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Found 4 papers, 1 papers with code
SketchOGD: Memory-Efficient Continual Learning
This paper proposes a memory-efficient solution to catastrophic forgetting, improving upon an established algorithm known as orthogonal gradient descent (OGD).
Data-Driven Control with Inherent Lyapunov Stability
Recent advances in learning-based control leverage deep function approximators, such as neural networks, to model the evolution of controlled dynamical systems over time.
One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares
While deep neural networks are capable of achieving state-of-the-art performance in various domains, their training typically requires iterating for many passes over the dataset.
Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution
This paper constructs shallow sigmoid-type neural networks that achieve 100% accuracy in classification for datasets following a linear separability condition.
