane</td> 
   <td>分离超平面</td> 
  </tr> 
  <tr> 
   <td>Shannon entropy</td> 
   <td>香农熵</td> 
  </tr> 
  <tr> 
   <td>Shift invariance</td> 
   <td>平移不变性</td> 
  </tr> 
  <tr> 
   <td>Siamese Network</td> 
   <td>孪生网络</td> 
  </tr> 
  <tr> 
   <td>Similarity measure</td> 
   <td>相似度度量</td> 
  </tr> 
  <tr> 
   <td>Simulated annealing</td> 
   <td>模拟退火</td> 
  </tr> 
  <tr> 
   <td>Simultaneous localization and mapping/SLAM</td> 
   <td>同步定位与地图构建</td> 
  </tr> 
  <tr> 
   <td>Singular value</td> 
   <td>奇异值</td> 
  </tr> 
  <tr> 
   <td>Singular Value Decomposition</td> 
   <td>奇异值分解</td> 
  </tr> 
  <tr> 
   <td>Slack variables</td> 
   <td>松弛变量</td> 
  </tr> 
  <tr> 
   <td>Slowness principle</td> 
   <td>慢性原则</td> 
  </tr> 
  <tr> 
   <td>Smoothing</td> 
   <td>平滑</td> 
  </tr> 
  <tr> 
   <td>Smoothness prior</td> 
   <td>平滑先验</td> 
  </tr> 
  <tr> 
   <td>Soft margin</td> 
   <td>软间隔</td> 
  </tr> 
  <tr> 
   <td>Soft margin maximization</td> 
   <td>软间隔最大化</td> 
  </tr> 
  <tr> 
   <td>Soft voting</td> 
   <td>软投票</td> 
  </tr> 
  <tr> 
   <td>Sparse activation</td> 
   <td>稀疏激活</td> 
  </tr> 
  <tr> 
   <td>Sparse coding</td> 
   <td>稀疏编码</td> 
  </tr> 
  <tr> 
   <td>Sparse connectivity</td> 
   <td>稀疏连接</td> 
  </tr> 
  <tr> 
   <td>Sparse initialization</td> 
   <td>稀疏初始化</td> 
  </tr> 
  <tr> 
   <td>Sparse representation</td> 
   <td>稀疏表征</td> 
  </tr> 
  <tr> 
   <td>Sparsity</td> 
   <td>稀疏性</td> 
  </tr> 
  <tr> 
   <td>Specialization</td> 
   <td>特化</td> 
  </tr> 
  <tr> 
   <td>Spectral Clustering</td> 
   <td>谱聚类</td> 
  </tr> 
  <tr> 
   <td>Spectral radius</td> 
   <td>谱半径</td> 
  </tr> 
  <tr> 
   <td>Speech Recognition</td> 
   <td>语音识别</td> 
  </tr> 
  <tr> 
   <td>Spiking Neural Nets</td> 
   <td>脉冲神经网络</td> 
  </tr> 
  <tr> 
   <td>Splitting variable</td> 
   <td>切分变量</td> 
  </tr> 
  <tr> 
   <td>Squashing function</td> 
   <td>挤压函数</td> 
  </tr> 
  <tr> 
   <td>Stability-plasticity dilemma</td> 
   <td>可塑性-稳定性困境</td> 
  </tr> 
  <tr> 
   <td>Stacked Deconvolutional Network/SDN</td> 
   <td>堆叠解卷积网络</td> 
  </tr> 
  <tr> 
   <td>Standard deviation</td> 
   <td>标准差</td> 
  </tr> 
  <tr> 
   <td>Static game</td> 
   <td>静态博弈</td> 
  </tr> 
  <tr> 
   <td>Stationary distribution</td> 
   <td>稳态分布</td> 
  </tr> 
  <tr> 
   <td>Stationary point</td> 
   <td>驻点</td> 
  </tr> 
  <tr> 
   <td>Statistical learning</td> 
   <td>统计学习</td> 
  </tr> 
  <tr> 
   <td>Status feature function</td> 
   <td>状态特征函数</td> 
  </tr> 
  <tr> 
   <td>Stochastic gradient descent</td> 
   <td>随机梯度下降</td> 
  </tr> 
  <tr> 
   <td>Stochastic Matrix</td> 
   <td>随机矩阵</td> 
  </tr> 
  <tr> 
   <td>Stochastic maximum likelihood</td> 
   <td>随机最大似然</td> 
  </tr> 
  <tr> 
   <td>Stochastic Neighbor Embedding</td> 
   <td>随机近邻嵌入</td> 
  </tr> 
  <tr> 
