| --- |
| license: apache-2.0 |
| tags: |
| - fast-weights |
| - associative-memory |
| - meta-learning |
| - sequence-modeling |
| - gradio |
| --- |
| |
| # Fast-Weight Time Machine |
|
|
| Fast-Weight Time Machine tests temporary variable binding with an explicit, |
| sequence-local weight matrix. A controller receives key/value writes, produces a |
| write strength, and accumulates outer products in a fast memory. A later query key |
| reads the memory in one matrix-vector operation. The learned parameters stay fixed |
| between examples; only the fast matrix changes inside each sequence. |
|
|
| The historical anchor is Schmidhuber's 1992 |
| [Learning to Control Fast-Weight Memories](https://doi.org/10.1162/NECO.1992.4.1.131), |
| which described feedforward controllers producing context-dependent weight changes |
| and included adaptive temporary-variable binding. This project is a modern |
| outer-product interpretation tested against a similarly sized GRU. It does not |
| claim to reproduce the original implementation or experiments exactly. |
|
|
| ## Verified results |
|
|
| The models trained on 16,000 sequences containing four writes and 12 distractors. |
| Each held-out condition contained 4,000 new sequences. |
|
|
| | Condition | Fast weights, 2,508 params | GRU, 3,050 params | |
| | --- | ---: | ---: | |
| | 4 bindings, 12 distractors | 100.00% | 37.40% | |
| | 8 bindings, 12 distractors | 100.00% | 27.03% | |
| | 4 bindings, 64 distractors | 100.00% | 31.15% | |
| | 8 bindings, 64 distractors | 99.98% | 23.83% | |
|
|
| This task is intentionally aligned with the fast-weight architecture: an explicit |
| outer-product matrix can bind a key and value, while the GRU must compress all |
| bindings into one recurrent vector. The comparison demonstrates the inductive bias; |
| it is not a general claim that fast weights outperform GRUs on arbitrary sequences. |
|
|
| ## Reproduce |
|
|
| ```powershell |
| uv run python projects/fast-weight-time-machine/train.py |
| ``` |
|
|
