Provable local learning rule by expert aggregation for a Hawkes network
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916134571212800 |
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| author | Jaffard, Sophie Vaiter, Samuel Muzy, Alexandre Reynaud-Bouret, Patricia |
| author_facet | Jaffard, Sophie Vaiter, Samuel Muzy, Alexandre Reynaud-Bouret, Patricia |
| contents | We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named HAN for Hawkes Aggregation of Neurons, is based on a local synaptic learning rule based on spiking probabilities at each output node. We were able to use local regret bounds to prove mathematically that the network is able to learn on average and even asymptotically under more restrictive assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_08061 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Provable local learning rule by expert aggregation for a Hawkes network Jaffard, Sophie Vaiter, Samuel Muzy, Alexandre Reynaud-Bouret, Patricia Statistics Theory We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named HAN for Hawkes Aggregation of Neurons, is based on a local synaptic learning rule based on spiking probabilities at each output node. We were able to use local regret bounds to prove mathematically that the network is able to learn on average and even asymptotically under more restrictive assumptions. |
| title | Provable local learning rule by expert aggregation for a Hawkes network |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2304.08061 |