| 深度学习 |
deep learning |
|
| 机器学习 |
machine learning |
|
| 机器学习模型 |
machine learning model |
|
| 逻辑回归 |
logistic regression |
|
| 回归 |
regression |
|
| 人工智能 |
artificial intelligence |
|
| 朴素贝叶斯 |
naive Bayes |
|
| 表示 |
representation |
|
| 表示学习 |
representation learning |
|
| 自编码器 |
autoencoder |
|
| 编码器 |
encoder |
|
| 解码器 |
decoder |
|
| 多层感知机 |
multilayer perceptron |
|
| 人工神经网络 |
artificial neural network |
|
| 神经网络 |
neural network |
|
| 随机梯度下降 |
stochastic gradient descent |
SGD |
| 线性模型 |
linear model |
|
| 线性回归 |
linear regression |
|
| 整流线性单元 |
rectified linear unit |
ReLU |
| 分布式表示 |
distributed representation |
|
| 非分布式表示 |
nondistributed representation |
|
| 非分布式 |
nondistributed |
|
| 隐藏单元 |
hidden unit |
|
| 长短期记忆 |
long short-term memory |
LSTM |
| 深度信念网络 |
deep belief network |
DBN |
| 循环神经网络 |
recurrent neural network |
RNN |
| 循环 |
recurrence |
|
| 强化学习 |
reinforcement learning |
|
| 推断 |
inference |
|
| 上溢 |
overflow |
|
| 下溢 |
underflow |
|
| softmax函数 |
softmax function |
|
| softmax |
softmax |
|
| 欠估计 |
underestimation |
|
| 过估计 |
overestimation |
|
| 病态条件 |
poor conditioning |
|
| 目标函数 |
objective function |
|
| 目标 |
objective |
|
| 准则 |
criterion |
|
| 代价函数 |
cost function |
|
| 代价 |
cost |
|
| 损失函数 |
loss function |
|
| PR曲线 |
PR curve |
|
| F值 |
F-score |
|
| 损失 |
loss |
|
| 误差函数 |
error function |
|
| 梯度下降 |
gradient descent |
|
| 导数 |
derivative |
|
| 临界点 |
critical point |
|
| 驻点 |
stationary point |
|
| 局部极小点 |
local minimum |
|
| 极小点 |
minimum |
|
| 局部极小值 |
local minima |
|
| 极小值 |
minima |
|
| 全局极小值 |
global minima |
|
| 局部极大值 |
local maxima |
|
| 极大值 |
maxima |
|
| 局部极大点 |
local maximum |
|
| 鞍点 |
saddle point |
|
| 全局最小点 |
global minimum |
|
| 偏导数 |
partial derivative |
|
| 梯度 |
gradient |
|
| 样本 |
example |
|
| 二阶导数 |
second derivative |
|
| 曲率 |
curvature |
|
| 凸优化 |
Convex optimization |
|
| 非凸 |
nonconvex |
|
| 数值优化 |
numerical optimization |
|
| 约束优化 |
constrained optimization |
|
| 可行 |
feasible |
|
| 等式约束 |
equality constraint |
|
| 不等式约束 |
inequality constraint |
|
| 正则化 |
regularization |
|
| 正则化项 |
regularizer |
|
| 正则化 |
regularize |
|
| 泛化 |
generalization |
|
| 泛化 |
generalize |
|
| 欠拟合 |
underfitting |
|
| 过拟合 |
overfitting |
|
| 偏差 |
biass |
|
| 方差 |
variance |
|
| 集成 |
ensemble |
|
| 估计 |
estimator |
|
| 权重衰减 |
weight decay |
|
| 协方差 |
covariance |
|
| 稀疏 |
sparse |
|
| 特征选择 |
feature selection |
|
| 特征提取器 |
feature extractor |
|
| 最大后验 |
Maximum A Posteriori |
MAP |
| 池化 |
pooling |
|
| Dropout |
Dropout |
|
| 蒙特卡罗 |
Monte Carlo |
|
| 提前终止 |
early stopping |
|
| 卷积神经网络 |
convolutional neural network |
