人工智能相关术语（按首字母排序）
| 缩写 | 英语 | 汉语 |
|---|---|---|
| A | ||
| Activation Function | 激活函数 | |
| Adversarial Networks | 对抗网络 | |
| Affine Layer | 仿射层 | |
| agent | 代理/智能体 | |
| algorithm | 算法 | |
| alpha-beta pruning | α-β剪枝 | |
| anomaly detection | 异常检测 | |
| approximation | 近似 | |
| AGI | Artificial General Intelligence | 通用人工智能 |
| AI | Artificial Intelligence | 人工智能 |
| association analysis | 关联分析 | |
| attention mechanism | 注意机制 | |
| autoencoder | 自编码器 | |
| ASR | automatic speech recognition | 自动语音识别 |
| automatic summarization | 自动摘要 | |
| average gradient | 平均梯度 | |
| Average-Pooling | 平均池化 | |
| B | ||
| BP | backpropagation | 反向传播 |
| BPTT | Backpropagation Through Time | 通过时间的反向传播 |
| BN | Batch Normalization | 分批标准化 |
| Bayesian network | 贝叶斯网络 | |
| Bias-Variance Dilemma | 偏差/方差困境 | |
| Bi-LSTM | Bi-directional Long-Short Term Memory | 双向长短期记忆 |
| bias | 偏置/偏差 | |
| big data | 大数据 | |
| Boltzmann machine | 玻尔兹曼机 | |
| C | ||
| CPU | Central Processing Unit | 中央处理器 |
| chunk | 词块 | |
| clustering | 聚类 | |
| cluster analysis | 聚类分析 | |
| co-adapting | 共适应 | |
| co-occurrence | 共现 | |
| Computation Cost | 计算成本 | |
| Computational Linguistics | 计算语言学 | |
| computer vision | 计算机视觉 | |
| concept drift | 概念漂移 | |
| CRF | conditional random field | 条件随机域/场 |
| convergence | 收敛 | |
| CA | conversational agent | 会话代理 |
| convexity | 凸性 | |
| CNN | convolutional neural network | 卷积神经网络 |
| Cost Function | 成本函数 | |
| cross entropy | 交叉熵 | |
| D | ||
| Decision Boundary | 决策边界 | |
| Decision Trees | 决策树 | |
| DBN | Deep Belief Network | 深度信念网络 |
| DCGAN | Deep Convolutional Generative Adversarial Network | 深度卷积生成对抗网络 |
| DL | deep learning | 深度学习 |
| DNN | deep neural network | 深度神经网络 |
| Deep Q-Learning | 深度Q学习 | |
| DQN | Deep Q-Network | 深度Q网络 |
| DNC | differentiable neural computer | 可微分神经计算机 |
| dimensionality reduction algorithm | 降维算法 | |
| discriminative model | 判别模型 | |
| discriminator | 判别器 | |
| divergence | 散度 | |
| domain adaption | 领域自适应 | |
| Dropout | ||
| Dynamic Fusion | 动态融合 | |
| E | ||
| Embedding | 嵌入 | |
| emotional analysis | 情绪分析 | |
| End-to-End | 端到端 | |
| EM | Expectation-Maximization | 期望最大化 |
| Exploding Gradient Problem | 梯度爆炸问题 | |
| ELM | Extreme Learning Machine | 超限学习机 |
| F | ||
| FAIR | Facebook Artificial Intelligence Research | Facebook人工智能研究所 |
| factorization | 因子分解 | |
| feature engineering | 特征工程 | |
| Featured Learning | 特征学习 | |
| Feedforward Neural Networks | 前馈神经网络 | |
| G | ||
| game theory | 博弈论 | |
| GMM | Gaussian Mixture Model | 高斯混合模型 |
| GA | Genetic Algorithm | 遗传算法 |
| Generalization | 泛化 | |
| GAN | Generative Adversarial Networks | 生成对抗网络 |
| Generative Model | 生成模型 | |
| Generator | 生成器 | |
| Global Optimization | 全局优化 | |
| GNMT | Google Neural Machine Translation | 谷歌神经机器翻译 |
| Gradient Descent | 梯度下降 | |
| graph theory | 图论 | |
| GPU | graphics processing unit | 图形处理单元/图形处理器 |
| H | ||
| HDM | hidden dynamic model | 隐动态模型 |
| hidden layer | 隐藏层 | |
| HMM | Hidden Markov Model | 隐马尔可夫模型 |
| hybrid computing | 混合计算 | |
| hyperparameter | 超参数 | |
| I | ||
| ICA | Independent Component Analysis | 独立成分分析 |
| input | 输入 | |
| ICML | International Conference for Machine Learning | 国际机器学习大会 |
| language phenomena | 语言现象 | |
| latent dirichlet allocation | 隐含狄利克雷分布 | |
| J | ||
| JSD | Jensen-Shannon Divergence | JS距离 |
| K | ||
| K-Means Clustering | K-均值聚类 | |
| K-NN | K-Nearest Neighbours Algorithm | K-最近邻算法 |
| Knowledge Representation | 知识表征 | |
| KB | knowledge base | 知识库 |
