Source Themes

Variational Multi-Task Learning with Gumbel-Softmax Priors

We propose variational multi-task learning (VMTL), a general probabilistic inference framework in which we cast multi-task learning as a variational Bayesian inference problem.

Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

We propose a model of semantic memory for WSD in a meta-learning setting.

Variational Topic Inference for Chest X-Ray Report Generation

We propose variational topic inference for automatic report generation.

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

In this paper, we address the challenges of domain shift and the uncertainty with a probabilistic framework based on variational Bayesian inference, by incorporating uncertainty into neural network weights.

Kernel Continual Learning

We deploy an episodic memory unit that stores a subset of samples for each task to learn task-specific classifiers based on kernel ridge regression.

MetaNorm: Learning to Normalize Few-Shot Batches Across Domains

We propose MetaNorm, a simple yet effective meta-learning normalization approach that learns adaptive statistics for few-shot classification and domain generalization.

Repetitive Activity Counting by Sight and Sound

This paper strives for repetitive activity counting in videos. Different from existing works, which all analyze the visual video content only, we incorporate for the first time the corresponding sound into the repetition counting process.

Variational Prototype Inference for Few-Shot Semantic Segmentation

This paper propose variational prototype inference to address few-shot semantic segmentation in a probabilistic framework.

Variational Knowledge Distillation for Disease Classification in Chest X-Rays

We propose variational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays that lever- ages knowledge from EHRs.

Few-Shot Semantic Segmentation with Democratic Attention Networks

Few-Shot Segmentation, Graph Attention, Democratic Attention Network, Multi-Scale Guidance