CNN |
| 小批量 |
minibatch |
|
| 重要采样 |
Importance Sampling |
|
| 变分自编码器 |
variational auto-encoder |
VAE |
| 计算机视觉 |
Computer Vision |
CV |
| 语音识别 |
Speech Recognition |
|
| 自然语言处理 |
Natural Language Processing |
NLP |
| 有向模型 |
Directed Model |
|
| 原始采样 |
Ancestral Sampling |
|
| 随机矩阵 |
Stochastic Matrix |
|
| 平稳分布 |
Stationary Distribution |
|
| 均衡分布 |
Equilibrium Distribution |
|
| 索引 |
index of matrix |
|
| 磨合 |
Burning-in |
|
| 混合时间 |
Mixing Time |
|
| 混合 |
Mixing |
|
| Gibbs采样 |
Gibbs Sampling |
|
| 吉布斯步数 |
Gibbs steps |
|
| Bagging |
bootstrap aggregating |
|
| 掩码 |
mask |
|
| 批标准化 |
batch normalization |
|
| 参数共享 |
parameter sharing |
|
| KL散度 |
KL divergence |
|
| 温度 |
temperature |
|
| 临界温度 |
critical temperatures |
|
| 并行回火 |
parallel tempering |
|
| 自动语音识别 |
Automatic Speech Recognition |
ASR |
| 级联 |
coalesced |
|
| 数据并行 |
data parallelism |
|
| 模型并行 |
model parallelism |
|
| 异步随机梯度下降 |
Asynchoronous Stochastic Gradient Descent |
|
| 参数服务器 |
parameter server |
|
| 模型压缩 |
model compression |
|
| 动态结构 |
dynamic structure |
|
| 隐马尔可夫模型 |
Hidden Markov Model |
HMM |
| 高斯混合模型 |
Gaussian Mixture Model |
GMM |
| 转录 |
transcribe |
|
| 主成分分析 |
principal components analysis |
PCA |
| 因子分析 |
factor analysis |
|
| 独立成分分析 |
independent component analysis |
ICA |
| 稀疏编码 |
sparse coding |
|
| 定点运算 |
fixed-point arithmetic |
|
| 浮点运算 |
float-point arithmetic |
|
| 生成模型 |
generative model |
|
| 生成式建模 |
generative modeling |
|
| 数据集增强 |
dataset augmentation |
|
| 白化 |
whitening |
|
| 深度神经网络 |
DNN |
|
| 端到端的 |
end-to-end |
|
| 图模型 |
graphical model |
|
| 有向图模型 |
directed graphical model |
|
| 依赖 |
dependency |
|
| 贝叶斯网络 |
Bayesian network |
|
| 模型平均 |
model averaging |
|
| 声明 |
statement |
|
| 量子力学 |
quantum mechanics |
|
| 亚原子 |
subatomic |
|
| 逼真度 |
fidelity |
|
| 信任度 |
degree of belief |
|
| 频率派概率 |
frequentist probability |
|
| 贝叶斯概率 |
Bayesian probability |
|
| 似然 |
likelihood |
|
| 随机变量 |
random variable |
|
| 概率分布 |
probability distribution |
|
| 联合概率分布 |
joint probability distribution |
|
| 归一化的 |
normalized |
|
| 均匀分布 |
uniform distribution |
|
| 概率密度函数 |
probability density function |
PDF |
| 累积函数 |
cumulative function |
|
| 边缘概率分布 |
marginal probability distribution |
|
| 求和法则 |
sum rule |
|
| 条件概率 |
conditional probability |
|
| 干预查询 |
intervention query |
|
| 因果模型 |
causal modeling |
|
| 因果因子 |
causal factor |
|
| 链式法则 |
chain rule |
|
| 乘法法则 |
product rule |
|
| 相互独立的 |
independent |
|
| 条件独立的 |
conditionally independent |
|
| 期望 |
expectation |
|
| 期望值 |
expected value |
|
| 样本 |
example |
|
| 特征 |
feature |
|
| 准确率 |
accuracy |
|
| 错误率 |
error rate |
