A new arXiv preprint describes a method that uses predictive ranking of LLVM intermediate representation to accelerate transfer-learning-based performance autotuning in high-performance computing. The approach aims to reduce the cost of finding optimal configurations as HPC systems grow more complex. It is categorized under machine learning research.
A new arXiv paper addresses the limited availability of labeled data for subtomogram classification in cryo-electron tomography. The authors propose a method to close the gap between simulated cryo-ET data and real experimental images, aiming to improve classification when only a few labeled examples exist. The approach is categorized under machine learning research.
A new arXiv paper introduces SpecAugment-Patch Merging, a method that masks input spectrograms at the patch level before positional embeddings are added, then merges patches to cut computation. The authors describe it as a simple approach to accelerate training of Audio Spectrogram Transformers. The work is categorized under machine learning research.
The paper examines how to distribute heterogeneous goods when screening applicants requires costly effort instead of money. It shows that mechanisms which skip screening entirely achieve better welfare as the variety of objects grows, and analyzes a symmetric continuous market setting. The work sits in the economics and mechanism-design side of machine learning research rather than proposing a new model.