Conjugate Energy-Based Models
Authors: Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, Jan-Willem Van De Meent
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that conjugate EBMs achieve competitive results in terms of image modelling, predictive power of latent space, and outof-domain detection on a variety of datasets. |
| Researcher Affiliation | Collaboration | 1Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA 2Oracle Labs, MA, USA 3Computer Science department, Boston College, MA, USA. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | Figure 2 shows samples from CEBMs trained on MNIST, Fashion-MNIST, SVHN, and CIFAR-10. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning for training, validation, and testing. It mentions training classifiers with varying numbers of examples per class but not a general validation split for the main model training. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments were found in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We train our models using 60 SGLD steps, 90k gradient steps, batch size 128, Adam optimizer with learning rate 1e-4. |