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Found 10 papers, 5 papers with code
Mamba-based Deep Learning Approaches for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
Conclusions: Deep learning models can infer major sleep stages from the ANNE One, a wearable system without electroencephalography (EEG), and can be successfully applied to data from adults attending a tertiary care sleep clinic.
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories
We propose a method that integrates constraint learning into imitation learning by extracting driving constraints from expert trajectories.
Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution
The feature importance curves measure the changes across quantiles of outcome distribution given an external impact of change in the explanatory features.
Receiver Resonant Frequency Adaptive Tracking in Wireless Power Transfer Systems Using Primary Variable Capacitor
Parameter variations within the resonant network of wireless power transfer (WPT) systems can cause drift in the resonant frequency, leading to a detuned system that requires higher power capacity and experiences reduced transfer efficiency.
DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots
We demonstrate the effectiveness of our framework in enabling high-quality real-time reconstruction and visualization of unknown scenes as they are captured and streamed from cameras in mobile robots and edge devices.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting
In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections.
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation
We apply the proposed method to the MNIST dataset and the MIT-BIH dataset with a convolutional auto-encoder.
RankSEG: A Consistent Ranking-based Framework for Segmentation
In this paper, we establish a theoretical foundation of segmentation with respect to the Dice/IoU metrics, including the Bayes rule and Dice-/IoU-calibration, analogous to classification-calibration or Fisher consistency in classification.
A General Framework for Revealing Human Mind with auto-encoding GANs
For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.
