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 |
(在深度学习网络的训练过程中,对于神经网络单元,按照一定的概率将其暂时从网络中)丢弃 |
|
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 |
|
|