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Found 17 papers, 6 papers with code
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints
Fingerprint-based indoor localization is often labor-intensive due to the need for dense grids and repeated measurements across time and space.
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding
Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding.
Video Deblurring by Sharpness Prior Detection and Edge Information
Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance.
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach
The rapid growth of AI in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets.
PeriGuru: A Peripheral Robotic Mobile App Operation Assistant based on GUI Image Understanding and Prompting with LLM
With considerations for privacy, permissions, and cross-platform compatibility issues, we endeavor to devise and develop PeriGuru in this work, a peripheral robotic mobile app operation assistant based on GUI image understanding and prompting with Large Language Model (LLM).
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
By approximating the Koopman operator, an infinite-dimensional operator governing all possible observations of the dynamic system, to act on the flow mapping of the dynamic system, we can equivalently learn the solution of a non-linear PDE family by solving simple linear prediction problems.
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
