all AI news
Timely Communications for Remote Inference
April 26, 2024, 4:42 a.m. | Md Kamran Chowdhury Shisher, Yin Sun, I-Hong Hou
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we analyze the impact of data freshness on remote inference systems, where a pre-trained neural network infers a time-varying target (e.g., the locations of vehicles and pedestrians) based on features (e.g., video frames) observed at a sensing node (e.g., a camera). One might expect that the performance of a remote inference system degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature …
abstract analyze arxiv communications cs.it cs.lg cs.ni data expect features impact inference locations math.it network neural network node paper pedestrians performance sensing systems type vehicles video
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US