1,692 research outputs found
Stochastic Inverse Reinforcement Learning
The goal of the inverse reinforcement learning (IRL) problem is to recover
the reward functions from expert demonstrations. However, the IRL problem like
any ill-posed inverse problem suffers the congenital defect that the policy may
be optimal for many reward functions, and expert demonstrations may be optimal
for many policies. In this work, we generalize the IRL problem to a well-posed
expectation optimization problem stochastic inverse reinforcement learning
(SIRL) to recover the probability distribution over reward functions. We adopt
the Monte Carlo expectation-maximization (MCEM) method to estimate the
parameter of the probability distribution as the first solution to the SIRL
problem. The solution is succinct, robust, and transferable for a learning task
and can generate alternative solutions to the IRL problem. Through our
formulation, it is possible to observe the intrinsic property for the IRL
problem from a global viewpoint, and our approach achieves a considerable
performance on the objectworld.Comment: 8+2 pages, 5 figures, Under Revie
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
Motor imagery (MI) classification is one of the most widely-concern research
topics in Electroencephalography (EEG)-based brain-computer interfaces (BCIs)
with extensive industry value. The MI-EEG classifiers' tendency has changed
fundamentally over the past twenty years, while classifiers' performance is
gradually increasing. In particular, owing to the need for characterizing
signals' non-Euclidean inherence, the first geometric deep learning (GDL)
framework, Tensor-CSPNet, has recently emerged in the BCI study. In essence,
Tensor-CSPNet is a deep learning-based classifier on the second-order
statistics of EEGs. In contrast to the first-order statistics, using these
second-order statistics is the classical treatment of EEG signals, and the
discriminative information contained in these second-order statistics is
adequate for MI-EEG classification. In this study, we present another GDL
classifier for MI-EEG classification called Graph-CSPNet, using graph-based
techniques to simultaneously characterize the EEG signals in both the time and
frequency domains. It is realized from the perspective of the time-frequency
analysis that profoundly influences signal processing and BCI studies. Contrary
to Tensor-CSPNet, the architecture of Graph-CSPNet is further simplified with
more flexibility to cope with variable time-frequency resolution for signal
segmentation to capture the localized fluctuations. In the experiments,
Graph-CSPNet is evaluated on subject-specific scenarios from two well-used
MI-EEG datasets and produces near-optimal classification accuracies.Comment: 16 pages, 5 figures, 9 Tables; This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles,
pedestrians and etc.) benefits the on-road motion planning for intelligent and
autonomous vehicles. Complex scenes always yield great challenges in modeling
the patterns of surrounding traffic. For example, one main challenge comes from
the intractable interaction effects in a complex traffic system. In this paper,
we propose a multi-layer architecture Interaction-aware Kalman Neural Networks
(IaKNN) which involves an interaction layer for resolving high-dimensional
traffic environmental observations as interaction-aware accelerations, a motion
layer for transforming the accelerations to interaction aware trajectories, and
a filter layer for estimating future trajectories with a Kalman filter network.
Attributed to the multiple traffic data sources, our end-to-end trainable
approach technically fuses dynamic and interaction-aware trajectories boosting
the prediction performance. Experiments on the NGSIM dataset demonstrate that
IaKNN outperforms the state-of-the-art methods in terms of effectiveness for
traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium
(IV) 202
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
The efficacy of Electroencephalogram (EEG) classifiers can be augmented by
increasing the quantity of available data. In the case of geometric deep
learning classifiers, the input consists of spatial covariance matrices derived
from EEGs. In order to synthesize these spatial covariance matrices and
facilitate future improvements of geometric deep learning classifiers, we
propose a generative modeling technique based on state-of-the-art score-based
models. The quality of generated samples is evaluated through visual and
quantitative assessments using a left/right-hand-movement motor imagery
dataset. The exceptional pixel-level resolution of these generative samples
highlights the formidable capacity of score-based generative modeling.
Additionally, the center (Frechet mean) of the generated samples aligns with
neurophysiological evidence that event-related desynchronization and
synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency
bands during motor imagery processing. The quantitative evaluation revealed
that 84.3% of the generated samples could be accurately predicted by a
pre-trained classifier and an improvement of up to 8.7% in the average accuracy
over ten runs for a specific test subject in a holdout experiment.Comment: 7 pages, 4 figures; This work has been accepted by the 2023 45th
Annual International Conference of the IEEE Engineering in Medicine & Biology
Conference (IEEE EMBC 2023'). Copyright will be transferred without notice,
after which this version may no longer be accessibl
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