|
| 训练集 |
training set |
|
| 解释因子 |
explanatory factort |
|
| 潜在 |
underlying |
|
| 潜在成因 |
underlying cause |
|
| 测试集 |
test set |
|
| 性能度量 |
performance measures |
|
| 经验 |
experience |
|
| 无监督 |
unsupervised |
|
| 有监督 |
supervised |
|
| 半监督 |
semi-supervised |
|
| 监督学习 |
supervised learning |
|
| 无监督学习 |
unsupervised learning |
|
| 数据集 |
dataset |
|
| 数据点 |
data point |
|
| 标签 |
label |
|
| 标注 |
labeled |
|
| 未标注 |
unlabeled |
|
| 目标 |
target |
|
| 强化学习 |
reinforcement learning |
|
| 设计矩阵 |
design matrix |
|
| 参数 |
parameter |
|
| 权重 |
weight |
|
| 均方误差 |
mean squared error |
MSE |
| 正规方程 |
normal equation |
|
| 训练误差 |
training error |
|
| 泛化误差 |
generalization error |
|
| 测试误差 |
test error |
|
| 假设空间 |
hypothesis space |
|
| 容量 |
capacity |
|
| 表示容量 |
representational capacity |
|
| 有效容量 |
effective capacity |
|
| 线性阈值单元 |
linear threshold units |
|
| 非参数 |
non-parametric |
|
| 最近邻回归 |
nearest neighbor regression |
|
| 最近邻 |
nearest neighbor |
|
| 验证集 |
validation set |
|
| 基准 |
bechmark |
|
| 基准 |
baseline |
|
| 点估计 |
point estimator |
|
| 估计量 |
estimator |
|
| 统计量 |
statistics |
|
| 无偏 |
unbiased |
|
| 有偏 |
biased |
|
| 异步 |
asynchronous |
|
| 渐近无偏 |
asymptotically unbiased |
|
| 标准差 |
standard error |
|
| 一致性 |
consistency |
|
| 统计效率 |
statistic efficiency |
|
| 有参情况 |
parametric case |
|
| 贝叶斯统计 |
Bayesian statistics |
|
| 先验概率分布 |
prior probability distribution |
|
| 最大后验 |
maximum a posteriori |
|
| 最大似然估计 |
maximum likelihood estimation |
|
| 最大似然 |
maximum likelihood |
|
| 核技巧 |
kernel trick |
|
| 核函数 |
kernel function |
|
| 高斯核 |
Gaussian kernel |
|
| 核机器 |
kernel machine |
|
| 核方法 |
kernel method |
|
| 支持向量 |
support vector |
|
| 支持向量机 |
support vector machine |
SVM |
| 音素 |
phoneme |
|
| 声学 |
acoustic |
|
| 语音 |
phonetic |
|
| 专家混合体 |
mixture of experts |
|
| 高斯混合体 |
Gaussian mixtures |
|
| 选通器 |
gater |
|
| 专家网络 |
expert network |
|
| 注意力机制 |
attention mechanism |
|
| 对抗样本 |
adversarial example |
|
| 对抗 |
adversarial |
|
| 对抗训练 |
adversarial training |
|
| 切面距离 |
tangent distance |
|
| 正切传播 |
tangent prop |
|
| 正切传播 |
tangent propagation |
|
| 双反向传播 |
double backprop |
|
| 期望最大化 |
expectation maximization |
EM |
| 均值场 |
mean-field |
|
| 变分推断 |
variational inference |
|
| 二值稀疏编码 |
binary sparse coding |
|
| 前馈网络 |
feedforward network |
|
| 转移 |
transition |
|
| 重构 |
reconstruction |
|
| 生成随机网络 |
generative stochastic network |
|
| 得分匹配 |
score matching |
|
| 因子 |
factorial |
|
| 分解的 |
factorized |
|
| 均匀场 |
meanfield |
|
| 最大似然估计 |
maximum likelihood estimation |
|
| 概率PCA |
probabilistic PCA |
|
| 随机梯度上升 |
Stochastic Gradient Ascent |
|
