698 research outputs found
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Graph networks are a new machine learning (ML) paradigm that supports both
relational reasoning and combinatorial generalization. Here, we develop
universal MatErials Graph Network (MEGNet) models for accurate property
prediction in both molecules and crystals. We demonstrate that the MEGNet
models outperform prior ML models such as the SchNet in 11 out of 13 properties
of the QM9 molecule data set. Similarly, we show that MEGNet models trained on
crystals in the Materials Project substantially outperform prior
ML models in the prediction of the formation energies, band gaps and elastic
moduli of crystals, achieving better than DFT accuracy over a much larger data
set. We present two new strategies to address data limitations common in
materials science and chemistry. First, we demonstrate a physically-intuitive
approach to unify four separate molecular MEGNet models for the internal energy
at 0 K and room temperature, enthalpy and Gibbs free energy into a single free
energy MEGNet model by incorporating the temperature, pressure and entropy as
global state inputs. Second, we show that the learned element embeddings in
MEGNet models encode periodic chemical trends and can be transfer-learned from
a property model trained on a larger data set (formation energies) to improve
property models with smaller amounts of data (band gaps and elastic moduli)
Deep representation learning for human motion prediction and classification
Generative models of 3D human motion are often restricted to a small number
of activities and can therefore not generalize well to novel movements or
applications. In this work we propose a deep learning framework for human
motion capture data that learns a generic representation from a large corpus of
motion capture data and generalizes well to new, unseen, motions. Using an
encoding-decoding network that learns to predict future 3D poses from the most
recent past, we extract a feature representation of human motion. Most work on
deep learning for sequence prediction focuses on video and speech. Since
skeletal data has a different structure, we present and evaluate different
network architectures that make different assumptions about time dependencies
and limb correlations. To quantify the learned features, we use the output of
different layers for action classification and visualize the receptive fields
of the network units. Our method outperforms the recent state of the art in
skeletal motion prediction even though these use action specific training data.
Our results show that deep feedforward networks, trained from a generic mocap
database, can successfully be used for feature extraction from human motion
data and that this representation can be used as a foundation for
classification and prediction.Comment: This paper is published at the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 201
GADY: Unsupervised Anomaly Detection on Dynamic Graphs
Anomaly detection on dynamic graphs refers to detecting entities whose
behaviors obviously deviate from the norms observed within graphs and their
temporal information. This field has drawn increasing attention due to its
application in finance, network security, social networks, and more. However,
existing methods face two challenges: dynamic structure constructing challenge
- difficulties in capturing graph structure with complex time information and
negative sampling challenge - unable to construct excellent negative samples
for unsupervised learning. To address these challenges, we propose Unsupervised
Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first
challenge, we propose a continuous dynamic graph model to capture the
fine-grained information, which breaks the limit of existing discrete methods.
Specifically, we employ a message-passing framework combined with positional
features to get edge embeddings, which are decoded to identify anomalies. For
the second challenge, we pioneer the use of Generative Adversarial Networks to
generate negative interactions. Moreover, we design a loss function to alter
the training goal of the generator while ensuring the diversity and quality of
generated samples. Extensive experiments demonstrate that our proposed GADY
significantly outperforms the previous state-of-the-art method on three
real-world datasets. Supplementary experiments further validate the
effectiveness of our model design and the necessity of each module
WL meet VC
Recently, many works studied the expressive power of graph neural networks
(GNNs) by linking it to the -dimensional Weisfeiler--Leman algorithm
(). Here, the is a well-studied
heuristic for the graph isomorphism problem, which iteratively colors or
partitions a graph's vertex set. While this connection has led to significant
advances in understanding and enhancing GNNs' expressive power, it does not
provide insights into their generalization performance, i.e., their ability to
make meaningful predictions beyond the training set. In this paper, we study
GNNs' generalization ability through the lens of Vapnik--Chervonenkis (VC)
dimension theory in two settings, focusing on graph-level predictions. First,
when no upper bound on the graphs' order is known, we show that the bitlength
of GNNs' weights tightly bounds their VC dimension. Further, we derive an upper
bound for GNNs' VC dimension using the number of colors produced by the
. Secondly, when an upper bound on the graphs' order is
known, we show a tight connection between the number of graphs distinguishable
by the and GNNs' VC dimension. Our empirical study
confirms the validity of our theoretical findings.Comment: arXiv admin note: text overlap with arXiv:2206.1116
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