87 research outputs found
Learned Cardinalities: Estimating Correlated Joins with Deep Learning
We describe a new deep learning approach to cardinality estimation. MSCN is a
multi-set convolutional network, tailored to representing relational query
plans, that employs set semantics to capture query features and true
cardinalities. MSCN builds on sampling-based estimation, addressing its
weaknesses when no sampled tuples qualify a predicate, and in capturing
join-crossing correlations. Our evaluation of MSCN using a real-world dataset
shows that deep learning significantly enhances the quality of cardinality
estimation, which is the core problem in query optimization.Comment: CIDR 2019. https://github.com/andreaskipf/learnedcardinalitie
Estimating Cardinalities with Deep Sketches
We introduce Deep Sketches, which are compact models of databases that allow
us to estimate the result sizes of SQL queries. Deep Sketches are powered by a
new deep learning approach to cardinality estimation that can capture
correlations between columns, even across tables. Our demonstration allows
users to define such sketches on the TPC-H and IMDb datasets, monitor the
training process, and run ad-hoc queries against trained sketches. We also
estimate query cardinalities with HyPer and PostgreSQL to visualize the gains
over traditional cardinality estimators.Comment: To appear in SIGMOD'1
Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view
of link prediction on graphs. Interaction data such as movie ratings can be
represented by a bipartite user-item graph with labeled edges denoting observed
ratings. Building on recent progress in deep learning on graph-structured data,
we propose a graph auto-encoder framework based on differentiable message
passing on the bipartite interaction graph. Our model shows competitive
performance on standard collaborative filtering benchmarks. In settings where
complimentary feature information or structured data such as a social network
is available, our framework outperforms recent state-of-the-art methods.Comment: 9 pages, 3 figures, updated with additional experimental evaluatio
MolGAN: An implicit generative model for small molecular graphs
Deep generative models for graph-structured data offer a new angle on the
problem of chemical synthesis: by optimizing differentiable models that
directly generate molecular graphs, it is possible to side-step expensive
search procedures in the discrete and vast space of chemical structures. We
introduce MolGAN, an implicit, likelihood-free generative model for small
molecular graphs that circumvents the need for expensive graph matching
procedures or node ordering heuristics of previous likelihood-based methods.
Our method adapts generative adversarial networks (GANs) to operate directly on
graph-structured data. We combine our approach with a reinforcement learning
objective to encourage the generation of molecules with specific desired
chemical properties. In experiments on the QM9 chemical database, we
demonstrate that our model is capable of generating close to 100% valid
compounds. MolGAN compares favorably both to recent proposals that use
string-based (SMILES) representations of molecules and to a likelihood-based
method that directly generates graphs, albeit being susceptible to mode
collapse.Comment: 11 pages, 3 figures, 3 table
RadixSpline: A Single-Pass Learned Index
Recent research has shown that learned models can outperform state-of-the-art
index structures in size and lookup performance. While this is a very promising
result, existing learned structures are often cumbersome to implement and are
slow to build. In fact, most approaches that we are aware of require multiple
training passes over the data.
We introduce RadixSpline (RS), a learned index that can be built in a single
pass over the data and is competitive with state-of-the-art learned index
models, like RMI, in size and lookup performance. We evaluate RS using the SOSD
benchmark and show that it achieves competitive results on all datasets,
despite the fact that it only has two parameters.Comment: Third International Workshop on Exploiting Artificial Intelligence
Techniques for Data Management (aiDM 2020
DeepInf: Social Influence Prediction with Deep Learning
Social and information networking activities such as on Facebook, Twitter,
WeChat, and Weibo have become an indispensable part of our everyday life, where
we can easily access friends' behaviors and are in turn influenced by them.
Consequently, an effective social influence prediction for each user is
critical for a variety of applications such as online recommendation and
advertising.
Conventional social influence prediction approaches typically design various
hand-crafted rules to extract user- and network-specific features. However,
their effectiveness heavily relies on the knowledge of domain experts. As a
result, it is usually difficult to generalize them into different domains.
Inspired by the recent success of deep neural networks in a wide range of
computing applications, we design an end-to-end framework, DeepInf, to learn
users' latent feature representation for predicting social influence. In
general, DeepInf takes a user's local network as the input to a graph neural
network for learning her latent social representation. We design strategies to
incorporate both network structures and user-specific features into
convolutional neural and attention networks. Extensive experiments on Open
Academic Graph, Twitter, Weibo, and Digg, representing different types of
social and information networks, demonstrate that the proposed end-to-end
model, DeepInf, significantly outperforms traditional feature engineering-based
approaches, suggesting the effectiveness of representation learning for social
applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding
CompILE: Compositional Imitation Learning and Execution
We introduce Compositional Imitation Learning and Execution (CompILE): a
framework for learning reusable, variable-length segments of
hierarchically-structured behavior from demonstration data. CompILE uses a
novel unsupervised, fully-differentiable sequence segmentation module to learn
latent encodings of sequential data that can be re-composed and executed to
perform new tasks. Once trained, our model generalizes to sequences of longer
length and from environment instances not seen during training. We evaluate
CompILE in a challenging 2D multi-task environment and a continuous control
task, and show that it can find correct task boundaries and event encodings in
an unsupervised manner. Latent codes and associated behavior policies
discovered by CompILE can be used by a hierarchical agent, where the high-level
policy selects actions in the latent code space, and the low-level,
task-specific policies are simply the learned decoders. We found that our
CompILE-based agent could learn given only sparse rewards, where agents without
task-specific policies struggle.Comment: ICML (2019
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