Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Authors: John Lawson, George Tucker, Bo Dai, Rajesh Ranganath
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,050 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,862 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe and evaluate three instantiations of such models based on truncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling. These models outperform or perform comparably to the recently proposed Learned Accept/Reject Sampling algorithm [5] and provide new insights on ranking Noise Contrastive Estimation [34, 46] and Contrastive Predictive Coding [57]. |
| Researcher Affiliation | Collaboration | Dieterich Lawson Stanford University EMAIL George Tucker , Bo Dai Google Research, Brain Team EMAIL Rajesh Ranganath New York University EMAIL |
| Pseudocode | Yes | Algorithm 1 TRS(π, U, T) generative process Algorithm 2 SNIS(π, U) generative process Algorithm 3 HIS(π, U, ϵ, α0:T ) generative process |
| Open Source Code | Yes | Code and image samples: sites.google.com/view/energy-inspired-models. |
| Open Datasets | Yes | We evaluated the proposed models on a set of synthetic datasets, binarized MNIST [43] and Fashion MNIST [69], and continuous MINST, Fashion MNIST, and Celeb A [45]. |
| Dataset Splits | No | The paper mentions evaluating on a 'test set' but does not explicitly provide details about training/validation/test splits or a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions 'tuning hyperparameters' and refers to 'Appendix D for details on the datasets, network architectures, and other implementation details' but does not provide concrete hyperparameter values or detailed training configurations within the main text. |