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].
Self Normalizing Flows
Authors: Thomas A Keller, Jorn W.T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forrรฉ, Max Welling
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that such models are remarkably stable and optimize to similar data likelihood values as their exact gradient counterparts, while training more quickly and surpassing the performance of functionally constrained counterparts. |
| Researcher Affiliation | Collaboration | 1Uv A-Bosch Delta Lab 2University of Amsterdam, Netherlands. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide extended explanations for these discrepancies, as well as a link to our code repository, in the appendix (See Section A.3). |
| Open Datasets | Yes | In this framework we train models on MNIST, CIFAR-10, and the downsized Imagenet 32x32 dataset. |
| Dataset Splits | Yes | Figure 5. Negative log-likelihood on the MNIST validation set for a 2-layer fully connected ๏ฌow trained with exact vs. self normalizing (SNF) gradients. |
| Hardware Specification | No | Table 3. Runtime comparison for the models presented in Tables 1 and 2. Hardware and implementation details are in Section A.3. (The main text defers hardware details to the appendix and does not provide specific models or specifications). |
| Software Dependencies | No | Table 3. Runtime comparison for the models presented in Tables 1 and 2. Hardware and implementation details are in Section A.3. (The main text defers software dependency details to the appendix and does not provide specific versions). |
| Experiment Setup | No | All training details can be found in the appendix (see Section A.3). (The main text defers explicit experimental setup details like hyperparameters and training configurations to the appendix). |