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Found 7 papers, 1 papers with code
Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning
A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images.
Learning to Prune Filters in Convolutional Neural Networks
Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way.
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
To foster future research we demonstrate the limitations of the current benchmarking setup and provide new reference patient-wise splits for the used data sets.
Hierarchical Attention Prototypical Networks for Few-Shot Text Classification
Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available.
"COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation
The outbreak of the infectious and fatal disease COVID-19 has revealed that pandemics assail public health in two waves: first, from the contagion itself and second, from plagues of suspicion and stigma.
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks.
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
In online RL, we propose a DistRL algorithm that constructs confidence sets using maximum likelihood estimation.
