Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
State-Regularized Recurrent Neural Networks
Authors: Cheng Wang, Mathias Niepert
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our hypotheses through experiments both on synthetic and real-world datasets. We explore the improvement of the extrapolation capabilities of SR-RNNs and closely investigate their memorization behavior. |
| Researcher Affiliation | Industry | Cheng Wang 1 Mathias Niepert 1 1NEC Laboratories Europe, Heidelberg, Germany. Correspondence to: Cheng Wang <EMAIL>. |
| Pseudocode | Yes | Due to space constraints, the pseudo-code of the extraction algorithm is listed in the Supplementary Material. |
| Open Source Code | Yes | An implementation of SR-RNNs is available at https:// github.com/deepsemantic/sr-rnns. |
| Open Datasets | Yes | We evaluate the DFA extraction algorithm for SR-RNNs on RNNs trained on the Tomita grammars (Tomita, 1982)... |
| Dataset Splits | Yes | We created two datasets for BP. A large one with 22,286 training sequences (positive: 13,025; negative: 9,261) and 6,704 validation sequences (positive: 3,582; negative: 3,122). The small dataset consists of 1,008 training sequences (positive: 601; negative: 407), and 268 validation sequences (positive: 142; negative: 126). |
| Hardware Specification | Yes | All experiments were conducted on a single Titan Xp with 12G memory. |
| Software Dependencies | No | The paper mentions 'Theano' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | Unless otherwise indicated we always (a) use single-layer RNNs, (b) learn an embedding for input tokens before feeding it to the RNNs, (c) apply ADADELTA (Zeiler, 2012) for regular language and RMSPROP (Tieleman & Hinton, 2012) with a learning rate of 0.01 and momentum of 0.9 for the rest; (d) do not use dropout (Srivastava et al., 2014) or batch normalization (Cooijmans et al., 2017) of any kind; and (e) use state-regularized RNNs based on equations 3&5 with a temperature of τ = 1 (standard softmax). |