Continual Learning and Neural Network Advancements

The field of neural networks and continual learning is moving towards innovative solutions to address the stability gap and improve overall performance. Researchers are drawing inspiration from biological systems, such as noradrenergic bursts, to develop adaptive mechanisms that balance plasticity and stability. New neuron architectures, like the APTx Neuron, are being proposed to integrate activation and computation into a single trainable expression. Additionally, there is a growing focus on understanding the theoretical foundations of neural networks, including the use of calculus of variations to analyze the Transformer. Noteworthy papers in this area include:

  • Noradrenergic-inspired gain modulation, which effectively attenuates the stability gap in joint training.
  • The APTx Neuron, a novel neural computation unit that achieves superior expressiveness and computational efficiency.
  • Constrained rational activations, which balance expressivity and robustness in reinforcement learning.
  • Theoretical analyses, such as the calculus of variations of the Transformer, which provide new insights into neural network dynamics.

Sources

Noradrenergic-inspired gain modulation attenuates the stability gap in joint training

Understanding Two-Layer Neural Networks with Smooth Activation Functions

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning

The calculus of variations of the Transformer on the hyperspherical tangent bundle

Reactivation: Empirical NTK Dynamics Under Task Shifts

Regression-aware Continual Learning for Android Malware Detection

Neural Tangent Kernels and Fisher Information Matrices for Simple ReLU Networks with Random Hidden Weights

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