We lifted 13 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| optimass/Maximally_Interfered_Retrieval | canonical | 7 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| ResNet18 | Ran | optimass/Maximally_Interfered_Retrieval/model.py code served (permissive licence) · get_code("c6b1a6798ffeb442") |
| conv3x3 | Ran | optimass/Maximally_Interfered_Retrieval/model.py code served (permissive licence) · get_code("ec20f22a185cd708") |
| get_grad_vector | Ran | optimass/Maximally_Interfered_Retrieval/utils.py code served (permissive licence) · get_code("947e501e9d13e0ce") |
| log_normal_diag | Ran | optimass/Maximally_Interfered_Retrieval/VAE/distributions.py code served (permissive licence) · get_code("7d71e086676bc142") |
| log_normal_normalized | Ran | optimass/Maximally_Interfered_Retrieval/VAE/distributions.py code served (permissive licence) · get_code("a811f7f779566e3e") |
| log_normal_standard | Ran | optimass/Maximally_Interfered_Retrieval/VAE/distributions.py code served (permissive licence) · get_code("a39500bc34bfcdd6") |
| onehot | Ran | optimass/Maximally_Interfered_Retrieval/utils.py code served (permissive licence) · get_code("651082fb231f9a1d") |
| get_cifar_buffer | Not yet run | optimass/Maximally_Interfered_Retrieval/buffer.py code served (permissive licence) · get_code("7f8a097320e69838") |
| get_future_step_parameters | Not yet run | optimass/Maximally_Interfered_Retrieval/utils.py code served (permissive licence) · get_code("a1dbf374743f5c7b") |
| get_permuted_mnist | Not yet run | optimass/Maximally_Interfered_Retrieval/data.py code served (permissive licence) · get_code("93eb2ca19dee45bc") |
| get_split_cifar10 | Not yet run | optimass/Maximally_Interfered_Retrieval/data.py code served (permissive licence) · get_code("de5833eda07e76b8") |
| get_split_mnist | Not yet run | optimass/Maximally_Interfered_Retrieval/data.py code served (permissive licence) · get_code("a78b3966b0df22e0") |
| retrieve_hybrid | Not yet run | optimass/Maximally_Interfered_Retrieval/mir.py code served (permissive licence) · get_code("952eca59dbb44579") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for modern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at https://github.com/optimass/Maximally_Interfered_Retrieval.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1908.04742")
get_code_for_paper("1908.04742")
have("1908.04742")
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