logo
ResearchBunny Logo
Efficient Meta-Learning via Error-based Context Pruning for Implicit Neural Representations

Computer Science

Efficient Meta-Learning via Error-based Context Pruning for Implicit Neural Representations

J. Tack, S. Kim, et al.

Introducing Error-based Context Pruning (ECoP), a groundbreaking meta-learning technique that optimizes large-scale implicit neural representations (INRs) by intelligently selecting context points based on predictive error. This innovative approach enhances reconstruction quality and facilitates learning in high-dimensional signals, showcasing remarkable improvements across various modalities. This research was conducted by Jihoon Tack, Subin Kim, Sihyun Yu, Jaeho Lee, Jinwoo Shin, and Jonathan Richard Schwarz.

00:00
00:00
~3 min • Beginner • English
Citation Metrics
Citations
0
Influential Citations
0
Reference Count
0

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

Related Publications

Explore these studies to deepen your understanding of the subject.

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting
Engineering and Technology

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting

S. V. Oh, S. Yoo, et al.

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification
Medicine and Health

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

K. Seddiki, P. Saudemont, et al.

Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: A Comparative Analysis and Validation Study
Medicine and Health

Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: A Comparative Analysis and Validation Study

H. Chen, M. Alfred, et al.

Permutation-based identification of important biomarkers for complex diseases via machine learning models
Medicine and Health

Permutation-based identification of important biomarkers for complex diseases via machine learning models

X. Mi, B. Zou, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny