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 [1].
Smoothed Gradients for Stochastic Variational Inference
Authors: Stephan Mandt, David Blei
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on latent Dirichlet allocation with three large corpora. We tested SVI for LDA, using the smoothed stochastic gradients, on three large corpora: 882K scientific abstracts from the Arxiv repository, using a vocabulary of 14K words. 1.7M articles from the New York Times, using a vocabulary of 8K words. 3.6M articles from Wikipedia, using a vocabulary of 7.7K words. |
| Researcher Affiliation | Academia | Stephan Mandt Department of Physics Princeton University EMAIL David Blei Department of Computer Science Department of Statistics Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1: Smoothed stochastic gradients for Latent Dirichlet Allocation |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the methodology described. |
| Open Datasets | Yes | We tested SVI for LDA, using the smoothed stochastic gradients, on three large corpora: 882K scientific abstracts from the Arxiv repository, using a vocabulary of 14K words. 1.7M articles from the New York Times, using a vocabulary of 8K words. 3.6M articles from Wikipedia, using a vocabulary of 7.7K words. |
| Dataset Splits | No | The paper mentions separating a 'test set from the training set' and then splitting the test set for evaluation, but it does not specify a distinct validation set or provide explicit percentages for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We set the minibatch size to B = 300 and furthermore set the number of topics to K = 100, and the hyper-parameters α = η = 0.5. We fixed the learning rate to ρ = 10 3. |