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Found 5 papers, 2 papers with code
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Recognizing that the primary object of interest in most settings is the distribution over functions induced by the posterior distribution over neural network parameters, we frame Bayesian inference in neural networks explicitly as inferring a posterior distribution over functions and propose a scalable function-space variational inference method that allows incorporating prior information and results in reliable predictive uncertainty estimates.
PanoViT: Vision Transformer for Room Layout Estimation from a Single Panoramic Image
In this paper, we propose PanoViT, a panorama vision transformer to estimate the room layout from a single panoramic image.
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
For the latter step, instead of using the chain rule based gradient estimators as in existing methods, we propose a variance reduced policy gradient estimator, which only requires two forward passes without backward propagation, thus achieving completely sparse training.
Rethinking Function-Space Variational Inference in Bayesian Neural Networks
Bayesian neural networks (BNNs) define distributions over functions induced by distributions over parameters.
Distortion-aware Monocular Depth Estimation for Omnidirectional Images
A main challenge for tasks on panorama lies in the distortion of objects among images.
