One weird trick for parallelizing convolutional neural networks
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.
Code (8)
Similar Papers 제목 키워드 기반
Weird Generalization is Weirdly Brittle
Weird generalization is a phenomenon in which models fine-tuned on data from a narrow domain (e.g. insecure code) develop surprising traits that manifest even outside that domain (e.g. broad misalignment)-a phenomenon th…
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on different knowledge graphs. In this report, we …
AttributeKnowledge GraphsLink PredictionTripletOne Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels. Most …
Pseudo LabelSegmentationSemantic SegmentationWeakly supervised Semantic Segmentation+1Should LLMs be WEIRD? Exploring WEIRDness and Human Rights in Large Language Models
Large language models (LLMs) are often trained on data that reflect WEIRD values: Western, Educated, Industrialized, Rich, and Democratic. This raises concerns about cultural bias and fairness. Using responses to the Wor…
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness, model performance, and new lines of res…
Fairness