He Wang PRO
Knowledge increases by sharing but not by saving.
Building a deep neural network
&
How could we use it as a density estimator
Based on :
[1] My tech post, "A neural network wasn't built in a day" (一段关于神经网络的故事) (2017)
[2] 1903.01998, 1909.06296, 2002.07656, 2008.03312; PRL(2020) 124 041102
Journal Club - Oct 20, 2020
Content
- What is a Neural Network (NN) anyway?
- One neural
- One layer of neural
- Basic types of neural network in academic papers
- A concise summary of current GW ML parameter estimation studies
- MAP, CVAE, Flow
- GSN (optional)
- What is a Neural Network (NN) anyway?
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
- Eg:
Yes or No
A number
A sequence
{x1,x2,…,xn}
\{x_1, x_2, \dots, x_n\}
{0 or 1}
\{0 \text{ or } 1\}
- What is a Neural Network (NN) anyway?
What's happend in a neural?（一个神经元的本事）
∑iwixi+b=w0x0+w1x1+⋯+wD−1xD−1+b
\sum_{i} w_{i} x_{i}+b=w_{0} x_{0}+w_{1} x_{1}+\cdots+w_{D-1} x_{D-1}+b
1×1[i∑wixi+b]=1×D[⋯xi…]⋅D×1⋮wi⋮+1×1[b]
\underbrace{\left[\sum_{i} w_{i} x_{i}+b\right]}_{1 \times 1}=\underbrace{\left[\cdots \quad x_{i} \quad \ldots\right]}_{1 \times D} \cdot \underbrace{\left[\begin{array}{c}
\vdots \\
w_{i} \\
\vdots
\end{array}\right]}_{D \times 1}+\underbrace{[b]}_{1 \times 1}
- Initialize a weight vector and a bias scalar randomly in one neural.
- Input a sample, then it gives us an output.
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
"score"
"your performance"
"one judge"
- What is a Neural Network (NN) anyway?
What's happend in a neural?（一个神经元的本事）
- Initialize a weight vector and a bias vector randomly in one neural.
- Input some samples, then it gives us some outputs.
"a bunch guys' show"
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
"scores"
"one judge"
N×1[∑iwixi+b⋮]=N×D[⋯⋮xi⋯]⋅D×1⋮wi⋮+N×1b⋮⋮
\underbrace{\left[\begin{array}{c}
\sum_{i} w_{i} x_{i}+b \\
\vdots
\end{array}\right]}_{N \times 1}=\underbrace{\left[\begin{array}{ccc}
\cdots & x_{i} & \cdots \\
\vdots & &
\end{array}\right]}_{N \times D} \cdot \underbrace{\left[\begin{array}{c}
\vdots \\
w_{i} \\
\vdots
\end{array}\right]}_{D \times 1}+\underbrace{\left[\begin{array}{c}
b \\
\vdots \\
\vdots
\end{array}\right]}_{N \times 1}
- What is a Neural Network (NN) anyway?
What's happend in a neural?（一个神经元的本事）
- Initialize a weight vector and a bias vector randomly in one neural.
- Input some samples, then it gives us some outputs.
f(x)=max(0,x)
f(x)=\max (0, x)
ReLU
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
"a bunch guys' show"
"one judge"
N×1[∑iwixi+b⋮]=N×D[⋯⋮xi⋯]⋅D×1⋮wi⋮+N×1b⋮⋮
\underbrace{\left[\begin{array}{c}
\sum_{i} w_{i} x_{i}+b \\
\vdots
\end{array}\right]}_{N \times 1}=\underbrace{\left[\begin{array}{ccc}
\cdots & x_{i} & \cdots \\
\vdots & &
\end{array}\right]}_{N \times D} \cdot \underbrace{\left[\begin{array}{c}
\vdots \\
w_{i} \\
\vdots
\end{array}\right]}_{D \times 1}+\underbrace{\left[\begin{array}{c}
b \\
\vdots \\
\vdots
\end{array}\right]}_{N \times 1}
"scores"
- What is a Neural Network (NN) anyway?
Generalize to one layer of neural（层状的神经元）
- Initialize a weight matrix and a bias scalar randomly in one layer.
- Input one sample, then it gives us an output.
