Search Results for author:
Found 4 papers, 2 papers with code
Shabari: Delayed Decision-Making for Faster and Efficient Serverless Functions
However, today's serverless systems lack performance guarantees for function invocations, thus limiting support for performance-critical applications: we observed severe performance variability (up to 6x).
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models
Our results indicate three key findings: (1) using generative compression, it is feasible to leverage highly compressed data while incurring a negligible impact on machine perceptual quality; (2) machine perceptual quality correlates strongly with deep similarity metrics, indicating a crucial role of these metrics in the development of machine-oriented codecs; and (3) using lossy compressed datasets, (e. g. ImageNet) for pre-training can lead to counter-intuitive scenarios where lossy compression increases machine perceptual quality rather than degrading it.
MOSEL: Inference Serving Using Dynamic Modality Selection
Rapid advancements over the years have helped machine learning models reach previously hard-to-achieve goals, sometimes even exceeding human capabilities.
INFaaS: A Model-less and Managed Inference Serving System
This paper introduces INFaaS, a managed and model-less system for distributed inference serving, where developers simply specify the performance and accuracy requirements for their applications without needing to specify a specific model-variant for each query.