   <td>Stratified sampling</td> 
   <td>分层采样</td> 
  </tr> 
  <tr> 
   <td>Structural risk</td> 
   <td>结构风险</td> 
  </tr> 
  <tr> 
   <td>Structural risk minimization/SRM</td> 
   <td>结构风险最小化</td> 
  </tr> 
  <tr> 
   <td>Structured variational inference</td> 
   <td>结构化变分推断</td> 
  </tr> 
  <tr> 
   <td>Subsampling</td> 
   <td>下采样</td> 
  </tr> 
  <tr> 
   <td>Subspace</td> 
   <td>子空间</td> 
  </tr> 
  <tr> 
   <td>Supervised learning</td> 
   <td>监督学习／有导师学习</td> 
  </tr> 
  <tr> 
   <td>support vector expansion</td> 
   <td>支持向量展式</td> 
  </tr> 
  <tr> 
   <td>Support Vector Machine/SVM</td> 
   <td>支持向量机</td> 
  </tr> 
  <tr> 
   <td>Surrogat loss</td> 
   <td>替代损失</td> 
  </tr> 
  <tr> 
   <td>Surrogate function</td> 
   <td>替代函数</td> 
  </tr> 
  <tr> 
   <td>Symbolic learning</td> 
   <td>符号学习</td> 
  </tr> 
  <tr> 
   <td>Symbolism</td> 
   <td>符号主义</td> 
  </tr> 
  <tr> 
   <td>Synset</td> 
   <td>同义词集</td> 
  </tr> 
  <tr> 
   <td>Synthetic feature</td> 
   <td>合成特征</td> 
  </tr> 
  <tr> 
   <td>Tangent plane</td> 
   <td>切平面</td> 
  </tr> 
  <tr> 
   <td>Tangent prop</td> 
   <td>正切传播</td> 
  </tr> 
  <tr> 
   <td>T-Distribution Stochastic Neighbour Embedding/t-SNE</td> 
   <td>T分布随机近邻嵌入</td> 
  </tr> 
  <tr> 
   <td>Tempered transition</td> 
   <td>回火转移</td> 
  </tr> 
  <tr> 
   <td>Tensor</td> 
   <td>张量</td> 
  </tr> 
  <tr> 
   <td>Tensor Processing Units/TPU</td> 
   <td>张量处理单元</td> 
  </tr> 
  <tr> 
   <td>The least square method</td> 
   <td>最小二乘法</td> 
  </tr> 
  <tr> 
   <td>Threshold</td> 
   <td>阈值</td> 
  </tr> 
  <tr> 
   <td>Threshold logic unit</td> 
   <td>阈值逻辑单元</td> 
  </tr> 
  <tr> 
   <td>Threshold-moving</td> 
   <td>阈值移动</td> 
  </tr> 
  <tr> 
   <td>Tiled convolution</td> 
   <td>平铺卷积</td> 
  </tr> 
  <tr> 
   <td>Time delay neural network</td> 
   <td>时延神经网络</td> 
  </tr> 
  <tr> 
   <td>Time Step</td> 
   <td>时间步骤</td> 
  </tr> 
  <tr> 
   <td>Tractable</td> 
   <td>易处理的</td> 
  </tr> 
  <tr> 
   <td>Tokenization</td> 
   <td>标记化/分词</td> 
  </tr> 
  <tr> 
   <td>Training error</td> 
   <td>训练误差</td> 
  </tr> 
  <tr> 
   <td>Training instance</td> 
   <td>训练实例</td> 
  </tr> 
  <tr> 
   <td>Transductive learning</td> 
   <td>直推学习</td> 
  </tr> 
  <tr> 
   <td>Transfer learning</td> 
   <td>迁移学习/转移学习</td> 
  </tr> 
  <tr> 
   <td>Treebank</td> 
   <td>树库</td> 
  </tr> 
  <tr> 
   <td>Trial-by-error</td> 
   <td>试错法</td> 
  </tr> 
  <tr> 
   <td>Triangulate</td> 
   <td>三角形化</td> 
  </tr> 
  <tr> 
   <td>Trigram</td> 
   <td>三元语法</td> 
  </tr> 
  <tr> 
   <td>True negative</td> 
   <td>真负例 /[5]</td> 
  </tr> 
  <tr> 
   <td>True positive</td> 
   <td>真正例 /[5]</td> 
  </tr> 
  <tr> 
   <td>True Positive Rate/TPR</td> 
   <td>真正例率</td> 
  </tr> 
  <tr> 
   <td>Turing Machine</td> 
   <td>图灵机</td> 
  </tr> 