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
1×10[i∑wixi+b⋯]=1×D[⋯xi⋯]⋅D×10⋮wi⋮⋯⋯+Broadcasting [b⋯]
\underbrace{\left[\sum_{i} w_{i} x_{i}+b \quad \cdots\right]}_{1 \times 10}=\underbrace{\left[\begin{array}{lll}
\cdots & x_{i} & \cdots
\end{array}\right]}_{1 \times D} \cdot \underbrace{\left[\begin{array}{lll}
\vdots \\
w_{i} & \cdots & \cdots \\
\vdots
\end{array}\right]}_{D \times 10}+\underbrace{\left[\begin{array}{ll}
b & \cdots
\end{array}\right]}_{\text {Broadcasting }}
"score"
"10 judges"
"your performance"
- What is a Neural Network (NN) anyway?
Generalize to one layer of neural（层状的神经元）
- Initialize a weight matrix and a bias scalar randomly in one layer.
- Input one sample, then it gives us an output.
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
"10 judges"
"scores"
"a bunch guys' show"
N×D[∑iwixi+b⋮]=N×D[⋯⋮xi⋯]⋅D×10⋮wi⋮⋯⋯+N×10 (Broadcasting)b⋮⋮⋯
\underbrace{\left[\begin{array}{c}
\sum_{i} w_{i} x_{i}+b \\
\vdots
\end{array}\right]}_{N \times D}=\underbrace{\left[\begin{array}{ccc}
\cdots & x_{i} & \cdots \\
\vdots & &
\end{array}\right]}_{N \times D} \cdot \underbrace{\left[\begin{array}{lll}
\vdots \\
w_{i} & \cdots & \cdots \\
\vdots
\end{array}\right]}_{D \times 10}+
\underbrace{\left[\begin{array}{ll}
b & \cdots \\
\vdots \\
\vdots
\end{array}\right]}_{N \times 10 \\ \text { (Broadcasting)}\\ }
- What is a Neural Network (NN) anyway?
Fully-connected neural layers（全连接的神经元层）
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
In each layer:
input
output
num of neurals in this layer
Draw how one sample data flows
- For a fully-connected neural network, only the number of weight columns in each layer is the hyper-parameter we need to fix first.
- Only one word for the evaluation... (no more slices)
- Loss/cost/error func.
- Gradient descent / backward propagation algorithm
And how the shape of data changs.
- What is a Neural Network (NN) anyway?
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
Convolution is a specialized kind of linear operation.（卷积层是全连接层的一种特例）
s=[s0s1⋯s7]=[x0x1⋯x4]⋅w00000w1w0000w2w1w000w3w2w1w000w3w2w1w000w3w2w1000w3w20000w3=x⋅w
\mathbf{s}=\left[\begin{array}{llll}
s_{0} & s_{1} & \cdots & s_{7}
\end{array}\right]=\left[\begin{array}{llll}
x_{0} & x_{1} & \cdots & x_{4}
\end{array}\right] \cdot\left[\begin{array}{cccccccc}
w_{0} & w_{1} & w_{2} & w_{3} & 0 & 0 & 0 & 0 \\
0 & w_{0} & w_{1} & w_{2} & w_{3} & 0 & 0 & 0 \\
0 & 0 & w_{0} & w_{1} & w_{2} & w_{3} & 0 & 0 \\
0 & 0 & 0 & w_{0} & w_{1} & w_{2} & w_{3} & 0 \\
0 & 0 & 0 & 0 & w_{0} & w_{1} & w_{2} & w_{3}
\end{array}\right]=\mathbf{x} \cdot \mathbf{w}
- Flip-and-slide Form
- Matrix Form :
D=5,M=4
D=5, M=4
s(t)=∫x(τ)w(t−τ)dτ=:(x∗w)(t)
s(t)=\int x(\tau) w(t-\tau) d \tau=:(x * w)(t)
(a1x1+a1x2)∗w(x∗w)(t−T)=a1(x1∗w)+a2(x2∗w)=x(t−T)∗w(t)(linearity)(time invariance)
\begin{aligned}
\left(a_{1} x_{1}+a_{1} x_{2}\right) * w &=a_{1}\left(x_{1} * w\right)+a_{2}\left(x_{2} * w\right) \,&(\text{linearity})\\
(x * w)(t-T) &=x(t-T) * w(t) \,&(\text{time invariance})
\end{aligned}
s[n]=∑m=max(0,n−D)min(n,M)x[m]⋅w[n−m],n=0,1,…,D+M
s[n]=\sum_{m=\max (0, n-D)}^{\min (n, M)} x[m] \cdot w[n-m], n=0,1, \ldots, D+M
x[n],n=0,1,…,D−1;w[n],n=0,1,…,M−1
\begin{aligned}
&x[n], n=0,1, \ldots, D-1;\\
&w[n], n=0,1, \ldots, M-1
\end{aligned}
- Integral Form
- Discrete Form
- 参数共享 parameter sharing
- 稀疏交互 sparse interactions
- What is a Neural Network (NN) anyway?
