NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training
arXiv:2605.02044v2 Announce Type: replace Abstract: Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports real-time exploration of fully connected neural network training. Users can configure network architecture, activation functions, learning rates, and datasets, then observe activations, weight updates, and loss progression. NeuroViz visualizes weight changes in direct correspondence with activation signals in both forward and backward passes, enabling users to distinguish pre- and post-update s
The increasing complexity of neural networks and the growing demand for AI explainability are driving the need for better visualization and interpretability tools.
Improved interpretability of neural network training will accelerate AI research and development, making sophisticated models more accessible and debuggable for a wider range of users, particularly newcomers.
The ability to interactively visualize neural network training processes in real-time makes the 'black box' of AI more transparent, potentially democratizing access to advanced AI development.
- · AI researchers
- · AI developers
- · Educational institutions training AI professionals
- · AI startups focused on interpretability
- · Companies relying on opaque AI models
- · Traditional AI training methodologies without robust visualization
Wider adoption of advanced neural network architectures due to reduced complexity in understanding their training dynamics.
Faster iteration cycles in AI model development and debugging, leading to more robust and performant AI systems.
Enhanced trust in AI systems as their internal workings become more transparent, potentially accelerating regulatory frameworks that demand explainability.
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Read at arXiv cs.LG