| L | ||
| Latent Dirichlet Allocation | 隐狄利克雷分布 | |
| LSA | latent semantic analysis | 潜在语义分析 |
| learner | 学习器 | |
| Linear Regression | 线性回归 | |
| log likelihood | 对数似然 | |
| Logistic Regression | Logistic回归 | |
| LSTM | Long-Short Term Memory | 长短期记忆 |
| loss | 损失 | |
| M | ||
| MT | machine translation | 机器翻译 |
| Max-Pooling | 最大池化 | |
| Maximum Likelihood | 最大似然 | |
| minimax game | 最小最大博弈 | |
| Momentum | 动量 | |
| MLP | Multilayer Perceptron | 多层感知器 |
| multi-document summarization | 多文档摘要 | |
| MLP | multi layered perceptron | 多层感知器 |
| multimodal learning | 多模态学习 | |
| multiple linear regression | 多元线性回归 | |
| N | ||
| Naive Bayes Classifier | 朴素贝叶斯分类器 | |
| named entity recognition | 命名实体识别 | |
| Nash equilibrium | 纳什均衡 | |
| NLG | natural language generation | 自然语言生成 |
| NLP | natural language processing | 自然语言处理 |
| NLL | Negative Log Likelihood | 负对数似然 |
| NMT | Neural Machine Translation | 神经机器翻译 |
| NTM | Neural Turing Machine | 神经图灵机 |
| NCE | noise-contrastive estimation | 噪音对比估计 |
| non-convex optimization | 非凸优化 | |
| non-negative matrix factorization | 非负矩阵分解 | |
| Non-Saturating Game | 非饱和博弈 | |
| O | ||
| objective function | 目标函数 | |
| Off-Policy | 离策略 | |
| On-Policy | 在策略 | |
| one shot learning | 一次性学习 | |
| output | 输出 | |
| P | ||
| Parameter | 参数 | |
| parse tree | 解析树 | |
| part-of-speech tagging | 词性标注 | |
| PSO | Particle Swarm Optimization | 粒子群优化算法 |
| perceptron | 感知器 | |
| polarity detection | 极性检测 | |
| pooling | 池化 | |
| PPGN | Plug and Play Generative Network | 即插即用生成网络 |
| PCA | principal component analysis | 主成分分析 |
| Probability Graphical Model | 概率图模型 | |
| Q | ||
| QNN | Quantized Neural Network | 量子化神经网络 |
| quantum computer | 量子计算机 | |
| Quantum Computing | 量子计算 | |
| R | ||
| RBF | Radial Basis Function | 径向基函数 |
| Random Forest Algorithm | 随机森林算法 | |
| ReLU | Rectified Linear Unit | 线性修正单元/线性修正函数 |
| RNN | Recurrent Neural Network | 循环神经网络 |
| recursive neural network | 递归神经网络 | |
| RL | reinforcement learning | 强化学习 |
| representation | 表征 | |
| representation learning | 表征学习 | |
| Residual Mapping | 残差映射 | |
| Residual Network | 残差网络 | |
| RBM | Restricted Boltzmann Machine | 受限玻尔兹曼机 |
| Robot | 机器人 | |
| Robustness | 稳健性 | |
| RE | Rule Engine | 规则引擎 |
| S | ||
| saddle point | 鞍点 | |
| Self-Driving | 自动驾驶 | |
| SOM | self organised map | 自组织映射 |
| Semi-Supervised Learning | 半监督学习 | |
| sentiment analysis | 情感分析 | |
| SLAM | simultaneous localization and mapping | 同步定位与地图构建 |
| SVD | Singular Value Decomposition | 奇异值分解 |
| Spectral Clustering | 谱聚类 | |
| Speech Recognition | 语音识别 | |
| SGD | stochastic gradient descent | 随机梯度下降 |
| supervised learning | 监督学习 | |
| SVM | Support Vector Machine | 支持向量机 |
| synset | 同义词集 | |
| T | ||
| t-SNE | T-Distribution Stochastic Neighbour Embedding | T-分布随机近邻嵌入 |
| tensor | 张量 | |
| TPU | Tensor Processing Units | 张量处理单元 |
| the least square method | 最小二乘法 | |
| Threshold | 阙值 | |
| Time Step | 时间步骤 | |
| tokenization | 标记化 | |
| treebank | 树库 | |
| transfer learning | 迁移学习 | |
| Turing Machine | 图灵机 | |
| U | ||
| unsupervised learning | 无监督学习 | |
| V | ||
| Vanishing Gradient Problem | 梯度消失问题 | |
| VC Theory | Vapnik–Chervonenkis theory | 万普尼克-泽范兰杰斯理论 |
| von Neumann architecture | 冯·诺伊曼架构/结构 | |
| W | ||
| WGAN | Wasserstein GAN | |
| W | weight | 权重 |
| word embedding | 词嵌入 | |
| WSD | word sense disambiguation | 词义消歧 |
| X | ||
| Y | ||
| Z | ||
| ZSL | zero-shot learning | 零次学习 |
| zero-data learning | 零数据学习 | |
| 0 |
转自GitHub
