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Found 12 papers, 4 papers with code
Attentional Graph Neural Networks for Robust Massive Network Localization
To tap the potential of GNNs in regression, this paper integrates GNNs with attention mechanism, a technique that revolutionized sequential learning tasks with its adaptability and robustness, to tackle a challenging nonlinear regression problem: network localization.
GLIME: General, Stable and Local LIME Explanation
Additionally, LIME's sampling neighborhood is non-local and biased towards the reference, resulting in poor local fidelity and sensitivity to reference choice.
Theoretical foundations of studying criticality in the brain
These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data.
Koopman neural operator as a mesh-free solver of non-linear partial differential equations
In machine learning, numerous latest advances of solver designs are accomplished in developing neural operators, a kind of mesh-free approximators of the infinite-dimensional operators that map between different parameterization spaces of equation solutions.
KoopmanLab: machine learning for solving complex physics equations
To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator family, for learning PDEs without analytic solutions or closed forms.
Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels
Finally, we analyze our findings with information bottleneck theory to confirm the precise relations among dynamic isometry, mutual information maximization, and optimal channel properties in deep learning.
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow
In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows.
Bridging the information and dynamics attributes of neural activities
The brain works as a dynamic system to process information.
Resilience in Industrial Internet of Things Systems: A Communication Perspective
Industrial Internet of Things is an ultra-large-scale system that is much more sophisticated and fragile than conventional industrial platforms.
A unified theory of information transfer and causal relation
Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems.
Self-organized critical dynamics of RNA virus evolution
We anticipate our approach to be a general formalism to portray RNA virus evolution and help identify potential virus lineages to be concerned.
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion.
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Gesture Recognition
on DVS128 Gesture