Objective:
- Input a sample to a function (our NN).
- Then evaluate how is it "close" to the truth (also called label)
Neural networks in academic papers.（GW文献中的神经网络）
PRD. 100, 063015 (2019)
Mach. Learn.: Sci. Technol. 1 025014 (2020)
2003.09995
Expert Systems With Applications 151 (2020) 113378
"All of the current GW ML parameter estimation studies are still at the proof-of-principle stage" [2005.03745]
- A concise summary of current GW ML parameter estimation studies
Real-time regression
- Huerta's Group [PRD, 2018, 97, 044039], [PLB, 2018, 778, 64-70], [1903.01998], [2004.09524]
- Fan et al. [SCPMA, 62, 969512 (2019)]
- Carrillo et al. [GRG, 2016, 48, 141], [IJMP, 2017, D27, 1850043]
- Santos et al. [2003.09995]
- *Li et al. [2003.13928] (Bayes neural networks)
Explicit posteriors density
- Chua et al. [PRL, 2020, 124, 041102]
- produce Bayesian posteriors using neural networks.
- Gabbard et al. [1909.06296] (CVAE)
- produce samples from the posterior.
- 256Hz, 1-sec, 4-D, simulated noise
- Strong agreement between Bilby and the CVAE.
- Yamamoto & Tanaka [2002.12095] (CVAE)
- QNM frequencies estimations
- Green et al. [2002.07656, 2008.03312] (MAF, CVAE+,Flow-based)
- optimal results for now
Suppose we have a posterior distribution ptrue(x∣y). (y is the GW data, x is the corresponding parameters)
- The aim is to train a neural network to give an approximation p(x∣y) to ptrue(x∣y).
- Take the expectation value (over y) of the cross-entropy (KL divergence) between the two distributions as the loss function:
L=−∫dxdyptrue(x∣y)ptrue(y)logp(x∣y)=Ey∼ptrue(y)Ex∼ptrue(x∣y)[−logp(x∣y)]
L=-\int d x d y \,p_{\mathrm{true}}(x | y) p_{\mathrm{true}}(y) \log p(x | y)
= \mathbb{E}_{y\sim p_{true}(y)}\mathbb{E}_{x\sim p_{true}(x|y)} [-\log p(x | y) ]
fixed; costly sampling required
L=−∫dxdyptrue(x∣y)ptrue(y)logp(x∣y)=Ey∼ptrue(y)Ex∼ptrue(x∣y)[−logp(x∣y)]
L=-\int d x d y \,p_{\mathrm{true}}(x | y) p_{\mathrm{true}}(y) \log p(x | y)
= \mathbb{E}_{y\sim p_{true}(y)}\mathbb{E}_{x\sim p_{true}(x|y)} [-\log p(x | y) ]
fixed; costly sampling required
L=−∫dxdyptrue(y∣x)ptrue(x)logp(x∣y)=Ex∼ptrue(x)Ey∼ptrue(y∣x)[−logp(x∣y)]
L=-\int d x d y \,p_{\mathrm{true}}(y | x) p_{\mathrm{true}}(x) \log p(x | y)
= \mathbb{E}_{x\sim p_{true}(x)}\mathbb{E}_{y\sim p_{true}(y|x)} [-\log p(x | y) ]
Bayes' theorem
≈−N1∑i=1Nlogp(x(i)∣y(i))
\approx-\frac{1}{N} \sum_{i=1}^{N} \log p\left(x^{(i)} | y^{(i)}\right)
sampling from the likelihood: x(i)∼ptrue(x),y(i)∼ptrue(y∣x(i))
\text{sampling from the likelihood: } \\x^{(i)} \sim p_{\mathrm{true}}(x) , y^{(i)} \sim p_{\mathrm{true}}\left(y | x^{(i)}\right)
p(x∣y)=(2π)n∣detΣ(y)∣ω×exp(−21ij=1∑n(xi−μi(y))Σij−1(y)(xj−μj(y)))
\begin{aligned}
p(x | y)=& \frac{\omega}{\sqrt{(2 \pi)^{n}|\operatorname{det} \Sigma(y)|}} \times \\
& \exp \left(-\frac{1}{2} \sum_{i j=1}^{n}\left(x_{i}-\mu_{i}(y)\right) \Sigma_{i j}^{-1}(y)\left(x_{j}-\mu_{j}(y)\right)\right)
\end{aligned}
Chua et al. [PRL, 2020, 124, 041102] assume a multivariate normal distribution with weights:
x(i)
x^{(i)}
y(i)
y^{(i)}
μ(i),Σ(i),ω
\mu^{(i)}, \Sigma^{(i)}, \omega
L
L
NN
Suppose we have a posterior distribution ptrue(x∣y). (y is the GW data, x is the corresponding parameters)
- The aim is to train a neural network to give an approximation p(x∣y) to ptrue(x∣y).
