167,680 research outputs found
Stage-graph representations
AbstractWe consider graph applications of the well-known paradigm “killing two birds with one stone”. In the plane, this gives rise to a stage graph as follows: vertices are the points, and u, v is an edge if and only if the (infinite, straight) line segment joining u to v intersects the stage. Such graphs are shown to be comparability graphs of ordered sets of dimension 2. Similar graphs can be constructed when we have a fixed number k of stages on the plane. In this case, u, v is an edge if and only if the (straight) line segment uv intersects one of the k stages. In this paper, we study stage representations of stage graphs and give upper and lower bounds on the number of stages needed to represent a graph
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI. Since no single
planner can work well for all tasks and domains, portfolio-based techniques
have become increasingly popular in recent years. In particular, deep learning
emerges as a promising methodology for online planner selection. Owing to the
recent development of structural graph representations of planning tasks, we
propose a graph neural network (GNN) approach to selecting candidate planners.
GNNs are advantageous over a straightforward alternative, the convolutional
neural networks, in that they are invariant to node permutations and that they
incorporate node labels for better inference.
Additionally, for cost-optimal planning, we propose a two-stage adaptive
scheduling method to further improve the likelihood that a given task is solved
in time. The scheduler may switch at halftime to a different planner,
conditioned on the observed performance of the first one. Experimental results
validate the effectiveness of the proposed method against strong baselines,
both deep learning and non-deep learning based.
The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at
https://github.com/matenure/GNN_planner. Data set is released at
https://github.com/IBM/IPC-graph-dat
Symmetrization for Embedding Directed Graphs
Recently, one has seen a surge of interest in developing such methods
including ones for learning such representations for (undirected) graphs (while
preserving important properties). However, most of the work to date on
embedding graphs has targeted undirected networks and very little has focused
on the thorny issue of embedding directed networks. In this paper, we instead
propose to solve the directed graph embedding problem via a two-stage approach:
in the first stage, the graph is symmetrized in one of several possible ways,
and in the second stage, the so-obtained symmetrized graph is embedded using
any state-of-the-art (undirected) graph embedding algorithm. Note that it is
not the objective of this paper to propose a new (undirected) graph embedding
algorithm or discuss the strengths and weaknesses of existing ones; all we are
saying is that whichever be the suitable graph embedding algorithm, it will fit
in the above proposed symmetrization framework.Comment: has been accepted to The Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI 2019) Student Abstract and Poster Progra
Learning Cross-modal Context Graph for Visual Grounding
Visual grounding is a ubiquitous building block in many vision-language tasks
and yet remains challenging due to large variations in visual and linguistic
features of grounding entities, strong context effect and the resulting
semantic ambiguities. Prior works typically focus on learning representations
of individual phrases with limited context information. To address their
limitations, this paper proposes a language-guided graph representation to
capture the global context of grounding entities and their relations, and
develop a cross-modal graph matching strategy for the multiple-phrase visual
grounding task. In particular, we introduce a modular graph neural network to
compute context-aware representations of phrases and object proposals
respectively via message propagation, followed by a graph-based matching module
to generate globally consistent localization of grounding phrases. We train the
entire graph neural network jointly in a two-stage strategy and evaluate it on
the Flickr30K Entities benchmark. Extensive experiments show that our method
outperforms the prior state of the arts by a sizable margin, evidencing the
efficacy of our grounding framework. Code is available at
"https://github.com/youngfly11/LCMCG-PyTorch".Comment: AAAI-202
Self-supervised graph representations of WSIs
In this manuscript we propose a framework for the analysis of whole slide images (WSI) on the cell entity space with self-supervised deep learning on graphs and explore its representation quality at different levels of application. It consists of a two step process in which the cell level analysis is performed locally, by clusters of nearby cells that can be seen as small regions of the image, in order to learn representations that capture the cell environment and distribution. In a second stage, a WSI graph is generated with these regions as nodes and the representations learned as initial node embeddings. The graph is leveraged for a downstream task, region of interest (ROI) detection addressed as a graph clustering. The representations outperform the evaluation baselines at both levels of application, which has been carried out predicting whether a cell, or region, is tumor or not based on its learned representations with a logistic regressor.This work has been supported by the Spanish Research Agency (AEI) under project PID2020- 116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033 and the FI-AGAUR grant funded by Direcció General de Recerca (DGR) of Departament de Recerca i Universitats (REU) of the Generalitat de Catalunya.Peer ReviewedPostprint (published version
Independent Distribution Regularization for Private Graph Embedding
Learning graph embeddings is a crucial task in graph mining tasks. An
effective graph embedding model can learn low-dimensional representations from
graph-structured data for data publishing benefiting various downstream
applications such as node classification, link prediction, etc. However, recent
studies have revealed that graph embeddings are susceptible to attribute
inference attacks, which allow attackers to infer private node attributes from
the learned graph embeddings. To address these concerns, privacy-preserving
graph embedding methods have emerged, aiming to simultaneously consider primary
learning and privacy protection through adversarial learning. However, most
existing methods assume that representation models have access to all sensitive
attributes in advance during the training stage, which is not always the case
due to diverse privacy preferences. Furthermore, the commonly used adversarial
learning technique in privacy-preserving representation learning suffers from
unstable training issues. In this paper, we propose a novel approach called
Private Variational Graph AutoEncoders (PVGAE) with the aid of independent
distribution penalty as a regularization term. Specifically, we split the
original variational graph autoencoder (VGAE) to learn sensitive and
non-sensitive latent representations using two sets of encoders. Additionally,
we introduce a novel regularization to enforce the independence of the
encoders. We prove the theoretical effectiveness of regularization from the
perspective of mutual information. Experimental results on three real-world
datasets demonstrate that PVGAE outperforms other baselines in private
embedding learning regarding utility performance and privacy protection.Comment: Accepted by CIKM 202
Data-Efficient Decentralized Visual SLAM
Decentralized visual simultaneous localization and mapping (SLAM) is a
powerful tool for multi-robot applications in environments where absolute
positioning systems are not available. Being visual, it relies on cameras,
cheap, lightweight and versatile sensors, and being decentralized, it does not
rely on communication to a central ground station. In this work, we integrate
state-of-the-art decentralized SLAM components into a new, complete
decentralized visual SLAM system. To allow for data association and
co-optimization, existing decentralized visual SLAM systems regularly exchange
the full map data between all robots, incurring large data transfers at a
complexity that scales quadratically with the robot count. In contrast, our
method performs efficient data association in two stages: in the first stage a
compact full-image descriptor is deterministically sent to only one robot. In
the second stage, which is only executed if the first stage succeeded, the data
required for relative pose estimation is sent, again to only one robot. Thus,
data association scales linearly with the robot count and uses highly compact
place representations. For optimization, a state-of-the-art decentralized
pose-graph optimization method is used. It exchanges a minimum amount of data
which is linear with trajectory overlap. We characterize the resulting system
and identify bottlenecks in its components. The system is evaluated on publicly
available data and we provide open access to the code.Comment: 8 pages, submitted to ICRA 201
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