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Found 8 papers, 2 papers with code
Investigation into the Training Dynamics of Learned Optimizers
Optimization is an integral part of modern deep learning.
Learning to Optimize with Dynamic Mode Decomposition
Designing faster optimization algorithms is of ever-growing interest.
SimpleTRON: Simple Transformer with O(N) Complexity
In this paper, we propose that the dot product pairwise matching attention layer, which is widely used in Transformer-based models, is redundant for the model performance.
Image Inpainting Using Wasserstein Generative Adversarial Imputation Network
Image inpainting is one of the important tasks in computer vision which focuses on the reconstruction of missing regions in an image.
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
MetaDL Challenge 2020 focused on image classification tasks in few-shot settings.
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Missing data is one of the most common preprocessing problems.
Unsupervised Latent Space Translation Network
One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation.
Missing Features Reconstruction and Its Impact on Classification Accuracy
The imputation impact is researched on a combination of traditional methods such as k-NN, linear regression, and MICE compared to modern imputation methods such as multi-layer perceptron (MLP) and gradient boosted trees (XGBT).
