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..
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Authors: John Lawson, George Tucker, Bo Dai, Rajesh Ranganath
NeurIPS 2019 | Venue PDF | LLM Run Details
| 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. |