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..
A Simple Neural Attentive Meta-Learner
Authors: Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural Attent Ive Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins. |
| Researcher Affiliation | Collaboration | UC Berkeley, Department of Electrical Engineering and Computer Science Embodied Intelligence EMAIL Accepted as a conference paper at ICLR 2018 Authors contributed equally and are listed in alphabetical order. Part of this work was done at Open AI. |
| Pseudocode | Yes | 1: function DENSEBLOCK(inputs, dilation rate R, number of filters D): 2: xf, xg = Causal Conv(inputs, R, D), Causal Conv(inputs, R, D) 3: activations = tanh(xf) * sigmoid(xg) 4: return concat(inputs, activations) |
| Open Source Code | No | Some video results can be found at https://sites.google.com/view/snail-iclr-2018/. (This link is for video results, not source code.) |
| Open Datasets | Yes | Introduced by Lake et al. (2011), Omniglot consists of black-and-white images of handwritten characters gathered from 50 languages, for a total of 1632 different classes with 20 instances per class. ... Mini-Image Net is a more difficult benchmark; a subset of the well-known Image Net dataset, it consists of 84 84 color images from 100 different classes with 600 instances per class. We used the split released by Ravi & Larochelle (2017) and used by a number of other works |
| Dataset Splits | Yes | Mini-Image Net ... We used the split released by Ravi & Larochelle (2017) and used by a number of other works, with 64 classes for training, 16 for validation, and 20 for testing. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like 'Adam (Kingma & Ba, 2015)' and methods like 'trust region policy optimization with generalized advantage estimation (TRPO with GAE; Schulman et al. (2015; 2016))', but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or other libraries). |
| Experiment Setup | Yes | The hyperparameters are listed in Table 7. For multi-armed bandits, tabular MDPs, and visual navigation, we used the same hyperparamters as Duan et al. (2016) to make our results directly comparable; additional tuning could potentially improve SNAIL s performance. Table 7: The TRPO + GAE hyperparameters we used in our RL experiments. Hyperparameter: Batch Size (timesteps), Discount, GAE λ, Mean KL |