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].
Delta-AI: Local objectives for amortized inference in sparse graphical models
Authors: Jean-Pierre RenΓ© Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS: SYNTHETIC DATA; 5 EXPERIMENTS: VARIATIONAL EM ON REAL DATA |
| Researcher Affiliation | Academia | Mila Qu ebec AI Institute, Universit e de Montr eal Montreal, Quebec, Canada |
| Pseudocode | Yes | Algorithm 1 Ξ-amortized inference (basic form) |
| Open Source Code | Yes | Code: https://github.com/GFNOrg/Delta-AI. |
| Open Datasets | Yes | latent variable modeling for MNIST images (Deng, 2012). We use the AMASS dataset (Mahmood et al., 2019) |
| Dataset Splits | No | The paper mentions held-out test data and 20% is held-out as a test set for the test splits, but it does not specify explicit percentages or sample counts for a validation dataset split, nor does it refer to a standard split with validation. |
| Hardware Specification | No | The research was enabled in part by computational resources provided by the Digital Research Alliance of Canada (https://alliancecan.ca), Mila (https://mila.quebec), and NVIDIA. This mentions NVIDIA and computational resources but lacks specific models (e.g., GPU series or CPU types) for the hardware used in experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | Each model is trained for total 200k iterations. Baseline GFNs: Batchsize is set to 1k. ... Learning rate of the parameters of the amortized sampler is set to 10 3 and that of the partition function estimator is set to 10 1. Those learning rates are step-wisely decayed by 0.1 at 40k, 80k, 120k, 160k, and 180k-th iteration. ... We use π= 0.1. ... For the training policy, we simply use the tempered off-policy with temperature set to 2. |