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Found 7 papers, 2 papers with code
Rethinking Semi-supervised Learning with Language Models
Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks.
Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose
We infer finger 3D position $(x, y, z)$ and pose (pitch and yaw) on a mobile device using capacitive sensors which can sense the finger up to 5cm above the screen.
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
We propose a new probabilistic method for unsupervised recovery of corrupted data.
Spatial images from temporal data
Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light).
Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year.
Variational Sparse Coding
We show that these sparse representations are advantageous over standard VAE representations on two benchmark classification tasks (MNIST and Fashion-MNIST) by demonstrating improved classification accuracy and significantly increased robustness to the number of latent dimensions.
Variational Inference for Computational Imaging Inverse Problems
In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts.
