目录
- Probabilistic Graphical Models
Probabilistic Graphical Models
Statistical and Algorithmic Foundations of Deep Learning
Author: Eric Xing
01 An overview of DL components
Historical remarks: early days of neural networks
我们知道生物神经元是这样的:
上游细胞通过轴突(Axon)将神经递质传送给下游细胞的树突。 人工智能受到该原理的启发,是按照下图来构造人工神经元(或者是感知器)的。
类似的,生物神经网络 —— > 人工神经网络
![在这里插入图片描述](https://www.icode9.com/i/ll/?i=2020051209264072.png?,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L05HVWV2ZXIxNQ==,size_16,color_FFFFFF,t_70Reverse-mode automatic differentiation (aka backpropagation)
Reverse-mode automatic differentiation (aka backpropagation)
下面我们来看看具体的感知器学习算法。
假设这是一个回归问题x->y,\(y = f(x)+\eta\)$, 则目标函数为
为了求出该函数的解,我们需要对其求导,具体的:
其中
由此\(w\)的更新公式为:
下面我们来说说神经网络模型:
其中,隐藏单元没有目标。
人工神经网络不过是可以由计算图表示的复杂功能组成。
通过应用链式规则并使用反向累积,我们得到:
该算法通常称为反向传播。 如果某些功能是随机的怎么办?使用随机反向传播!现代软件包可以自动执行此操作(稍后再介绍)
Modern building blocks: units, layers, activations functions, loss functions, etc.
常用激活函数:
- Linear and ReLU
- Sigmoid and tanh
- Etc.
网络层:
- Fully connected
- Convolutional & pooling
- Recurrent
- ResNets
- Etc.
-
也就是说基本构成要素的可以任意组合,如果有多种损失功能的话,可以实现多目标预测和转移学习等。 只要有足够的数据,更深的架构就会不断改进。
Feature learning
成功学习中间表示[Lee et al ICML 2009,Lee et al NIPS 2009]
表示学习:网络学习越来越多的抽象数据表示形式,这些数据被“解开”,即可以进行线性分离。
02 Similarities and differences between GMs and NNs
Graphical models vs. computational graphs
Graphical models:
- 用于以图形形式编码有意义的知识和相关的不确定性的表示形式
- 学习和推理基于经过充分研究(依赖于结构)的技术(例如EM,消息传递,VI,MCMC等)的丰富工具箱
- 图形代表模型
Utility of the graph - 一种用于从局部结构综合全局损失函数的工具(潜在功能,特征功能等)
- 一种设计合理有效的推理算法的工具(总和,均值场等)
- 激发近似和惩罚的工具(结构化MF,树近似等)
- 用于监视理论和经验行为以及推理准确性的工具
Utility of the loss function
- 学习算法和模型质量的主要衡量指标
Deep neural networks :
- 学习有助于最终指标上的计算和性能的表示形式(中间表示形式不保证一定有意义)
- 学习主要基于梯度下降法(aka反向传播);推论通常是微不足道的,并通过“向前传递”完成
- 图形代表计算
Utility of the network
- 概念上综合复杂决策假设的工具(分阶段的投影和聚合)
- 用于组织计算操作的工具(潜在状态的分阶段更新)
- 用于设计加工步骤和计算模块的工具(逐层并行化)
- 在评估DL推理算法方面没有明显的用途
到目前为止,图形模型是概率分布的表示,而神经网络是函数近似器(无概率含义)。有些神经网络实际上是图形模型(即单位/神经元代表随机变量):
- 玻尔兹曼机器Boltzmann machines (Hinton&Sejnowsky,1983)
- 受限制的玻尔兹曼机器Restricted Boltzmann machines(Smolensky,1986)
- Sigmoid信念网络的学习和推理Learning and Inference in sigmoid belief networks(Neal,1992)
- 深度信念网络中的快速学习Fast learning in deep belief networks(Hinton,Osindero,Teh,2006年)
- 深度玻尔兹曼机器Deep Boltzmann machines(Salakhutdinov和Hinton,2009年)
接下来我们会逐一介绍他们。
I: Restricted Boltzmann Machines
受限玻尔兹曼机器,缩写为RBM。 RBM是用二部图(bi-partite graph)表示的马尔可夫随机场,图的一层/部分中的所有节点都连接到另一层中的所有节点; 没有层间连接。
联合分布为:
单个数据点的对数似然度(不可观察的边际被边缘化):
对数似然比的梯度 模型参数:
对数似然比的梯度 参数(替代形式):
两种期望都可以通过抽样来近似, 从后部采样是准确的(RBM在给定的h上分解)。 通过MCMC从关节进行采样(例如,吉布斯采样)
在神经网络文献中:
- 计算第一项称为钳位/唤醒/正相(网络是“清醒的”,因为它取决于可见变量)
- 计算第二项称为非固定/睡眠/*/负相(该网络“处于睡眠状态”,因为它对关节的可见变量进行了采样;比喻,它梦见了可见的输入)
通过随机梯度下降(SGD)优化给定数据的模型对数似然来完成学习, 第二项(负相)的估计严重依赖于马尔可夫链的混合特性,这经常导致收敛缓慢并且需要额外的计算。
II: Sigmoid Belief Networks
Sigimoid信念网是简单的贝叶斯网络,其二进制变量的条件概率由Sigmoid函数表示:
贝叶斯网络表现出一种称为“解释效应”的现象:如果A与C相关,则B与C相关的机会减少。 ⇒在给定C的情况下A和B相互关联。
值得注意的是, 由于“解释效应”,当我们以信念网络中的可见层为条件时,所有隐藏变量都将成为因变量。
Sigmoid Belief Networks as graphical models
尼尔提出了用于学习和推理的蒙特卡洛方法(尼尔,1992年):
RBMs are infinite belief networks
要对模型参数进行梯度更新,我们需要通过采样计算期望值。
- 我们可以在第一阶段从后验中精确采样
- 我们运行吉布斯块抽样,以从联合分布中近似抽取样本
条件分布\(p(v| h)\)和\(p(h|v)\)用sigmoid表示, 因此,我们可以将以RBM表示的联合分布中的Gibbs采样视为无限深的Sigmoid信念网络中的自顶向下传播!
