Papers › Learning to Remember More with Less Memorization
Learning to Remember More with Less Memorization
Hung Le, Truyen Tran, Svetha Venkatesh
Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize that this scheme of writing is suboptimal in memory utilization and introduces redundant computation. To validate our hypothesis, we derive a theoretical bound on the amount of information stored in a RAM-like system and formulate an optimization problem that maximizes the bound. The proposed solution dubbed Uniform Writing is proved to be optimal under the assumption of equal timestep contributions. To relax this assumption, we introduce modifications to the original solution, resulting in a solution termed Cached Uniform Writing. This method aims to balance between maximizing memorization and forgetting via overwriting mechanisms. Through an extensive set of experiments, we empirically demonstrate the advantages of our solutions over other recurrent architectures, claiming the state-of-the-arts in various sequential modeling tasks.
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Code
Syntology Ran 3 of 3 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it.
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3 samples harvested; 3 ran; 1 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | Yelp Binary classification | DNC+CUW | Error | 3.60 | #13 of 20 | Archive leaderboard | report |
| Sentiment Analysis | Yelp Fine-grained classification | DNC+CUW | Error | 34.40 | #12 of 17 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | DNC+CUW | Permuted Accuracy | 96.3% | #20 of 30 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | DNC+CUW | Unpermuted Accuracy | 99.1% | #20 of 30 | Archive leaderboard | report |
| Text Classification | AG News | DNC+CUW | Error | 6.10 | #6 of 24 | Archive leaderboard | report |
| Text Classification | Yahoo! Answers | DNC+CUW | Accuracy | 74.30 | #5 of 10 | 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.
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