- Take the expectation value (over y) of the cross-entropy (KL divergence) between the two distributions as the loss function:
L=−∫dxdyptrue(x∣y)ptrue(y)logp(x∣y)=Ey∼ptrue(y)Ex∼ptrue(x∣y)[−logp(x∣y)]
L=-\int d x d y \,p_{\mathrm{true}}(x | y) p_{\mathrm{true}}(y) \log p(x | y)
= \mathbb{E}_{y\sim p_{true}(y)}\mathbb{E}_{x\sim p_{true}(x|y)} [-\log p(x | y) ]
fixed; costly sampling required
Bayes' theorem
≈−N1∑i=1Nlogp(x(i)∣y(i))
\approx-\frac{1}{N} \sum_{i=1}^{N} \log p\left(x^{(i)} | y^{(i)}\right)
sampling from the likelihood: x(i)∼ptrue(x),y(i)∼ptrue(y∣x(i))
\text{sampling from the likelihood: } \\x^{(i)} \sim p_{\mathrm{true}}(x) , y^{(i)} \sim p_{\mathrm{true}}\left(y | x^{(i)}\right)
p(x∣y)=(2π)n∣detΣ(y)∣ω×exp(−21ij=1∑n(xi−μi(y))Σij−1(y)(xj−μj(y)))
\begin{aligned}
p(x | y)=& \frac{\omega}{\sqrt{(2 \pi)^{n}|\operatorname{det} \Sigma(y)|}} \times \\
& \exp \left(-\frac{1}{2} \sum_{i j=1}^{n}\left(x_{i}-\mu_{i}(y)\right) \Sigma_{i j}^{-1}(y)\left(x_{j}-\mu_{j}(y)\right)\right)
\end{aligned}
Chua et al. [PRL, 2020, 124, 041102] assume a multivariate normal distribution with weights:
x(i)
x^{(i)}
y(i)
y^{(i)}
μ(i),Σ(i),ω
\mu^{(i)}, \Sigma^{(i)}, \omega
L
L
Gabbard et al. [1909.06296] (CVAE)
logp(x∣y)=Eq(z∣x,y)logp(x∣y)
\log p(x | y)=\mathbb{E}_{q(z | x, y)} \log p(x | y)
≡LELBO+DKL[q(z∣x,y)∣∣p(z∣x,y)]
\equiv\mathcal{L}_{ELBO} + D_{KL}[q(z|x,y)||p(z|x,y)]
L∼Ey∼ptrue(x)Ex∼ptrue(y∣x)[−LELBO]
L \sim \mathbb{E}_{y\sim p_{true}(x)}\mathbb{E}_{x\sim p_{true}(y|x)} [-\mathcal{L}_{ELBO} ]
=Ey∼ptrue(x)Ex∼ptrue(y∣x){−Ez∼q(z∣x,y)log[p(x∣z,y)]+DKL[q(z∣x,y)∣∣p(z∣y)]}
= \mathbb{E}_{y\sim p_{true}(x)}\mathbb{E}_{x\sim p_{true}(y|x)}\{-\mathbb{E}_{z\sim q(z|x,y)}\log[p(x|z,y)] +D_{KL}[q(z|x,y)||p(z|y)] \}
x(i)
x^{(i)}
y(i)
y^{(i)}
μp(i),Σp(i)
\mu_p^{(i)}, \Sigma_p^{(i)}
μq(i),Σq(i)
\mu_q^{(i)}, \Sigma_q^{(i)}
z(j)
z^{(j)}
μ(j),Σ(j)
\mu^{(j)}, \Sigma^{(j)}
NN
NN
NN
NN
Suppose we have a posterior distribution ptrue(x∣y). (y is the GW data, x is the corresponding parameters)
- The aim is to train a neural network to give an approximation p(x∣y) to ptrue(x∣y).
