41 research outputs found
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
The key to electroencephalography (EEG)-based brain-computer interface (BCI)
lies in neural decoding, and its accuracy can be improved by using hybrid BCI
paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually
require separate processing processes for EEG signals in each paradigm, which
greatly reduces the efficiency of EEG feature extraction and the
generalizability of the model. Here, we propose a two-stream convolutional
neural network (TSCNN) based hybrid brain-computer interface. It combines
steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms.
TSCNN automatically learns to extract EEG features in the two paradigms in the
training process, and improves the decoding accuracy by 25.4% compared with the
MI mode, and 2.6% compared with SSVEP mode in the test data. Moreover, the
versatility of TSCNN is verified as it provides considerable performance in
both single-mode (70.2% for MI, 93.0% for SSVEP) and hybrid-mode scenarios
(95.6% for MI-SSVEP hybrid). Our work will facilitate the real-world
applications of EEG-based BCI systems
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
We tackle the problem of graph out-of-distribution (OOD) generalization.
Existing graph OOD algorithms either rely on restricted assumptions or fail to
exploit environment information in training data. In this work, we propose to
simultaneously incorporate label and environment causal independence (LECI) to
fully make use of label and environment information, thereby addressing the
challenges faced by prior methods on identifying causal and invariant
subgraphs. We further develop an adversarial training strategy to jointly
optimize these two properties for casual subgraph discovery with theoretical
guarantees. Extensive experiments and analysis show that LECI significantly
outperforms prior methods on both synthetic and real-world datasets,
establishing LECI as a practical and effective solution for graph OOD
generalization
Stochastic Optimization of Areas UnderPrecision-Recall Curves with Provable Convergence
Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common
metrics for evaluating classification performance for imbalanced problems.
Compared with AUROC, AUPRC is a more appropriate metric for highly imbalanced
datasets. While stochastic optimization of AUROC has been studied extensively,
principled stochastic optimization of AUPRC has been rarely explored. In this
work, we propose a principled technical method to optimize AUPRC for deep
learning. Our approach is based on maximizing the averaged precision (AP),
which is an unbiased point estimator of AUPRC. We cast the objective into a sum
of {\it dependent compositional functions} with inner functions dependent on
random variables of the outer level. We propose efficient adaptive and
non-adaptive stochastic algorithms named SOAP with {\it provable convergence
guarantee under mild conditions} by leveraging recent advances in stochastic
compositional optimization. Extensive experimental results on image and graph
datasets demonstrate that our proposed method outperforms prior methods on
imbalanced problems in terms of AUPRC. To the best of our knowledge, our work
represents the first attempt to optimize AUPRC with provable convergence. The
SOAP has been implemented in the libAUC library at~\url{https://libauc.org/}.Comment: 24 pages, 10 figure
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
We study property prediction for crystal materials. A crystal structure
consists of a minimal unit cell that is repeated infinitely in 3D space. How to
accurately represent such repetitive structures in machine learning models
remains unresolved. Current methods construct graphs by establishing edges only
between nearby nodes, thereby failing to faithfully capture infinite repeating
patterns and distant interatomic interactions. In this work, we propose several
innovations to overcome these limitations. First, we propose to model
physics-principled interatomic potentials directly instead of only using
distances as in many existing methods. These potentials include the Coulomb
potential, London dispersion potential, and Pauli repulsion potential. Second,
we model the complete set of potentials among all atoms, instead of only
between nearby atoms as in existing methods. This is enabled by our
approximations of infinite potential summations with provable error bounds. We
further develop efficient algorithms to compute the approximations. Finally, we
propose to incorporate our computations of complete interatomic potentials into
message passing neural networks for representation learning. We perform
experiments on the JARVIS and Materials Project benchmarks for evaluation.
Results show that the use of interatomic potentials and complete interatomic
potentials leads to consistent performance improvements with reasonable
computational costs. Our code is publicly available as part of the AIRS library
(https://github.com/divelab/AIRS)
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Supervised machine learning approaches have been increasingly used in
accelerating electronic structure prediction as surrogates of first-principle
computational methods, such as density functional theory (DFT). While numerous
quantum chemistry datasets focus on chemical properties and atomic forces, the
ability to achieve accurate and efficient prediction of the Hamiltonian matrix
is highly desired, as it is the most important and fundamental physical
quantity that determines the quantum states of physical systems and chemical
properties. In this work, we generate a new Quantum Hamiltonian dataset, named
as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics
trajectories and 130,831 stable molecular geometries, based on the QM9 dataset.
By designing benchmark tasks with various molecules, we show that current
machine learning models have the capacity to predict Hamiltonian matrices for
arbitrary molecules. Both the QH9 dataset and the baseline models are provided
to the community through an open-source benchmark, which can be highly valuable
for developing machine learning methods and accelerating molecular and
materials design for scientific and technological applications. Our benchmark
is publicly available at
https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmark
Automated Data Augmentations for Graph Classification
Data augmentations are effective in improving the invariance of learning
machines. We argue that the corechallenge of data augmentations lies in
designing data transformations that preserve labels. This is
relativelystraightforward for images, but much more challenging for graphs. In
this work, we propose GraphAug, a novelautomated data augmentation method
aiming at computing label-invariant augmentations for graph
classification.Instead of using uniform transformations as in existing studies,
GraphAug uses an automated augmentationmodel to avoid compromising critical
label-related information of the graph, thereby producing
label-invariantaugmentations at most times. To ensure label-invariance, we
develop a training method based on reinforcementlearning to maximize an
estimated label-invariance probability. Comprehensive experiments show that
GraphAugoutperforms previous graph augmentation methods on various graph
classification tasks
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Visual object detection is a computer vision-based artificial intelligence
(AI) technique which has many practical applications (e.g., fire hazard
monitoring). However, due to privacy concerns and the high cost of transmitting
video data, it is highly challenging to build object detection models on
centrally stored large training datasets following the current approach.
Federated learning (FL) is a promising approach to resolve this challenge.
Nevertheless, there currently lacks an easy to use tool to enable computer
vision application developers who are not experts in federated learning to
conveniently leverage this technology and apply it in their systems. In this
paper, we report FedVision - a machine learning engineering platform to support
the development of federated learning powered computer vision applications. The
platform has been deployed through a collaboration between WeBank and Extreme
Vision to help customers develop computer vision-based safety monitoring
solutions in smart city applications. Over four months of usage, it has
achieved significant efficiency improvement and cost reduction while removing
the need to transmit sensitive data for three major corporate customers. To the
best of our knowledge, this is the first real application of FL in computer
vision-based tasks