ResLogit: A residual neural network logit model for data-driven choice modelling

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Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occ ...

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Dynamic hand gesture recognition (HGR) is critical for real-time prosthetics and neural robotics applications, where high-density surface electromyography (HD-sEMG) signals provide valuable insights into muscle activity. However, adapting machine learning ...

Lattice light-sheet microscopy provides a crucial observation window into intra- and intercellular physiology of living specimens but at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present meta-l ...

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Since the 80s, indoor radon, a radioactive noble gas, became a public health concern, with the WHO declaring it a carcinogen in 1987. Accurate radon measurement is crucial for estimating exposure and shaping policies to reduce lung cancer. Nowadays, the re ...

ResLogit: A residual neural network logit model for data-driven choice modelling

Chat with Graph Search

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

An Analysis of Model Robustness across Concurrent Distribution Shifts

Learning Algorithms in the Limit

SARS-CoV-2 Infection and the Risk of New Chronic Conditions: Insights from a Longitudinal Population-Based Study

Low-Rank Adaptation for Transformers in Dynamic Hand Gesture Recognition Using High-Density Surface Electromyography

Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging

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HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models

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