<ul><li>Neural Scaling Laws demonstrate that as model size, data, and computational resources scale, neural networks exhibit improved performance.</li><li>In multivariate forecasting, Neural Scaling Laws suggest that increasing model parameters, data, and compute power leads to higher accuracy and better generalization.</li><li>Several neural architectures, such as RNNs, LSTM networks, CNNs, and Transformer-based models, have proven effective for multivariate forecasting.</li><li>Optimization techniques like model pruning, knowledge distillation, quantization, and emerging trends in self-supervised learning, federated learning, and quantum computing help balance performance and computational efficiency.</li></ul>

AI Model Performance Optimization with Neural Scaling

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