Bayesian Deep Learning for Integrated Intelligence ... · Bayesian Deep Learning for Integrated...
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Bayesian Deep Learning for Integrated Intelligence: Bridging the Gap between Perception and Inference
Dit-Yan Yeung
Department of Computer Science and Engineering
Joint work with Hao Wang, Naiyan Wang, and Xingjian Shi
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Bayesian Deep Learning
Deep Learning & Graphical Models
Perception & Inference/reasoning
Per
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Motivation:
Graphical model
Bayesian deep learning
Inference/reasoning
Deep learning
Our goal
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Inference & Reasoning: Recommendation
Movie Recommendation
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Inference & Reasoning: Social Network Analysis
• Community Detection • Link Prediction • Information Diffusion
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Bayesian Deep Learning: Under a Principled Framework
Probabilistic Graphical Models
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Collaborative Deep Learning
Wang et al. 2015 (KDD)
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Recommender Systems
Observed preferences:
To predict: Matrix completion
Rating matrix:
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Recommender Systems with Content
Content information:
Plots, directors, actors, etc.
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Modeling the Content Information
Handcrafted features Automatically
learn features
Automatically
learn features and
adapt for ratings
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Modeling the Content Information
1. Powerful features for content information
Deep learning
2. Feedback from rating information Non-i.i.d.
Collaborative deep learning
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Deep Learning
Stacked denoising autoencoders
Convolutional neural networks
Recurrent neural networks
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.
Bengio et al. 2015
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Deep Learning
Stacked denoising autoencoders
Convolutional neural networks
Recurrent neural networks
Typically for i.i.d. data
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Modeling the Content Information
1. Powerful features for content information
Deep learning
2. Feedback from rating information Non-i.i.d.
Collaborative deep learning (CDL)
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Contribution
Collaborative deep learning:
* deep learning for non-i.i.d. data
* joint representation learning and
collaborative filtering
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Contribution
Collaborative deep learning
Complex target:
* beyond targets like classification and regression
* to complete a low-rank matrix
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Contribution
Collaborative deep learning
Complex target
First hierarchical Bayesian models for
hybrid deep recommender system
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Stacked Denoising Autoencoders (SDAE)
Corrupted input Clean input
Vincent et al. 2010
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Probabilistic Matrix Factorization (PMF) Graphical model:
Generative process:
Objective function if using MAP:
latent vector of item j
latent vector of user i
rating of item j from user i
Notation:
Salakhutdinov et al. 2008
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Probabilistic SDAE
Generalized SDAE
Graphical model:
Generative process:
corrupted input
clean input
weights and biases
Notation:
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Collaborative Deep Learning Graphical model:
Collaborative deep learning SDAE
corrupted input
clean input
weights and biases
content representation
rating of item j from user i
latent vector of item j
latent vector of user i
Notation: Two-way interaction
•More powerful representation •Infer missing ratings from content •Infer missing content from ratings
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Collaborative Deep Learning
Neural network representation for degenerated CDL
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Collaborative Deep Learning
Information flows from ratings to content
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Collaborative Deep Learning
Information flows from content to ratings
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Collaborative Deep Learning
Representation learning <-> recommendation
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Learning
maximizing the posterior probability is equivalent to maximizing the joint log-likelihood
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Learning
Prior (regularization) for user latent vectors, weights, and biases
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Learning
Generating item latent vectors from content representation with Gaussian offset
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Learning
‘Generating’ clean input from the output of probabilistic SDAE with Gaussian offset
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Learning
Generating the input of Layer l from the output of Layer l-1 with Gaussian offset
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Learning
measures the error of predicted ratings
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Learning
If goes to infinity, the likelihood becomes
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Update Rules For U and V, use block coordinate descent:
For W and b, use a modified version of backpropagation:
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Datasets
Content information
Titles and abstracts Titles and abstracts Movie plots
Wang et al. 2011 Wang et al. 2013
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Evaluation Metrics
Recall:
Mean Average Precision (mAP):
Higher recall and mAP indicate better recommendation performance
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Comparing Methods
Hybrid methods using BOW and ratings
Loosely coupled; interaction is not two-way
PMF+LDA
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Recall@M
citeulike-t, sparse setting
citeulike-t, dense setting
Netflix, sparse setting
Netflix, dense setting
When the ratings are very sparse:
When the ratings are dense:
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Mean Average Precision (mAP)
Exactly the same as Oord et al. 2013, we set the cutoff point at 500 for each user.
A relative performance boost of about 50%
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Number of Layers
Sparse Setting
Dense Setting
The best performance is achieved when the number of layers is 2 or 3 (4 or 6 layers of generalized neural networks).
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Example User
Moonstruck
True Romance
Romance Movies
Precision: 30% VS 20%
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Example User
Johnny English
American Beauty
Action & Drama Movies
Precision: 50% VS 20%
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Example User
Precision: 90% VS 50%
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Summary: Collaborative Deep Learning
Non-i.i.d (collaborative) deep learning
With a complex target
First hierarchical Bayesian models for
hybrid deep recommender system
Significantly advance the state of the art
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Extension of CDL
Word2vec, tf-idf
Sampling-based, variational inference
Tagging information, networks
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Relational Stacked Denoising Autoencoders
Wang et al. 2015 (AAAI)
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Motivation
• Unsupervised representation learning • Enhance representation power with relational information
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Stacked Denoising Autoencoders (SDAE)
Corrupted input Clean input
Vincent et al. 2010
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Probabilistic SDAE
Generalized SDAE
Graphical model:
Generative process:
corrupted input
clean input
weights and biases
Notation:
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Relational SDAE: Generative Process
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Relational SDAE : Generative Process
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Relational SDAE: Graphical Model
corrupted input
clean input
adjacency matrix
Notation:
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Multi-Relational SDAE : Graphical Model
corrupted input
clean input
adjacency matrix
Notation:
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Relational SDAE: Objective Function
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Update Rules
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From Representation to Tag Recommendation
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Algorithm
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Datasets
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Sparse Setting, citeulike-a
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Dense Setting, citeulike-a
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Sparse Setting, movielens-plot
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Dense Setting, movielens-plot
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Tagging Scientific Articles
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Tagging Movies
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Tagging Movies
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Summary: Relational SDAE
Adapt SDAE for tag recommendation
A probabilistic relational model for
relational deep learning
State-of-the-art performance
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Bayesian Deep Learning: Under a Principled Framework
Relational SDAE Collaborative Deep Learning
Probabilistic Graphical Models
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Take-home Messages
• Probabilistic graphical models for formulating both representation learning and inference/reasoning components
• Learnable representation serving as a bridge
• Tight, two-way interaction is crucial
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Future Goals
General Framework: 1. Ability of understanding text,
images, and videos
2. Ability of inference and planning
under uncertainty
3. Close the gap between human
intelligence and artificial intelligence
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Thanks!
Q&A