RBM等效于无限深的信念网络。当我们训练RBM时,实际上就是在训练一个无限深的简短网, 只是所有图层的权重都捆绑在一起。如果权重在某种程度上“统一”,我们将获得一个深度信仰网络。
Deep Belief Networks and Boltzmann Machines
III: Deep Belief Nets
DBN是混合图形模型(链图)。其联合概率分布可表示为:
其中蕴含的挑战:
由于explaining away effect,因此在DBN中进行精确推断是有问题的
训练分两个阶段进行:
- 贪婪的预训练+临时微调; 没有适当的联合训练
- 近似推断为前馈(自下而上)
Layer-wise pre-training
- 预训练并冻结第一个RBM
- 在顶部堆叠另一个RBM并对其进行训练
- 重物2层以上的重物保持绑紧状态
- 我们重复此过程:预训练和解开
Fine-tuning
- Pre-training is quite ad-hoc(特别指定) and is unlikely to lead to a good probabilistic model per se
- However, the layers of representations could perhaps be useful for some other downstream tasks!
- We can further “fine-tune” a pre-trained DBN for some other task
Setting A: Unsupervised learning (DBN → autoencoder)
- Pre-train a stack of RBMs in a greedy layer-wise fashion
- “Unroll” the RBMs to create an autoencoder
- Fine-tune the parameters by optimizing the reconstruction error(重构误差)
Setting B: Supervised learning (DBN → classifier)
- Pre-train a stack of RBMs in a greedy layer-wise fashion
- “Unroll” the RBMs to create a feedforward classifier
- Fine-tune the parameters by optimizing the reconstruction error
Deep Belief Nets and Boltzmann Machines
DBMs are fully un-directed models (Markov random fields). Can be trained similarly as RBMs via MCMC (Hinton & Sejnowski, 1983). Use a variational approximation(变分近似) of the data distribution for faster training (Salakhutdinov & Hinton, 2009). Similarly, can be used to initialize other networks for downstream tasks
A few critical points to note about all these models:
- The primary goal of deep generative models is to represent the distribution of the observable variables. Adding layers of hidden variables allows to represent increasingly more complex distributions.
- Hidden variables are secondary (auxiliary) elements used to facilitate learning of complex dependencies between the observables.
- Training of the model is ad-hoc, but what matters is the quality of learned hidden representations.
- Representations are judged by their usefulness on a downstream task (the probabilistic meaning of the model is often discarded at the end).
- In contrast, classical graphical models are often concerned with the correctness of learning and inference of all variables
Conclusion
- DL & GM: the fields are similar in the beginning (structure, energy, etc.), and then diverge to their own signature pipelines
- DL: most effort is directed to comparing different architectures and their components (models are driven by evaluating empirical performance on a downstream tasks)
- DL models are good at learning robust hierarchical representations from the data and suitable for simple reasoning (call it “low-level cognition”)
- GM: the effort is directed towards improving inference accuracy and convergence speed
- GMs are best for provably correct inference and suitable for high-level complex reasoning tasks (call it “high-level cognition”) 推理任务
- Convergence of both fields is very promising!
03 Combining DL methods and GMs
Using outputs of NNs as inputs to GMs
Combining sequential NNs and GMs
HMM:隐马尔可夫
Hybrid NNs + conditional GMs
In a standard CRF条件随机场, each of the factor cells is a parameter.
In a hybrid model, these values are computed by a neural network.
GMs with potential functions represented by NNs q NNs with structured outputs
Using GMs as Prediction Explanations
!!!! How do we build a powerful predictive model whose predictions we can interpret in terms of semantically meaningful features?
Contextual Explanation Networks (CENs)
- The final prediction is made by a linear GM.
- Each coefficient assigns a weight to a meaningful attribute.
- Allows us to judge predictions in terms of GMs produced by the context encoder.
CEN: Implementation Details
Workflow:
- Maintain a (sparse稀疏) dictionary of GM parameters.
- Process complex inputs (images, text, time series, etc.) using deep nets; use soft attention to either select or combine models from the dictionary.
• Use constructed GMs (e.g., CRFs) to make predictions.
• Inspect GM parameters to understand the reasoning behind predictions.
Results: imagery as context
Based on the imagery, CEN learns to select different models for urban and rural
Results: classical image & text datasets
CEN architectures for survival analysis
04 Bayesian Learning of NNs
Bayesian learning of NN parameters q Deep kernel learning
A neural network as a probabilistic model: Likelihood: \(p(y|x, \theta)\)
- Categorical distribution for classification ⇒ cross-entropy loss 交叉熵损失
- Gaussian distribution for regression ⇒ squared loss平方损失
- Gaussianprior⇒L2regularization
- Laplaceprior⇒L1regularization
Bayesian learning [MacKay 1992, Neal 1996, de Freitas 2003]