TIDE augments standard transformers with per-layer token embedding injection via an ensemble of memory blocks and a depth-conditioned router to mitigate rare-token undertraining and contextual collapse.
Journal of Machine Learning Research , volume =
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CT-AGD accelerates first-order optimization in deep learning by using finite-difference curvature estimates and noise-mitigation heuristics, achieving equivalent accuracy with 33% fewer training epochs and overhead comparable to Adam.
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TIDE: Every Layer Knows the Token Beneath the Context
TIDE augments standard transformers with per-layer token embedding injection via an ensemble of memory blocks and a depth-conditioned router to mitigate rare-token undertraining and contextual collapse.
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Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
CT-AGD accelerates first-order optimization in deep learning by using finite-difference curvature estimates and noise-mitigation heuristics, achieving equivalent accuracy with 33% fewer training epochs and overhead comparable to Adam.