1,194 research outputs found
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
The ability to perform effective planning is crucial for building an
instruction-following agent. When navigating through a new environment, an
agent is challenged with (1) connecting the natural language instructions with
its progressively growing knowledge of the world; and (2) performing long-range
planning and decision making in the form of effective exploration and error
correction. Current methods are still limited on both fronts despite extensive
efforts. In this paper, we introduce the Evolving Graphical Planner (EGP), a
model that performs global planning for navigation based on raw sensory input.
The model dynamically constructs a graphical representation, generalizes the
action space to allow for more flexible decision making, and performs efficient
planning on a proxy graph representation. We evaluate our model on a
challenging Vision-and-Language Navigation (VLN) task with photorealistic
images and achieve superior performance compared to previous navigation
architectures. For instance, we achieve a 53% success rate on the test split of
the Room-to-Room navigation task through pure imitation learning, outperforming
previous navigation architectures by up to 5%
A3Graph : adversarial attributed autoencoder for graph representation learning
Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph. © 2021 ACM
Evolving Computation Graphs
Graph neural networks (GNNs) have demonstrated success in modeling relational
data, especially for data that exhibits homophily: when a connection between
nodes tends to imply that they belong to the same class. However, while this
assumption is true in many relevant situations, there are important real-world
scenarios that violate this assumption, and this has spurred research into
improving GNNs for these cases. In this work, we propose Evolving Computation
Graphs (ECGs), a novel method for enhancing GNNs on heterophilic datasets. Our
approach builds on prior theoretical insights linking node degree, high
homophily, and inter vs intra-class embedding similarity by rewiring the GNNs'
computation graph towards adding edges that connect nodes that are likely to be
in the same class. We utilise weaker classifiers to identify these edges,
ultimately improving GNN performance on non-homophilic data as a result. We
evaluate ECGs on a diverse set of recently-proposed heterophilous datasets and
demonstrate improvements over the relevant baselines. ECG presents a simple,
intuitive and elegant approach for improving GNN performance on heterophilic
datasets without requiring prior domain knowledge.Comment: To appear at ICML TAGML 2023; 18 pages, 2 figure
Unified Topological Inference for Brain Networks in Temporal Lobe Epilepsy Using the Wasserstein Distance
Persistent homology can extract hidden topological signals present in brain
networks. Persistent homology summarizes the changes of topological structures
over multiple different scales called filtrations. Doing so detect hidden
topological signals that persist over multiple scales. However, a key obstacle
of applying persistent homology to brain network studies has always been the
lack of coherent statistical inference framework. To address this problem, we
present a unified topological inference framework based on the Wasserstein
distance. Our approach has no explicit models and distributional assumptions.
The inference is performed in a completely data driven fashion. The method is
applied to the resting-state functional magnetic resonance images (rs-fMRI) of
the temporal lobe epilepsy patients collected at two different sites:
University of Wisconsin-Madison and the Medical College of Wisconsin. However,
the topological method is robust to variations due to sex and acquisition, and
thus there is no need to account for sex and site as categorical nuisance
covariates. We are able to localize brain regions that contribute the most to
topological differences. We made MATLAB package available at
https://github.com/laplcebeltrami/dynamicTDA that was used to perform all the
analysis in this study
Positional Encoding-based Resident Identification in Multi-resident Smart Homes
We propose a novel resident identification framework to identify residents in
a multi-occupant smart environment. The proposed framework employs a feature
extraction model based on the concepts of positional encoding. The feature
extraction model considers the locations of homes as a graph. We design a novel
algorithm to build such graphs from layout maps of smart environments. The
Node2Vec algorithm is used to transform the graph into high-dimensional node
embeddings. A Long Short-Term Memory (LSTM) model is introduced to predict the
identities of residents using temporal sequences of sensor events with the node
embeddings. Extensive experiments show that our proposed scheme effectively
identifies residents in a multi-occupant environment. Evaluation results on two
real-world datasets demonstrate that our proposed approach achieves 94.5% and
87.9% accuracy, respectively.Comment: 27 pages, 11 figures, 2 table
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Node classification is a classical graph machine learning task on which Graph
Neural Networks (GNNs) have recently achieved strong results. However, it is
often believed that standard GNNs only work well for homophilous graphs, i.e.,
graphs where edges tend to connect nodes of the same class. Graphs without this
property are called heterophilous, and it is typically assumed that specialized
methods are required to achieve strong performance on such graphs. In this
work, we challenge this assumption. First, we show that the standard datasets
used for evaluating heterophily-specific models have serious drawbacks, making
results obtained by using them unreliable. The most significant of these
drawbacks is the presence of a large number of duplicate nodes in the datasets
Squirrel and Chameleon, which leads to train-test data leakage. We show that
removing duplicate nodes strongly affects GNN performance on these datasets.
Then, we propose a set of heterophilous graphs of varying properties that we
believe can serve as a better benchmark for evaluating the performance of GNNs
under heterophily. We show that standard GNNs achieve strong results on these
heterophilous graphs, almost always outperforming specialized models. Our
datasets and the code for reproducing our experiments are available at
https://github.com/yandex-research/heterophilous-graph
Machine Learning on Neutron and X-Ray Scattering
Neutron and X-ray scattering represent two state-of-the-art materials
characterization techniques that measure materials' structural and dynamical
properties with high precision. These techniques play critical roles in
understanding a wide variety of materials systems, from catalysis to polymers,
nanomaterials to macromolecules, and energy materials to quantum materials. In
recent years, neutron and X-ray scattering have received a significant boost
due to the development and increased application of machine learning to
materials problems. This article reviews the recent progress in applying
machine learning techniques to augment various neutron and X-ray scattering
techniques. We highlight the integration of machine learning methods into the
typical workflow of scattering experiments. We focus on scattering problems
that faced challenge with traditional methods but addressable using machine
learning, such as leveraging the knowledge of simple materials to model more
complicated systems, learning with limited data or incomplete labels,
identifying meaningful spectra and materials' representations for learning
tasks, mitigating spectral noise, and many others. We present an outlook on a
few emerging roles machine learning may play in broad types of scattering and
spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom
Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks
Trajectory-User Linking (TUL) is crucial for human mobility modeling by
linking diferent trajectories to users with the exploration of complex mobility
patterns. Existing works mainly rely on the recurrent neural framework to
encode the temporal dependencies in trajectories, have fall short in capturing
spatial-temporal global context for TUL prediction. To ill this gap, this work
presents a new hierarchical spatio-temporal attention neural network, called
AttnTUL, to jointly encode the local trajectory transitional patterns and
global spatial dependencies for TUL. Speciically, our irst model component is
built over the graph neural architecture to preserve the local and global
context and enhance the representation paradigm of geographical regions and
user trajectories. Additionally, a hierarchically structured attention network
is designed to simultaneously encode the intra-trajectory and inter-trajectory
dependencies, with the integration of the temporal attention mechanism and
global elastic attentional encoder. Extensive experiments demonstrate the
superiority of our AttnTUL method as compared to state-of-the-art baselines on
various trajectory datasets. The source code of our model is available at
https://github.com/Onedean/AttnTUL.Comment: 22 pages, 8 figures, accepted by ACM Trans. Knowl. Discov. Data
Journal (TKDD
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