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Found 7 papers, 5 papers with code
Revisiting Reliability in Large-Scale Machine Learning Research Clusters
Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to grow.
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
Croissant: A Metadata Format for ML-Ready Datasets
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point.
Validating Large Language Models with ReLM
Although large language models (LLMs) have been touted for their ability to generate natural-sounding text, there are growing concerns around possible negative effects of LLMs such as data memorization, bias, and inappropriate language.
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Our analysis of over two million ML jobs in Google datacenters reveals that a significant fraction of model training jobs could benefit from faster input data pipelines.
Progressive Compressed Records: Taking a Byte out of Deep Learning Data
Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices.
Efficient Augmentation via Data Subsampling
Data augmentation is commonly used to encode invariances in learning methods.
