Papers › Learning Associative Inference Using Fast Weight Memory
Learning Associative Inference Using Fast Weight Memory
Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber
Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such associative inference. To this end, we augment the LSTM model with an associative memory, dubbed Fast Weight Memory (FWM). Through differentiable operations at every step of a given input sequence, the LSTM updates and maintains compositional associations stored in the rapidly changing FWM weights. Our model is trained end-to-end by gradient descent and yields excellent performance on compositional language reasoning problems, meta-reinforcement-learning for POMDPs, and small-scale word-level language modelling.
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Code
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Code Syntology ran Syntology
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | Penn Treebank (Word Level) | AWD-FWM Schlag et al. (2020) | Params | 24M | #21 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | AWD-FWM Schlag et al. (2020) | Test perplexity | 54.48 | #21 of 43 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Word Level) | AWD-FWM Schlag et al. (2020) | Validation perplexity | 56.76 | #21 of 43 | Archive leaderboard | report |
| Language Modelling | WikiText-2 | AWD-FWM Schlag et al. (2020) | Number of params | 37M | #27 of 38 | Archive leaderboard | report |
| Language Modelling | WikiText-2 | AWD-FWM Schlag et al. (2020) | Test perplexity | 61.65 | #27 of 38 | Archive leaderboard | report |
| Language Modelling | WikiText-2 | AWD-FWM Schlag et al. (2020) | Validation perplexity | 54.48 | #27 of 38 | Archive leaderboard | report |
| Question Answering | catbAbI LM-mode | Fast Weight Memory | Accuracy (mean) | 93.04% | #1 of 4 | Archive leaderboard | report |
| Question Answering | catbAbI LM-mode | AWD-Transformer XL | Accuracy (mean) | 90.23% | #2 of 4 | Archive leaderboard | report |
| Question Answering | catbAbI LM-mode | AWD-LSTM | Accuracy (mean) | 80.15% | #3 of 4 | Archive leaderboard | report |
| Question Answering | catbAbI LM-mode | Metalearned Neural Memory (plastic) | Accuracy (mean) | 69.3% | #4 of 4 | Archive leaderboard | report |
| Question Answering | catbAbI QA-mode | Fast Weight Memory | 1:1 Accuracy | 96.75% | #1 of 4 | Archive leaderboard | report |
| Question Answering | catbAbI QA-mode | Metalearned Neural Memory (plastic) | 1:1 Accuracy | 88.97% | #2 of 4 | Archive leaderboard | report |
| Question Answering | catbAbI QA-mode | AWD-Transformer XL | 1:1 Accuracy | 87.66% | #3 of 4 | Archive leaderboard | report |
| Question Answering | catbAbI QA-mode | AWD-LSTM | 1:1 Accuracy | 80.88% | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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