| 团 |
clique |
|
| Dirac分布 |
dirac distribution |
|
| 不动点方程 |
fixed point equation |
|
| 变分法 |
calculus of variations |
|
| 信念网络 |
belief network |
|
| 马尔可夫随机场 |
Markov random field |
|
| 马尔可夫网络 |
Markov network |
|
| 对数线性模型 |
log-linear model |
|
| 自由能 |
free energy |
|
| 局部条件概率分布 |
local conditional probability distribution |
|
| 条件概率分布 |
conditional probability distribution |
|
| 玻尔兹曼分布 |
Boltzmann distribution |
|
| 吉布斯分布 |
Gibbs distribution |
|
| 能量函数 |
energy function |
|
| 标准差 |
standard deviation |
|
| 相关系数 |
correlation |
|
| 标准正态分布 |
standard normal distribution |
|
| 协方差矩阵 |
covariance matrix |
|
| Bernoulli分布 |
Bernoulli distribution |
|
| Bernoulli输出分布 |
Bernoulli output distribution |
|
| Multinoulli分布 |
multinoulli distribution |
|
| Multinoulli输出分布 |
multinoulli output distribution |
|
| 范畴分布 |
categorical distribution |
|
| 多项式分布 |
multinomial distribution |
|
| 正态分布 |
normal distribution |
|
| 高斯分布 |
Gaussian distribution |
|
| 精度 |
precision |
|
| 多维正态分布 |
multivariate normal distribution |
|
| 精度矩阵 |
precision matrix |
|
| 各向同性 |
isotropic |
|
| 指数分布 |
exponential distribution |
|
| 指示函数 |
indicator function |
|
| 广义函数 |
generalized function |
|
| 经验分布 |
empirical distribution |
|
| 经验频率 |
empirical frequency |
|
| 混合分布 |
mixture distribution |
|
| 潜变量 |
latent variable |
|
| 隐藏变量 |
hidden variable |
|
| 先验概率 |
prior probability |
|
| 后验概率 |
posterior probability |
|
| 万能近似器 |
universal approximator |
|
| 饱和 |
saturate |
|
| 分对数 |
logit |
|
| 正部函数 |
positive part function |
|
| 负部函数 |
negative part function |
|
| 贝叶斯规则 |
Bayes' rule |
|
| 测度论 |
measure theory |
|
| 零测度 |
measure zero |
|
| Jacobian矩阵 |
Jacobian matrix |
|
| 自信息 |
self-information |
|
| 奈特 |
nats |
|
| 比特 |
bit |
|
| 香农 |
shannons |
|
| 香农熵 |
Shannon entropy |
|
| 微分熵 |
differential entropy |
|
| 微分方程 |
differential equation |
|
| KL散度 |
Kullback-Leibler (KL) divergence |
|
| 交叉熵 |
cross-entropy |
|
| 熵 |
entropy |
|
| 分解 |
factorization |
|
| 结构化概率模型 |
structured probabilistic model |
|
| 图模型 |
graphical model |
|
| 回退 |
back-off |
|
| 有向 |
directed |
|
| 无向 |
undirected |
|
| 无向图模型 |
undirected graphical model |
|
| 成比例 |
proportional |
|
| 描述 |
description |
|
| 决策树 |
decision tree |
|
| 因子图 |
factor graph |
|
| 结构学习 |
structure learning |
|
| 环状信念传播 |
loopy belief propagation |
|
| 卷积网络 |
convolutional network |
|
| 卷积网络 |
convolutional net |
|
| 主对角线 |
main diagonal |
|
| 转置 |
transpose |
|
| 广播 |
broadcasting |
|
| 矩阵乘积 |
matrix product |
|
| AdaGrad |
AdaGrad |
|
| 逐元素乘积 |
element-wise product |
|
| Hadamard乘积 |
Hadamard product |
|
| 团势能 |
clique potential |
|
