Latent Gaussian process (GP) models are widely used in neuroscience to
u...
Transformers have been shown to work well for the task of English euphem...
Latent variable models have become instrumental in computational neurosc...
The recent trend for multi-camera 3D object detection is through the uni...
Various automated program repair (APR) techniques have been proposed to ...
Privacy-preserving machine learning has become a popular area of researc...
One-bit analog-to-digital converter (ADC), performing signal sampling as...
We propose an augmented Parallel-Pyramid Net (P^2 Net) with feature
refi...
The standard approach to fitting an autoregressive spike train model is ...
A fundamental problem in statistical neuroscience is to model how neuron...
Research in population and public health focuses on the mechanisms betwe...
The target of human pose estimation is to determine body part or joint
l...
Safety-critical applications require machine learning models that output...
Nonlinear state-space models are powerful tools to describe dynamical
st...
A fast and effective motion deblurring method has great application valu...
ICU mortality risk prediction is a tough yet important task. On one hand...
Privacy is a major concern in sharing human subject data to researchers ...
State space models provide an interpretable framework for complex time s...
When governed by underlying low-dimensional dynamics, the interdependenc...