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..
The Statistical Recurrent Unit
Authors: Junier B. Oliva, Barnabás Póczos, Jeff Schneider
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures hyperparameters for both synthetic and real-world tasks. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA. Correspondence to: Junier B. Oliva <EMAIL>. |
| Pseudocode | No | The paper provides update equations and a graphical representation (Figure 1) but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | See https://github.com/junieroliva/ recurrent for code. |
| Open Datasets | Yes | Next we explore the ability of recurrent units to use long-term dependencies in ones data with a synthetic task using a real dataset. It has been observed that LSTMs perform poorly in classifying a long pixel-by-pixel sequence of MNIST digits (Le et al., 2015). |
| Dataset Splits | Yes | We generate a total of 176 points per sequence for 3200 training sequences, 400 validation sequences, and 400 testing sequences. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | All experiments were performed in Tensorflow (Abadi et al., 2016) and used the standard implementations of GRUCell and Basic LSTMCell for GRUs and LSTMs respectively. |
| Experiment Setup | Yes | In all experiments we used SGD for optimization using gradient clipping (Pascanu et al., 2013) with a norm of 1 on all algorithms. Unless otherwise specified 100 trials were performed to search over the following hyper-parameters on a validation set: one, initial learning rate the initial learning rate used for SGD, in range of [exp( 10), 1]; two, lr decay the multiplier to multiply the learning rate by every 1k iterations, in range of [0.8, 0.999]; three, dropout keep rate, percent of output units that are kept during dropout, in range (0, 1]; four, num units number of units for recurrent unit, in {1, . . . , 256}. |