37 research outputs found
NIR image colorization with graph-convolutional neural networks
Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
Many current approaches to machine learning in particle physics use generic
architectures that require large numbers of parameters and disregard underlying
physics principles, limiting their applicability as scientific modeling tools.
In this work, we present a machine learning architecture that uses a set of
inputs maximally reduced with respect to the full 6-dimensional Lorentz
symmetry, and is fully permutation-equivariant throughout. We study the
application of this network architecture to the standard task of top quark
tagging and show that the resulting network outperforms all existing
competitors despite much lower model complexity. In addition, we present a
Lorentz-covariant variant of the same network applied to a 4-momentum
regression task
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition
Speech emotion recognition (SER) is an essential part of human-computer
interaction. In this paper, we propose an SER network based on a Graph
Isomorphism Network with Weighted Multiple Aggregators (WMA-GIN), which can
effectively handle the problem of information confusion when neighbour nodes'
features are aggregated together in GIN structure. Moreover, a Full-Adjacent
(FA) layer is adopted for alleviating the over-squashing problem, which is
existed in all Graph Neural Network (GNN) structures, including GIN.
Furthermore, a multi-phase attention mechanism and multi-loss training strategy
are employed to avoid missing the useful emotional information in the stacked
WMA-GIN layers. We evaluated the performance of our proposed WMA-GIN on the
popular IEMOCAP dataset. The experimental results show that WMA-GIN outperforms
other GNN-based methods and is comparable to some advanced non-graph-based
methods by achieving 72.48% of weighted accuracy (WA) and 67.72% of unweighted
accuracy (UA).Comment: Accepted by Interspeech 202
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Human brains lie at the core of complex neurobiological systems, where the
neurons, circuits, and subsystems interact in enigmatic ways. Understanding the
structural and functional mechanisms of the brain has long been an intriguing
pursuit for neuroscience research and clinical disorder therapy. Mapping the
connections of the human brain as a network is one of the most pervasive
paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged
as a potential method for modeling complex network data. Deep models, on the
other hand, have low interpretability, which prevents their usage in
decision-critical contexts like healthcare. To bridge this gap, we propose an
interpretable framework to analyze disorder-specific Regions of Interest (ROIs)
and prominent connections. The proposed framework consists of two modules: a
brain-network-oriented backbone model for disease prediction and a globally
shared explanation generator that highlights disorder-specific biomarkers
including salient ROIs and important connections. We conduct experiments on
three real-world datasets of brain disorders. The results verify that our
framework can obtain outstanding performance and also identify meaningful
biomarkers. All code for this work is available at
https://github.com/HennyJie/IBGNN.git.Comment: Previous version presented at icml-imlh 2021 (no proceedings,
archived at 2107.05097), this version is accepted to miccai 202
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization
There are plenty of graph neural network (GNN) accelerators being proposed.
However, they highly rely on users' hardware expertise and are usually
optimized for one specific GNN model, making them challenging for practical use
. Therefore, in this work, we propose GNNBuilder, the first automated, generic,
end-to-end GNN accelerator generation framework. It features four advantages:
(1) GNNBuilder can automatically generate GNN accelerators for a wide range of
GNN models arbitrarily defined by users; (2) GNNBuilder takes standard PyTorch
programming interface, introducing zero overhead for algorithm developers; (3)
GNNBuilder supports end-to-end code generation, simulation, accelerator
optimization, and hardware deployment, realizing a push-button fashion for GNN
accelerator design; (4) GNNBuilder is equipped with accurate performance models
of its generated accelerator, enabling fast and flexible design space
exploration (DSE). In the experiments, first, we show that our accelerator
performance model has errors within for latency prediction and
for BRAM count prediction. Second, we show that our generated accelerators can
outperform CPU by and GPU by . This framework is
open-source, and the code is available at
https://anonymous.4open.science/r/gnn-builder-83B4/.Comment: 10 pages, 7 figures, 4 tables, 3 listing
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
The application of machine learning (ML) models to the analysis of
optimization algorithms requires the representation of optimization problems
using numerical features. These features can be used as input for ML models
that are trained to select or to configure a suitable algorithm for the problem
at hand. Since in pure black-box optimization information about the problem
instance can only be obtained through function evaluation, a common approach is
to dedicate some function evaluations for feature extraction, e.g., using
random sampling. This approach has two key downsides: (1) It reduces the budget
left for the actual optimization phase, and (2) it neglects valuable
information that could be obtained from a problem-solver interaction.
In this paper, we propose a feature extraction method that describes the
trajectories of optimization algorithms using simple descriptive statistics. We
evaluate the generated features for the task of classifying problem classes
from the Black Box Optimization Benchmarking (BBOB) suite. We demonstrate that
the proposed DynamoRep features capture enough information to identify the
problem class on which the optimization algorithm is running, achieving a mean
classification accuracy of 95% across all experiments.Comment: 9 pages, 5 figure