177 citations · 182 across the 5 of their papers we have counts for
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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…
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…
Fast and explainable clustering based on sorting
Xinye Chen, Stefan Güttel
We introduce a fast and explainable clustering method called CLASSIX. It consists of two phases, namely a greedy aggregation phase of the sorted data into groups of nearby data poi…
An efficient aggregation method for the symbolic representation of temporal data
Xinye Chen, Stefan Güttel
Symbolic representations are a useful tool for the dimension reduction of temporal data, allowing for the efficient storage of and information retrieval from time series. They can…
A comparison of LSTM and GRU networks for learning symbolic sequences
Roberto Cahuantzi, Xinye Chen, Stefan Güttel
We explore the architecture of recurrent neural networks (RNNs) by studying the complexity of string sequences it is able to memorize. Symbolic sequences of different complexity ar…