- Take the expectation value (over y) of the cross-entropy (KL divergence) between the two distributions as the loss function:
L=−∫dxdyptrue(y∣x)ptrue(x)logp(x∣y)=Ex∼ptrue(x)Ey∼ptrue(y∣x)[−logp(x∣y)]
L=-\int d x d y \,p_{\mathrm{true}}(y | x) p_{\mathrm{true}}(x) \log p(x | y)
= \mathbb{E}_{x\sim p_{true}(x)}\mathbb{E}_{y\sim p_{true}(y|x)} [-\log p(x | y) ]
L=−∫dxdyptrue(x∣y)ptrue(y)logp(x∣y)=Ey∼ptrue(y)Ex∼ptrue(x∣y)[−logp(x∣y)]
L=-\int d x d y \,p_{\mathrm{true}}(x | y) p_{\mathrm{true}}(y) \log p(x | y)
= \mathbb{E}_{y\sim p_{true}(y)}\mathbb{E}_{x\sim p_{true}(x|y)} [-\log p(x | y) ]
fixed; costly sampling required
Bayes' theorem
≈−N1∑i=1Nlogp(x(i)∣y(i))
\approx-\frac{1}{N} \sum_{i=1}^{N} \log p\left(x^{(i)} | y^{(i)}\right)
sampling from the likelihood: x(i)∼ptrue(x),y(i)∼ptrue(y∣x(i))
\text{sampling from the likelihood: } \\x^{(i)} \sim p_{\mathrm{true}}(x) , y^{(i)} \sim p_{\mathrm{true}}\left(y | x^{(i)}\right)
p(x∣y)=(2π)n∣detΣ(y)∣ω×exp(−21ij=1∑n(xi−μi(y))Σij−1(y)(xj−μj(y)))
\begin{aligned}
p(x | y)=& \frac{\omega}{\sqrt{(2 \pi)^{n}|\operatorname{det} \Sigma(y)|}} \times \\
& \exp \left(-\frac{1}{2} \sum_{i j=1}^{n}\left(x_{i}-\mu_{i}(y)\right) \Sigma_{i j}^{-1}(y)\left(x_{j}-\mu_{j}(y)\right)\right)
\end{aligned}
Chua et al. [PRL, 2020, 124, 041102] assume a multivariate normal distribution with weights:
x(i)
x^{(i)}
y(i)
y^{(i)}
μ(i),Σ(i),ω
\mu^{(i)}, \Sigma^{(i)}, \omega
NN
L
L
Gabbard et al. [1909.06296] (CVAE)
logp(x∣y)=Eq(z∣x,y)logp(x∣y)
\log p(x | y)=\mathbb{E}_{q(z | x, y)} \log p(x | y)
≡LELBO+DKL[q(z∣x,y)∣∣p(z∣x,y)]
\equiv\mathcal{L}_{ELBO} + D_{KL}[q(z|x,y)||p(z|x,y)]
L∼Ey∼ptrue(x)Ex∼ptrue(y∣x)[−LELBO]
L \sim \mathbb{E}_{y\sim p_{true}(x)}\mathbb{E}_{x\sim p_{true}(y|x)} [-\mathcal{L}_{ELBO} ]
=Ey∼ptrue(x)Ex∼ptrue(y∣x){−Ez∼q(z∣x,y)log[p(x∣z,y)]+DKL[q(z∣x,y)∣∣p(z∣y)]}
= \mathbb{E}_{y\sim p_{true}(x)}\mathbb{E}_{x\sim p_{true}(y|x)}\{-\mathbb{E}_{z\sim q(z|x,y)}\log[p(x|z,y)] +D_{KL}[q(z|x,y)||p(z|y)] \}
x(i)
x^{(i)}
y(i)
y^{(i)}
NN
NN
μp(i),Σp(i)
\mu_p^{(i)}, \Sigma_p^{(i)}
μq(i),Σq(i)
\mu_q^{(i)}, \Sigma_q^{(i)}
NN
z(j)
z^{(j)}
μ(j),Σ(j)
\mu^{(j)}, \Sigma^{(j)}
Green et al. [2002.07656] (MAF, CVAE+)
L=Ey∼ptrue(x)Ex∼ptrue(y∣x)[−logN(0,1)n(f−1(x))−logdet∂(x1,…,xn)∂(f1−1,…,fn−1)]
L=\mathbb{E}_{y\sim p_{true}(x)}\mathbb{E}_{x\sim p_{true}(y|x)} \left[-\log \mathcal{N}(0,1)^{n}\left(f^{-1}(x)\right)
-\log \left|\operatorname{det} \frac{\partial\left(f_{1}^{-1}, \ldots, f_{n}^{-1}\right)}{\partial\left(x_{1}, \ldots, x_{n}\right)}\right|\right]
Suppose we have a posterior distribution ptrue(x∣y). (y is the GW data, x is the corresponding parameters)
- The aim is to train a neural network to give an approximation p(x∣y) to ptrue(x∣y).
