Metalearned Neural Memory
Authors: Tsendsuren Munkhdalai, Alessandro Sordoni, TONG WANG, Adam Trischler
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our metalearned neural memory (MNM) on a diverse set of learning problems, which includes several algorithmic tasks, synthetic question answering on the b Ab I dataset, and maze exploration via reinforcement learning. Our model achieves strong performance on all of these benchmarks. |
| Researcher Affiliation | Industry | Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler Microsoft Research Montréal, Québec, Canada tsendsuren.munkhdalai@microsoft.com |
| Pseudocode | No | The paper contains Figure 1 illustrating the model, but it does not include any blocks or sections explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not provide any statement about making its source code available, nor does it include links to a code repository. |
| Open Datasets | Yes | We demonstrate the effectiveness of our metalearned neural memory (MNM) on a diverse set of learning problems, which includes several algorithmic tasks, synthetic question answering on the b Ab I dataset, and maze exploration via reinforcement learning. ... b Ab I is a synthetic question-answering benchmark that has been widely adopted to evaluate long-term memory and reasoning [45]. ... We train MNM agents on a maze exploration task from the literature on meta-reinforcement learning [4, 44]. Specifically, we adopted the grid world setup from [23]. |
| Dataset Splits | Yes | For the bAbI tasks, ... We used the standard 10K training examples per task and 1K test examples. |
| Hardware Specification | No | The paper does not specify any particular CPU or GPU models, memory amounts, or other specific hardware configurations used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of "Adam optimizer" and the "Advantage Actor-Critic (A2C) algorithm" but does not provide specific version numbers for any software libraries, frameworks (e.g., TensorFlow, PyTorch), or programming languages. |
| Experiment Setup | Yes | For the dictionary task, we train the MNM and LSTM+SALU models for 60K iterations using the Adam optimizer with a learning rate of 10−4. We use a batch size of 20. ... The MNM models have a 3-layer neural memory with 100 units in each layer. ... For the bAbI tasks, we train the MNM model for 100K iterations using Adam optimizer with a learning rate of 10−4 and a batch size of 20. ... All MNM models are trained with 4 parallel read/write heads. ... We used A2C with a discount factor of 0.99 and a learning rate of 7 · 10−4. The controller s hidden size is 200 and the memory s is 100 with 3 layers. The number of heads is 1. |