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Found 4 papers, 3 papers with code
Fixing Overconfidence in Dynamic Neural Networks
Dynamic neural networks are a recent technique that promises a remedy for the increasing size of modern deep learning models by dynamically adapting their computational cost to the difficulty of the inputs.
Periodic Activation Functions Induce Stationarity
Neural network models are known to reinforce hidden data biases, making them unreliable and difficult to interpret.
Stationary Activations for Uncertainty Calibration in Deep Learning
We introduce a new family of non-linear neural network activation functions that mimic the properties induced by the widely-used Mat\'ern family of kernels in Gaussian process (GP) models.
Movement Tracking by Optical Flow Assisted Inertial Navigation
We consider a visually dense approach, where the IMU data is fused with the dense optical flow field estimated from the camera data.
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