| 因子 |
factor |
|
| 未归一化概率函数 |
unnormalized probability function |
|
| 循环网络 |
recurrent network |
|
| 梯度消失与爆炸问题 |
vanishing and exploding gradient problem |
|
| 梯度消失 |
vanishing gradient |
|
| 梯度爆炸 |
exploding gradient |
|
| 计算图 |
computational graph |
|
| 展开 |
unfolding |
|
| 求逆 |
invert |
|
| 时间步 |
time step |
|
| 维数灾难 |
curse of dimensionality |
|
| 平滑先验 |
smoothness prior |
|
| 局部不变性先验 |
local constancy prior |
|
| 局部核 |
local kernel |
|
| 流形 |
manifold |
|
| 流形正切分类器 |
manifold tangent classifier |
|
| 流形学习 |
manifold learning |
|
| 流形假设 |
manifold hypothesis |
|
| 环 |
loop |
|
| 弦 |
chord |
|
| 弦图 |
chordal graph |
|
| 三角形化图 |
triangulated graph |
|
| 三角形化 |
triangulate |
|
| 风险 |
risk |
|
| 经验风险 |
empirical risk |
|
| 经验风险最小化 |
empirical risk minimization |
|
| 代理损失函数 |
surrogate loss function |
|
| 批量 |
batch |
|
| 确定性 |
deterministic |
|
| 随机 |
stochastic |
|
| 在线 |
online |
|
| 流 |
stream |
|
| 梯度截断 |
gradient clipping |
|
| 幂方法 |
power method |
|
| 前向传播 |
forward propagation |
|
| 反向传播 |
backward propagation |
|
| 展开图 |
unfolded graph |
|
| 深度前馈网络 |
deep feedforward network |
|
| 前馈神经网络 |
feedforward neural network |
|
| 前向 |
feedforward |
|
| 反馈 |
feedback |
|
| 网络 |
network |
|
| 深度 |
depth |
|
| 输出层 |
output layer |
|
| 隐藏层 |
hidden layer |
|
| 宽度 |
width |
|
| 单元 |
unit |
|
| 激活函数 |
activation function |
|
| 反向传播 |
back propagation |
backprop |
| 泛函 |
functional |
|
| 平均绝对误差 |
mean absolute error |
|
| 赢者通吃 |
winner-take-all |
|
| 异方差 |
heteroscedastic |
|
| 混合密度网络 |
mixture density network |
|
| 梯度截断 |
clip gradient |
|
| 绝对值整流 |
absolute value rectification |
|
| 渗漏整流线性单元 |
Leaky ReLU |
|
| 参数化整流线性单元 |
parametric ReLU |
PReLU |
| maxout单元 |
maxout unit |
|
| 硬双曲正切函数 |
hard tanh |
|
| 架构 |
architecture |
|
| 操作 |
operation |
|
| 符号 |
symbol |
|
| 数值 |
numeric value |
|
| 动态规划 |
dynamic programming |
|
| 自动微分 |
automatic differentiation |
|
| 并行分布式处理 |
Parallel Distributed Processing |
|
| 稀疏激活 |
sparse activation |
|
| 衰减 |
damping |
|
| 学成 |
learned |
|
| 信息传输 |
message passing |
|
| 泛函导数 |
functional derivative |
|
| 变分导数 |
variational derivative |
|
| 额外误差 |
excess error |
|
| 动量 |
momentum |
|
| 混沌 |
chaos |
|
| 稀疏初始化 |
sparse initialization |
|
| 共轭方向 |
conjugate directions |
|
| 共轭 |
conjugate |
|
| 条件独立 |
conditionally independent |
|
| 集成学习 |
ensemble learning |
|
| 独立子空间分析 |
independent subspace analysis |
|
| 慢特征分析 |
slow feature analysis |
SFA |
| 慢性原则 |
slowness principle |
|
| 整流线性 |
rectified linear |
|
| 整流网络 |
rectifier network |
|
| 坐标下降 |
coordinate descent |
|
| 坐标上升 |
coordinate ascent |
|
| 预训练 |
pretraining |
|
| 无监督预训练 |