  <tr> 
   <td>Twice-learning</td> 
   <td>二次学习</td> 
  </tr> 
  <tr> 
   <td>Two-dimensional array</td> 
   <td>二维数组</td> 
  </tr> 
  <tr> 
   <td>Underestimation</td> 
   <td>欠估计</td> 
  </tr> 
  <tr> 
   <td>Underfitting</td> 
   <td>欠拟合／欠配</td> 
  </tr> 
  <tr> 
   <td>Undersampling</td> 
   <td>欠采样</td> 
  </tr> 
  <tr> 
   <td>Understandability</td> 
   <td>可理解性</td> 
  </tr> 
  <tr> 
   <td>Undirected graphical model</td> 
   <td>无向图模型</td> 
  </tr> 
  <tr> 
   <td>Unequal cost</td> 
   <td>非均等代价</td> 
  </tr> 
  <tr> 
   <td>Unit norm</td> 
   <td>单位范数</td> 
  </tr> 
  <tr> 
   <td>Unit test</td> 
   <td>单元测试</td> 
  </tr> 
  <tr> 
   <td>Unit variance</td> 
   <td>单位方差</td> 
  </tr> 
  <tr> 
   <td>Unitary matrix</td> 
   <td>酉矩阵</td> 
  </tr> 
  <tr> 
   <td>Unit-step function</td> 
   <td>单位阶跃函数</td> 
  </tr> 
  <tr> 
   <td>Univariate decision tree</td> 
   <td>单变量决策树</td> 
  </tr> 
  <tr> 
   <td>Unprojection</td> 
   <td>反投影</td> 
  </tr> 
  <tr> 
   <td>Unshared convolution</td> 
   <td>非共享卷积</td> 
  </tr> 
  <tr> 
   <td>Unsupervised &nbsp;learning</td> 
   <td>无监督学习／无导师学习</td> 
  </tr> 
  <tr> 
   <td>Unsupervised layer-wise training</td> 
   <td>无监督逐层训练</td> 
  </tr> 
  <tr> 
   <td>Upper Confidence Bounds</td> 
   <td>上置信界限</td> 
  </tr> 
  <tr> 
   <td>Upsampling</td> 
   <td>上采样</td> 
  </tr> 
  <tr> 
   <td>Vanishing Gradient Problem</td> 
   <td>梯度消失问题</td> 
  </tr> 
  <tr> 
   <td>Variational derivative</td> 
   <td>变分导数</td> 
  </tr> 
  <tr> 
   <td>Variational free energy</td> 
   <td>变分自由能</td> 
  </tr> 
  <tr> 
   <td>Variational inference</td> 
   <td>变分推断</td> 
  </tr> 
  <tr> 
   <td>VC Theory</td> 
   <td>VC维理论</td> 
  </tr> 
  <tr> 
   <td>Version space</td> 
   <td>版本空间</td> 
  </tr> 
  <tr> 
   <td>Virtual adversarial example</td> 
   <td>虚拟对抗样本</td> 
  </tr> 
  <tr> 
   <td>Viterbi algorithm</td> 
   <td>维特比算法</td> 
  </tr> 
  <tr> 
   <td>Von Neumann architecture</td> 
   <td>冯 · 诺伊曼架构</td> 
  </tr> 
  <tr> 
   <td>Weak learner</td> 
   <td>弱学习器</td> 
  </tr> 
  <tr> 
   <td>Weight</td> 
   <td>权重</td> 
  </tr> 
  <tr> 
   <td>Weight decaying</td> 
   <td>权值衰减</td> 
  </tr> 
  <tr> 
   <td>Weight sharing</td> 
   <td>权共享</td> 
  </tr> 
  <tr> 
   <td>Weighted voting</td> 
   <td>加权投票法</td> 
  </tr> 
  <tr> 
   <td>Wasserstein GAN/WGAN</td> 
   <td>Wasserstein生成对抗网络</td> 
  </tr> 
  <tr> 
   <td>Within-class scatter matrix</td> 
   <td>类内散度矩阵</td> 
  </tr> 
  <tr> 
   <td>Word embedding</td> 
   <td>词嵌入</td> 
  </tr> 
  <tr> 
   <td>Word sense disambiguation</td> 
   <td>词义消歧</td> 
  </tr> 
  <tr> 
   <td>Zero mean</td> 
   <td>零均值</td> 
  </tr> 
  <tr> 
   <td>Zero-data learning</td> 
   <td>零数据学习</td> 
  </tr> 
  <tr> 
   <td>Zero-shot learning</td> 
   <td>零次学习</td> 
  </tr> 
 </tbody> 
</table>