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20202026
most citedA comparison of LSTM and GRU networks for learning symbolic sequences

177 citations · 182 across the 5 of their papers we have counts for

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cs.LG2026

ROTE: Benchmarking Neural Memorization on Complexity-Controlled Symbolic Sequences

Xinye Chen, Stefan Güttel, Mohammad Mozaffari

We introduce ROTE (RollOut Testing of Exact memorization), a benchmarking protocol for evaluating symbolic memorization of neural architectures. We study memorization and the exten…

cs.LG2026

Precision autotuning for linear solvers via contextual bandit-based RL

Erin Carson, Xinye Chen

We propose a reinforcement learning (RL) framework for \xy{responsive} precision tuning for linear solvers, which can be extended to general algorithms. The framework is formulated…