Search Results for author:
Found 6 papers, 1 papers with code
What is different between these datasets?
The performance of machine learning models heavily depends on the quality of input data, yet real-world applications often encounter various data-related challenges.
Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training
Our method enables pruning and training simultaneously, which saves energy in both the training and inference phases and avoids extra computational overhead from gating modules at inference time.
Topical: Learning Repository Embeddings from Source Code using Attention
This paper presents Topical, a novel deep neural network for repository level embeddings.
On the Utility of Prediction Sets in Human-AI Teams
We explore how such prediction sets impact expert decision-making in human-AI teams.
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation
We demonstrate that the widely varying data quality on FL client nodes leads to a sub-optimal centralised FL model for COVID-19 chest CT image segmentation.
Training a Task-Specific Image Reconstruction Loss
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution.
