Search Results for author:
Found 10 papers, 0 papers with code
Adaptive Patching for High-resolution Image Segmentation with Transformers
For high-resolution images, e. g. microscopic pathology images, the quadratic compute and memory cost prohibits the use of an attention-based model, if we are to use smaller patch sizes that are favorable in segmentation.
Adapting CSI-Guided Imaging Across Diverse Environments: An Experimental Study Leveraging Continuous Learning
This study explores the feasibility of adapting CSI-guided imaging across varied environments.
Ultra-Long Sequence Distributed Transformer
This paper presents a novel and efficient distributed training method, the Long Short-Sequence Transformer (LSS Transformer), for training transformer with long sequences.
CSI-Inpainter: Enabling Visual Scene Recovery from CSI Time Sequences for Occlusion Removal
Introducing CSI-Inpainter, a pioneering approach for occlusion removal using Channel State Information (CSI) time sequences, this work propels the application of wireless signal processing into the realm of visual scene recovery.
Myths and Legends in High-Performance Computing
While some laws end, new directions are emerging, such as algorithmic scaling or novel architecture research.
Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction
In this paper, we propose a novel image gradient decomposition method that significantly reduces the memory footprint for ptychographic reconstruction by tessellating image gradients and diffraction measurements into tiles.
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights.
An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence and alleviate memory capacity limitations when training large models and/or using high dimension inputs.
GTOPX Space Mission Benchmarks
This contribution introduces the GTOPX space mission benchmark collection, which is an extension of GTOP database published by the European Space Agency (ESA).
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism.
