BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.
Switch Transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23 (120):1–39
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Dynamic Pattern Recalibration (DPR) adds a perceive-route-modulate pipeline that generates time-aware modulation vectors to recalibrate hidden states in forecasting models, improving performance across architectures with low overhead.
citing papers explorer
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BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems
BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.
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Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting
Dynamic Pattern Recalibration (DPR) adds a perceive-route-modulate pipeline that generates time-aware modulation vectors to recalibrate hidden states in forecasting models, improving performance across architectures with low overhead.