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..
Deep State Space Models for Unconditional Word Generation
Authors: Florian Schmidt, Thomas Hofmann
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Evaluation, 5 Experiments, Table 1 shows the result for the standard split. |
| Researcher Affiliation | Academia | Florian Schmidt Department of Computer Science ETH Zürich EMAIL Thomas Hofmann Department of Computer Science ETH Zürich EMAIL |
| Pseudocode | Yes | Algorithm 1 Detailed forward pass with importance weighting |
| Open Source Code | No | No concrete statement about open-source code availability or repository links found in the paper. |
| Open Datasets | Yes | For our experiments, we use the Books Corpus [KZS+15, ZKZ+15], a freely available collection of novels comprising of almost 1B tokens out of which 1.3M are unique. |
| Dataset Splits | Yes | Besides the standard 10% test-train split at the word level, we also perform a second, alternative split at the vocabulary level. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific ancillary software details, such as library or solver names with version numbers, are mentioned in the paper. |
| Experiment Setup | Yes | Hidden state size and embedding size are identical to our model s. We investigate the flow in Equation (10), denoted as TRIL, its diagonal version DIAG and a simple identity ID. For the weighted version we use K {2, 5, 10} samples. Furthermore, we investigate deviating from the factorization (3) by using a bidirectional RNN conditioning on all w1...T in every timestep. Finally, for the best performing configuration, we also investigate state-sizes d = {16, 32}. |