- Take the expectation value (over y) of the cross-entropy (KL divergence) between the two distributions as the loss function:
L=−∫dxdyptrue(y∣x)ptrue(x)logp(x∣y)=Ex∼ptrue(x)Ey∼ptrue(y∣x)[−logp(x∣y)]
L=-\int d x d y \,p_{\mathrm{true}}(y | x) p_{\mathrm{true}}(x) \log p(x | y)
= \mathbb{E}_{x\sim p_{true}(x)}\mathbb{E}_{y\sim p_{true}(y|x)} [-\log p(x | y) ]
CVAE (Train)
Y (·, 256)
X (·, 5)
KL
μ(X,Y),Σ(X,Y)
\vec{\mu}(X,Y), \boldsymbol{\Sigma}(X,Y)
μ(Y),Σ(Y)
\vec{\mu}(Y), \boldsymbol{\Sigma}(Y)
E2
E1
X′ (·, 5)
KL[N(μX,ΣX2)∣∣N(μY,Σ2Y)]
KL[\mathcal{N}(\vec{\mu_X}, \boldsymbol{\Sigma}^2_X) || \mathcal{N}(\vec{\mu}_Y, \boldsymbol{\Sigma^2}_Y) ]
D
(2, 8)
μX,ΣX
\mu_X, \Sigma_X
FYI: if dims of latent space is 2, i.e.
sample z from N(μ,Σ2)
Y (·, 256)
(1, 8)
Training set: N=106
batchsize = 512
(·, 8)
[(μm1,σm1),...]
X1
X2
Xn
latent space
strain
params
- The key is to notice that any distribution in d dimensions can be generated by taking a set of d variables that are normally distributed and mapping them through a sufficiently complicated function .
CVAE: conditional variational autoencoder
Y (·, 256)
X (·, 5)
KL
μ(X,Y),Σ(X,Y)
\vec{\mu}(X,Y), \boldsymbol{\Sigma}(X,Y)
μ(Y),Σ(Y)
\vec{\mu}(Y), \boldsymbol{\Sigma}(Y)
E2
E1
X′ (·, 5)
KL[N(μX,ΣX2)∣∣N(μY,Σ2Y)]
KL[\mathcal{N}(\vec{\mu_X}, \boldsymbol{\Sigma}^2_X) || \mathcal{N}(\vec{\mu}_Y, \boldsymbol{\Sigma^2}_Y) ]
D
(2, 8)
μX,ΣX
\mu_X, \Sigma_X
FYI: if dims of latent space is 2, i.e.
sample z from N(μ,Σ2)
Y (·, 256)
(1, 8)
Training set: N=106
batchsize = 512
(·, 8)
[(μm1,σm1),...]
X1
X2
Xn
latent space
strain
params
logP(X∣Y)−D[Q(z∣X,Y)∥P(z∣X,Y)]=Ez∼Q[logP(X∣z,Y)]−D[Q(z∣X,Y)∥P(z∣Y)]
\log P(X|Y)-\mathcal{D}[Q(z | X,Y) \| P(z | X,Y)]=E_{z \sim Q}[\log P(X | z,Y)]-\mathcal{D}[Q(z | X,Y) \| P(z|Y)]
=:LELBO(P,Q,Y)
=:L_{ELBO}(P,Q,Y)
P
P
Q
- The key is to notice that any distribution in d dimensions can be generated by taking a set of d variables that are normally distributed and mapping them through a sufficiently complicated function .
