Extreme Low-State Quantized Transformer for Electricity-Load Forecasting
arXiv:2610.28784v1 Announce Type: new Abstract: Aggressive low-state weight quantization can substantially reduce neural-network representational flexibility, making predictive performance difficult to preserve when only a few discrete weight states are available. We investigate this regime for Transformer-based electricity-load forecasting using 3-, 5-, 7-, and 16-state weight quantization across feed-forward-only and attention-plus-feed-forward quantization. Two tasks predict either the next hour or the next 24 hours from the preceding 48 hours across two co-primary electricity-consumption d
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