Neural Algorithmic Reasoning Without Intermediate Supervision
Authors: Gleb Rodionov, Liudmila Prokhorenkova
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show that our approach is competitive with the current state-of-the-art results relying on intermediate supervision. Moreover, for some of the problems, we achieve the best known performance: for instance, we get the F1 score 98.7% for the sorting, which significantly improves over the previously known winner with 95.2%. |
| Researcher Affiliation | Industry | Gleb Rodionov Yandex Research Moscow, Russia rodionovgleb@yandex-team.ru Liudmila Prokhorenkova Yandex Research Amsterdam, The Netherlands ostroumova-la@yandex-team.ru |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper; there are no specific repository links, explicit code release statements, or code in supplementary materials. |
| Open Datasets | Yes | Our work follows the setup of the recently proposed CLRS Algorithmic Reasoning Benchmark (CLRS) (Veliˇckovi c et al., 2022). |
| Dataset Splits | Yes | Validation size 16 |
| Hardware Specification | Yes | Our models are trained on a single A100 GPU, requiring less than 1 hour to train. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Triplet-GMPNN architecture' but does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | Optimiser Adam Learning rate 0.001 Train steps count 10000 Evaluate each (steps) 50 Early-stopping patience (steps) 500 Batch size 32 Processor Triplet-GMPNN Hidden state size 128 Number of message passing steps per processor step 1 |