CVAE (Train)
Objective: maximise LELBO (与数据点 X 相关联的变分下界)
ELBO: Evidence Lower Bound
CVAE: conditional variational autoencoder
μX,ΣX
\mu_X, \Sigma_X
FYI: if dims of latent space is 2, i.e.
X1
X2
Xn
latent space
logP(X∣Y)−D[Q(z∣X,Y)∥P(z∣X,Y)]=Ez∼Q[logP(X∣z,Y)]−D[Q(z∣X,Y)∥P(z∣Y)]
\log P(X|Y)-\mathcal{D}[Q(z | X,Y) \| P(z | X,Y)]=E_{z \sim Q}[\log P(X | z,Y)]-\mathcal{D}[Q(z | X,Y) \| P(z|Y)]
=:LELBO(P,Q,Y)
=:L_{ELBO}(P,Q,Y)
- The key is to notice that any distribution in d dimensions can be generated by taking a set of d variables that are normally distributed and mapping them through a sufficiently complicated function .
CVAE (Test)
Y (·, 256)
μ(Y),Σ(Y)
\vec{\mu}(Y), \boldsymbol{\Sigma}(Y)
E1
X′ (·, 5)
D
sample z from N(μ,Σ2)
Y (·, 256)
(1, 8)
Training set: N=106
batchsize = 512
(·, 8)
[(μm1,σm1),...]
strain
P
(2, 8)
P
Objective: maximise LELBO (与数据点 X 相关联的变分下界)
ELBO: Evidence Lower Bound
CVAE: conditional variational autoencoder
Y (·, 256)
X (·, 5)
KL
μ(X,Y),Σ(X,Y)
\vec{\mu}(X,Y), \boldsymbol{\Sigma}(X,Y)
μ(Y),Σ(Y)
\vec{\mu}(Y), \boldsymbol{\Sigma}(Y)
E2
E1
X′ (·, 5)
KL[N(μX,ΣX2)∣∣N(μY,Σ2Y)]
KL[\mathcal{N}(\vec{\mu_X}, \boldsymbol{\Sigma}^2_X) || \mathcal{N}(\vec{\mu}_Y, \boldsymbol{\Sigma^2}_Y) ]
D
(2, 8)
sample z from N(μ,Σ2)
Y (·, 256)
(1, 8)
Training set: N=106
batchsize = 512
(·, 8)
[(μm1,σm1),...]
strain
params
logP(X∣Y)−D[Q(z∣X,Y)∥P(z∣X,Y)]=Ez∼Q[logP(X∣z,Y)]−D[Q(z∣X,Y)∥P(z∣Y)]
\log P(X|Y)-\mathcal{D}[Q(z | X,Y) \| P(z | X,Y)]=E_{z \sim Q}[\log P(X | z,Y)]-\mathcal{D}[Q(z | X,Y) \| P(z|Y)]
=:LELBO(P,Q,Y)
=:L_{ELBO}(P,Q,Y)
Objective: maximise LELBO (与数据点 X 相关联的变分下界)
P
P
Q
CVAE
Drawbacks:
- KL divergence
- LELBO
Mystery:
- How could it still work that well？
(for simple case only)
The final slice. 💪
ELBO: Evidence Lower Bound
CVAE: conditional variational autoencoder
Building a deep neural network & How could we use it as a density estimator Based on : [1] My tech post, " A neural network wasn't built in a day " ( 一段关于神经网络的故事 ) (2017) [2] 1903.01998, 1909.06296, 2002.07656, 2008.03312; PRL(2020) 124 041102 Journal Club - Oct 20, 2020
Building a deep neural network and How could we use it as a density estimator
By He Wang
Building a deep neural network and How could we use it as a density estimator
Abstract: Firstly, I will talk about some basic concepts of deep neural networks and I hope it would help clear up misunderstandings and rumors related to understand how a neural network works, etc. Then, based on these concepts, I will try to briefly review the current GW ML parameter estimation studies (1903.01998, 1909.06296, PRL(2020) 124 041102, 2002.07656, 2008.03312; selected), especially how they try to built up a neural network to estimate the posterior distribution. The relative drawbacks and mysteries of their works are also mentioned.
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