Papers › HyperNetworks
HyperNetworks
David Ha, Andrew Dai, Quoc V. Le
This work explores hypernetworks: an approach of using a one network, also known as a hypernetwork, to generate the weights for another network. Hypernetworks provide an abstraction that is similar to what is found in nature: the relationship between a genotype - the hypernetwork - and a phenotype - the main network. Though they are also reminiscent of HyperNEAT in evolution, our hypernetworks are trained end-to-end with backpropagation and thus are usually faster. The focus of this work is to make hypernetworks useful for deep convolutional networks and long recurrent networks, where hypernetworks can be viewed as relaxed form of weight-sharing across layers. Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve near state-of-the-art results on a variety of sequence modelling tasks including character-level language modelling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks. Our results also show that hypernetworks applied to convolutional networks still achieve respectable results for image recognition tasks compared to state-of-the-art baseline models while requiring fewer learnable parameters.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | Penn Treebank (Character Level) | 2-layer Norm HyperLSTM | Bit per Character (BPC) | 1.219 | #14 of 20 | Archive leaderboard | report |
| Language Modelling | Penn Treebank (Character Level) | 2-layer Norm HyperLSTM | Number of params | 14.4M | #14 of 20 | Archive leaderboard | report |
| Language Modelling | enwik8 | Hypernetworks | Bit per Character (BPC) | 1.34 | #40 of 42 | Archive leaderboard | report |
| Language Modelling | enwik8 | Hypernetworks | Number of params | 27M | #40 of 42 | 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
Introduced by this paper: HyperNetwork
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