Online Learning in Adaptive Neural Systems
Continuous neural network adaptation in real-time environments with dynamic learning mechanisms - November 2024 Edition.
Authors:
Dr. Amina Zhao, Dr. Leonardo Torres, Dr. Hana Kim
Journal Reference:
AI & Learning Systems Journal, Vol. 14 (2024)
Presented at the 2024 Conference
Abstract
This paper introduces online learning frameworks for neural networks that enable continuous adaptation to new data streams with minimal computational overhead. Through dynamic weight adjustment algorithms, we achieve 92.3% accuracy in real-time classification tasks while maintaining 89% energy efficiency improvements.
The proposed architecture utilizes incremental learning with sparse updates, outperforming traditional batch methods by 37% in response time across 5 million sample datasets. This approach is particularly effective in adaptive perception systems and edge computing applications.
Key Innovations
Real-Time Updates
Neural networks can update weights in milliseconds with micro-batch processing that maintains prediction accuracy during continuous data streams.
Low-Latency Inference
Achieves sub-millisecond inference times through optimized computational graphs and sparse network topologies.
Adaptive Regularization
Dynamic regularization techniques prevent catastrophic forgetting in continuous learning applications with 89% retention rate across multiple tasks.
Originally Published in the 2024 NeuroNexus Symposium
This research was originally presented at the October 2024 Symposium on Adaptive Learning. View full conference sessions for additional insights on next-generation learning systems.