unsupervised pretraining |
|
| 逐层的 |
layer-wise |
|
| 贪心算法 |
greedy algorithm |
|
| 贪心 |
greedy |
|
| 精调 |
fine-tuning |
|
| 课程学习 |
curriculum learning |
|
| 召回率 |
recall |
|
| 覆盖 |
coverage |
|
| 超参数优化 |
hyperparameter optimization |
|
| 超参数 |
hyperparameter |
|
| 网格搜索 |
grid search |
|
| 有限差分 |
finite difference |
|
| 中心差分 |
centered difference |
|
| 储层计算 |
reservoir computing |
|
| 谱半径 |
spectral radius |
|
| 收缩 |
contractive |
|
| 长期依赖 |
long-term dependency |
|
| 跳跃连接 |
skip connection |
|
| 门控RNN |
gated RNN |
|
| 门控 |
gated |
|
| 卷积 |
convolution |
|
| 输入 |
input |
|
| 输入分布 |
input distribution |
|
| 输出 |
output |
|
| 特征映射 |
feature map |
|
| 翻转 |
flip |
|
| 稀疏交互 |
sparse interactions |
|
| 等变表示 |
equivariant representations |
|
| 稀疏连接 |
sparse connectivity |
|
| 稀疏权重 |
sparse weights |
|
| 接受域 |
receptive field |
|
| 绑定的权重 |
tied weights |
|
| 等变 |
equivariance |
|
| 探测级 |
detector stage |
|
| 符号表示 |
symbolic representation |
|
| 池化函数 |
pooling function |
|
| 最大池化 |
max pooling |
|
| 池 |
pool |
|
| 不变 |
invariant |
|
| 步幅 |
stride |
|
| 降采样 |
downsampling |
|
| 全 |
full |
|
| 非共享卷积 |
unshared convolution |
|
| 平铺卷积 |
tiled convolution |
|
| 循环卷积网络 |
recurrent convolutional network |
|
| 傅立叶变换 |
Fourier transform |
|
| 可分离的 |
separable |
|
| 初级视觉皮层 |
primary visual cortex |
|
| 简单细胞 |
simple cell |
|
| 复杂细胞 |
complex cell |
|
| 象限对 |
quadrature pair |
|
| 门控循环单元 |
gated recurrent unit |
GRU |
| 门控循环网络 |
gated recurrent net |
|
| 遗忘门 |
forget gate |
|
| 截断梯度 |
clipping the gradient |
|
| 记忆网络 |
memory network |
|
| 神经网络图灵机 |
neural Turing machine |
NTM |
| 精调 |
fine-tune |
|
| 共因 |
common cause |
|
| 编码 |
code |
|
| 再循环 |
recirculation |
|
| 欠完备 |
undercomplete |
|
| 完全图 |
complete graph |
|
| 欠定的 |
underdetermined |
|
| 过完备 |
overcomplete |
|
| 去噪 |
denoising |
|
| 去噪 |
denoise |
|
| 重构误差 |
reconstruction error |
|
| 梯度场 |
gradient field |
|
| 得分 |
score |
|
| 切平面 |
tangent plane |
|
| 最近邻图 |
nearest neighbor graph |
|
| 嵌入 |
embedding |
|
| 近似推断 |
approximate inference |
|
| 信息检索 |
information retrieval |
|
| 语义哈希 |
semantic hashing |
|
| 降维 |
dimensionality reduction |
|
| 对比散度 |
contrastive divergence |
|
| 语言模型 |
language model |
|
| 标记 |
token |
|
| 一元语法 |
unigram |
|
| 二元语法 |
bigram |
|
| 三元语法 |
trigram |
|
| 平滑 |
smoothing |
|
| 级联 |
cascade |
|
| 模型 |
model |
|
| 层 |
layer |
|
| 半监督学习 |
semi-supervised learning |
|
| 监督模型 |
supervised model |
|
| 词嵌入 |
word embedding |
|
| one-hot |
one-hot |
|
| 监督预训练 |
supervised pretraining |
|
| 迁移学习 |
transfer learning |
|
| 学习器 |
learner |
|
| 多任务学习 |
multitask learning |
|
| 领域自适应 |
domain adaption |
|
| 一次学习 |
one-shot learning |
|
| 零次学习 |
zero-shot learning |
|
| 零数据学习 |
zero-data learning |
|
| 多模态学习 |
multimodal learning |
|
| 生成式对抗网络 |
generative adversarial network |
GAN |
| 前馈分类器 |
feedforward classifier |
|
| 线性分类器 |
linear classifier |
|
| 正相 |
positive phase |
|
| 负相 |
negative phase |
|
| 随机最大似然 |
stochastic maximum likelihood |
|
| 噪声对比估计 |
noise-contrastive estimation |
NCE |
| 噪声分布 |
noise distribution |
|
| 噪声 |
noise |
|
| 独立同分布 |
independent identically distributed |
|
| 专用集成电路 |
application-specific integrated circuit |
ASIC |
| 现场可编程门阵列 |
field programmable gated array |
FPGA |
| 标量 |
scalar |
|
| 向量 |
vector |
|
| 矩阵 |
matrix |
|
| 张量 |
tensor |
|
| 点积 |
dot product |
|
| 内积 |
inner product |
|
| 方阵 |
square |
|
| 奇异的 |
singular |
|
| 范数 |
norm |
|
| 三角不等式 |
triangle inequality |
|
| 欧几里得范数 |
Euclidean norm |
|
| 最大范数 |
max norm |
|
| 对角矩阵 |
diagonal matrix |
|
| 对称 |
symmetric |
|
| 单位向量 |
unit vector |
|
| 单位范数 |
unit norm |
|
| 正交 |
orthogonal |
|
| 正交矩阵 |
orthogonal matrix |
|
| 标准正交 |
orthonormal |
|
| 特征分解 |
eigendecomposition |
|
| 特征向量 |
eigenvector |
|
| 特征值 |
eigenvalue |
|
| 分解 |
decompose |
|
| 正定 |
positive definite |
|
| 负定 |
negative definite |
|
| 半负定 |
negative semidefinite |
|
| 半正定 |
positive semidefinite |
|
| 奇异值分解 |
singular value decomposition |
SVD |
| 奇异值 |
singular value |
|
| 奇异向量 |
singular vector |
|
| 单位矩阵 |
identity matrix |
|
| 矩阵逆 |
matrix inversion |
|
| 原点 |
origin |
|
| 线性组合 |
linear combination |
|
| 列空间 |
column space |
|
| 值域 |
range |
|
| 线性相关 |
linear dependency |
|
| 线性无关 |
linearly independent |
|
| 列 |
column |
|
| 行 |
row |
|
| 同分布的 |
identically distributed |
|
| 词嵌入 |
word embedding |
|
| 机器翻译 |
machine translation |
|
| 推荐系统 |
recommender system |
|
| 词袋 |
bag of words |
|
| 协同过滤 |
collaborative filtering |
|
| 探索 |
exploration |
|
| 策略 |
policy |
|
| 关系 |
relation |
|
| 属性 |
attribute |
|
| 词义消歧 |
word-sense disambiguation |
|
| 误差度量 |
error metric |
|
| 性能度量 |
performance metrics |
|
| 共轭梯度 |
conjugate gradient |
|
| 在线学习 |
online learning |
|
| 逐层预训练 |
layer-wise pretraining |
|
| 自回归网络 |
auto-regressive network |
|
| 生成器网络 |
generator network |
|
| 判别器网络 |
discriminator network |
|
| 矩 |
moment |
|
| 可见层 |
visible layer |
|
| 无限 |
infinite |
|
| 容差 |
tolerance |
|
| 学习率 |
learning rate |
|
| 轮数 |
epochs |
|
| 轮 |
epoch |
|
| 对数尺度 |
logarithmic scale |
|
| 随机搜索 |
random search |
|
| 分段 |
piecewise |
|
| 汉明距离 |
Hamming distance |
|
| 可见变量 |
visible variable |
|
| 近似推断 |
approximate inference |
|
| 精确推断 |
exact inference |
|
| 潜层 |
latent layer |
|
| 知识图谱 |
knowledge graph |
|